System

Abstract

A system is provided.SOLUTION: An education platform in a virtual reality space using generation-based artificial intelligence, comprising means for authenticating input information of a user and acquiring a learning history and setting information of the user, means for acquiring detailed information of a learning theme and a course on the basis of selection of the user and preparing a learning environment in the virtual reality space, means for providing an appropriate answer and explanation by the generation-based artificial intelligence to a question of the user, means for providing a necessary data set and tool while the user executes a practice project in the virtual reality space and monitors a progress status, and means for performing comprehensive evaluation on the basis of a learning progress and a test result of the user and generating and presenting an evaluation report.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] When working adults learn new technologies or obtain qualifications, time and environmental constraints are major barriers, making efficient and effective reskilling difficult. Traditional, one-sided, non-interactive learning methods also make it difficult to maintain the motivation of engineers. Furthermore, practical skills are often not acquired sufficiently, making it difficult for companies and organizations promoting digital transformation to develop workforces that are immediately ready for the job. [Means for solving the problem]

[0005] The present invention provides an educational platform that combines generative artificial intelligence and virtual reality. This platform includes the following means: a means for authenticating user input information and acquiring the user's learning history and setting information, a means for acquiring detailed information about learning themes and courses based on the user's selections and preparing a learning environment in the virtual reality space, a means for the generative artificial intelligence to provide appropriate answers and explanations to the user's questions, a means for the user to carry out practical projects in the virtual reality space and provide necessary data sets and tools while monitoring the progress, and a means for performing a comprehensive evaluation based on the user's learning progress and test results, and generating and presenting an evaluation report, thereby realizing an efficient and effective learning environment.

[0006] "Generative AI" refers to algorithms that generate answers and explanations in natural language tailored to the context in response to user input or questions.

[0007] "Virtual reality space" refers to a computer-generated, interactive, three-dimensional environment that provides a virtual learning environment that users can experience sensory-wise.

[0008] "Educational platform" refers to an online learning system that users use to acquire skills and improve their knowledge.

[0009] "User Input Information" means the identification and authentication information provided by a User when accessing the Learning Platform.

[0010] "Learning history" refers to a record of the learning content, progress, evaluation results, etc. that a user has undertaken so far.

[0011] "Setting information" refers to preferences and interface customization information selected by the user when studying.

[0012] A "learning topic" refers to a specific skill or area of ​​knowledge that a user is interested in learning.

[0013] "Course details" refers to information such as the specific curriculum set for each learning theme, necessary learning materials, and goals.

[0014] A "hands-on project" refers to an activity that includes assignments or tasks that users carry out in a virtual reality space to apply what they have learned in practice.

[0015] "Progress" refers to the progress or achievement of a user when working on a study or project.

[0016] A "dataset" refers to a set of data needed for a study or project.

[0017] "Tools" refers to software and applications used for learning and projects.

[0018] "Evaluation report" refers to a document that summarizes a comprehensive evaluation based on a user's learning progress and test results. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] This invention provides an educational platform that integrates generative artificial intelligence (AI) and virtual reality (VR) spaces. Users can learn in a virtual reality space while engaging in interactive dialogues using AI, enabling them to efficiently acquire practical skills.

[0041] Initial Setup

[0042] 1. User login

[0043] The user enters their account information (user name and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session.

[0044] 2. Initializing the virtual reality space

[0045] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space.

[0046] Select learning content

[0047] 1. Selecting a study topic

[0048] The user selects the topic they wish to study from a list of learning themes and courses provided on the platform. For example, the user selects a course such as "Python programming" or "data science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative AI. The server then obtains the necessary learning materials from the generative AI, formats the data, and sends it to the device.

[0049] 2. Preparing the virtual environment

[0050] The device prepares for learning based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a rich virtual learning environment and displays it to the user.

[0051] Start learning

[0052] 1. Start learning

[0053] The user clicks a button in the virtual reality interface to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI. For example, when the user uses the "print function," the generative AI explains how to use it.

[0054] 2. Interactive dialogue

[0055] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0056] Practical project implementation

[0057] 1. Project-Based Learning

[0058] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0059] 2. Team collaboration and sharing

[0060] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0061] Communication and Feedback

[0062] 1. Dialogue and feedback

[0063] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0064] Termination and Evaluation

[0065] 1. Completion of study

[0066] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report that includes, for example, the project's evaluation score and areas for improvement.

[0067] 2. Presentation of evaluation results

[0068] The device visually presents the assessment results to the user and displays next learning steps and recommended courses. For example, the device displays an assessment report and a list of next courses to be taken on the screen.

[0069] As described above, the educational platform of the present invention combines generative artificial intelligence and virtual reality space to provide an efficient and effective learning environment and assist users in acquiring skills.

[0070] The processing flow will be explained below.

[0071] Step 1: User Login

[0072] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[0073] Step 2: Initializing the Virtual Reality Space

[0074] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space, creating an environment that is easy for the user to learn.

[0075] Step 3: Choose a study topic

[0076] Users select the topic they want to study from a list of learning themes and courses provided on the platform. For example, a user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[0077] Step 4: Preparing the virtual environment

[0078] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[0079] Step 5: Start learning

[0080] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[0081] Step 6: Interactive Dialogue

[0082] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0083] Step 7: Project-Based Learning

[0084] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0085] Step 8: Team collaboration and sharing

[0086] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0087] Step 9: Dialogue and feedback

[0088] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0089] Step 10: Ending the training

[0090] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[0091] Step 11: Present the evaluation results

[0092] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[0093] Through the above steps, the educational platform of the present invention provides users with an efficient and effective learning environment.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] In conventional online education systems, learners often receive knowledge one-way, limiting the ability to acquire practical skills. Furthermore, there is no mechanism for responding to users' questions in real time, which can impede learning progress. Furthermore, limited opportunities for cooperation and communication between learners make it difficult to progress with collaborative learning tasks or projects.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes: means for authenticating user input information and acquiring the user's learning history and setting information; means for acquiring detailed information about learning themes and courses based on the user's selections and preparing a learning environment in a virtual reality space; means for a generative artificial intelligence (AI) to provide appropriate answers and explanations to the user's questions; means for monitoring the user's progress in real time and providing feedback from the AI; means for the user to carry out a practical project in the virtual reality space and provide necessary data sets and tools while monitoring the progress; means for the AI ​​to provide an analysis script template according to the project progress; and means for conducting a comprehensive evaluation based on the user's learning progress and test results, generating and presenting an evaluation report. This enables real-time feedback and question resolution, improving learners' understanding and enabling them to effectively acquire practical skills. It also promotes cooperation among learners, facilitating smooth collaborative work.

[0099] "User" refers to an individual who uses the system to study or carry out a project.

[0100] "Input information" refers to data including authentication information and setting information that a user provides to the system.

[0101] "Learning history" refers to historical data such as the courses a user has taken on the system and their progress.

[0102] "Setting information" refers to setting data such as the theme, layout, and language selected by the user for learning in the virtual reality space.

[0103] "Virtual reality space" refers to a three-dimensional virtual environment generated by a computer.

[0104] "Generative artificial intelligence" refers to an AI model that generates appropriate answers and explanations in response to user questions and requests.

[0105] "Detailed information" refers to information about the selected study topic or course, such as the curriculum, required materials, and objectives.

[0106] A "project" refers to a practical assignment or set of tasks that a user completes in a virtual reality space.

[0107] "Progress" refers to the user's achievements and current status as they progress through their studies or projects.

[0108] A "dataset" refers to a collection of data required to carry out a project or task.

[0109] "Tools" refers to software or features within a virtual environment that users use to advance their learning or projects.

[0110] "Script templates" refer to template code or programs provided by generative artificial intelligence as the project progresses.

[0111] "Comprehensive evaluation" refers to an overall evaluation based on the user's learning progress and test results.

[0112] "Evaluation report" refers to a report summarizing the results of the comprehensive evaluation.

[0113] "Voice input" refers to voice instructions or questions given by the user to the system through a microphone.

[0114] "Chat function" refers to a function that allows a user to enter text and interact with the system and other users.

[0115] The present invention provides a system that provides an educational platform that integrates generative artificial intelligence and virtual reality space, allowing users to learn in a virtual reality space while engaging in interactive dialogue using generative artificial intelligence, thereby efficiently acquiring practical skills.

[0116] Initial Setup

[0117] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from a database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. This process often uses a common authentication system or database management system. For example, MySQL or PostgreSQL can be used for database management.

[0118] The device then initializes the virtual reality space based on the user's settings (e.g., theme, layout, language, etc.). Rather than choosing a fixed layout or theme, virtual reality development platforms such as Unity and Unreal Engine are used to provide dynamically customizable content.

[0119] Select learning content

[0120] Users select the topic they want to learn from a list of learning themes and courses offered on the platform. For example, a user selects a course such as "Python programming" or "data science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative AI model. The server then receives the necessary learning materials from the generative AI model, formats the data, and sends it to the device. This allows users to easily access the materials they need.

[0121] Start learning

[0122] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback with instructions from the generative AI model. For example, when the user uses the "print function," the generative AI model explains how to use it. This allows the user to receive real-time feedback and instantly understand the learning content.

[0123] If a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, a user might ask, "I don't know how to use a loop in Python." The server analyzes the user's question through a generative AI model and provides appropriate answers and explanations in real time, allowing users to quickly resolve problems they encounter during their learning.

[0124] Practical project implementation

[0125] Users begin a practical project in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. Once data preprocessing is complete, the generative AI model provides a script template for analysis. In this way, users can learn independently while receiving support as they progress through the project.

[0126] Users can also share parts of projects with other learners and work together on tasks. For example, a user can collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users. Specifically, it is possible to use chat rooms and shared document functions. This allows users to collaborate in real time and effectively advance their learning.

[0127] Communication and Feedback

[0128] Users can interact with other learners and AI assistants to resolve their questions. For example, if a user requests feedback from other learners about their progress, the server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction, enhancing the learning experience. Specifically, comments from other learners and feedback from AI could be displayed on the screen.

[0129] Termination and Evaluation

[0130] When the user has completed their learning, they click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report. This may include a report that includes the project's evaluation scores and areas for improvement. This report will serve as a reflection on the user's learning and can be used to plan their next learning.

[0131] The device will visually present the assessment results to the user and display next learning steps or recommended courses. Specifically, the device could display an assessment report and a list of next courses on the screen, allowing users to review their learning outcomes and plan their next steps.

[0132] Examples of concrete examples and prompts

[0133] For example, if a user logs into a virtual reality course on "Python Programming," they could use the following prompt:

[0134] "How do I use a for loop in Python?"

[0135] "What's the next step in your data science project?"

[0136] Please give me a concrete example of how to use the print function.

[0137] In this way, the educational platform of the present invention combines generative artificial intelligence and virtual reality space to provide an efficient and effective learning environment and assist users in acquiring skills.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Step 1:

[0140] The user accesses the platform's login screen. The user enters their username and password and clicks the login button. Input: Username and password, Output: Login request. The server receives the entered information, retrieves authentication information from the database, and performs verification. Input: Login request, Output: Authentication result. If authentication is successful, the server retrieves the user's learning history and setting information and saves it in the session. Output: Learning history and setting information.

[0141] Step 2:

[0142] The device initializes the virtual reality space based on the user's setting information received from the server. Input: Learning history and setting information, Output: Initialization settings of the virtual reality space. The device customizes the virtual space based on the user's setting information (e.g., theme, layout, language used, etc.) and arranges interfaces such as menus, dashboards, and avatars. Specifically, the virtual reality environment is constructed using Unity or Unreal Engine. Output: Customized virtual reality space.

[0143] Step 3:

[0144] The user selects the topic or course they wish to study from the menu. Input: User's course selection, Output: Information about the selected topic or course. The server obtains detailed information about the selected topic and sends it to the generative AI model. Input: Detailed information about the selected topic, Output: Request to generate learning materials. The generative AI model generates the necessary learning materials and sends them back to the server. Input: Generation request, Output: Learning materials. The server sends the formatted data to the terminal, which displays the learning materials on the user's screen. Output: Learning materials displayed to the user.

[0145] Step 4:

[0146] The user clicks a button to begin learning. Input: User's instruction to start, Output: Request to start learning. The server monitors the user's progress in real time and provides feedback with instructions from the generative AI model. Input: User's progress data, Output: Feedback request to the generative AI model. For example, when the user uses the "print function," the generative AI model explains how to use it specifically. Output: Specific feedback to the user.

[0147] Step 5:

[0148] When a user has a question or concern, they use voice input or chat functionality to ask the virtual assistant a question. Input: User's question (voice or text), Output: Question request. The server sends this question to the generative AI model and obtains an appropriate answer. Input: Question request, Output: Answer from the generative AI model. The device presents this answer to the user visually or audibly. Output: Display of answer to user.

[0149] Step 6:

[0150] The user starts a practical project in the virtual reality space, and the server tracks its progress. Input: Instruction to start the project, Output: Collection of project progress data. The server automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, the generative AI model provides a script template for analysis. Input: Current progress data, Output: Analysis script template.

[0151] Step 7:

[0152] Users work together with other learners in a virtual reality space to complete tasks. Input: Start of collaboration, Output: Communication data for task allocation. The server supports communication between users and provides chat room and shared document functions. Input: Collaboration request, Output: Access information for chat rooms and shared documents. The terminal displays the progress of the collaboration in real time and updates the information. Output: Collaboration information updated in real time.

[0153] Step 8:

[0154] When learning is complete, the user clicks the finish button. Input: Finish instruction, Output: Learning end request. The server performs a comprehensive evaluation based on the user's learning history and test results, and generates an evaluation report. Input: Learning history and test results, Output: Evaluation report. The terminal visually presents the evaluation results to the user and displays the next learning step and recommended courses. Output: Display of evaluation results and recommended courses.

[0155] (Application example 1)

[0156] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0157] Traditional educational platforms tend to be static and have limited interactive content, making it difficult to personalize content based on users' interests and level of understanding. This limits their effectiveness, particularly in areas such as entertainment and practical skills. Furthermore, the lack of real-time, interactive feedback makes it difficult to improve users' learning efficiency. Furthermore, the lack of mechanisms to encourage collaboration with other learners makes it difficult for learners to interact with each other or collaborate.

[0158] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0159] In this invention, the server includes: means for authenticating user input information and acquiring the user's learning history and setting information; means for acquiring detailed information about learning themes and courses based on the user's selection and preparing a learning environment in a virtual reality space; means for a generative artificial intelligence to provide appropriate answers and explanations to the user's questions; means for the user to carry out practical projects in the virtual reality space and provide necessary data sets and tools while monitoring their progress; means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report; means for the user to learn entertainment content or acquire production skills based on their knowledge and skills; means for interactively responding to user input through the generative artificial intelligence and providing real-time responses via video and audio; and means for providing personalized educational content and entertainment experiences based on the user's interests. This not only significantly improves the user's learning efficiency but also enables them to acquire practical skills and knowledge in the entertainment field. Furthermore, it promotes cooperation and interaction with other learners, enhancing the learning experience through collaborative work.

[0160] "Generative AI" is an AI technology that generates new information and answers based on user input.

[0161] A "virtual reality space" is a realistic computer-generated virtual environment that a user experiences using a dedicated device.

[0162] An "educational platform" refers to a system or service provided to help learners acquire specific knowledge or skills.

[0163] "User input information" refers to data such as account information, setting information, questions, and feedback that a user provides to the system.

[0164] "Study history" is data that records what a user has studied in the past and their progress.

[0165] "Setting information" is data including individual settings such as themes, layouts, and languages ​​used that are specified by the user for the system.

[0166] "Study topic" refers to the content or field that a user is studying, such as programming or data science.

[0167] "Course details" refers to information including a curriculum based on a specific learning theme, required materials, learning objectives, etc.

[0168] "Learning environment" refers to the entire setup, including consoles, holograms, and interfaces, required for users to learn within a virtual reality space.

[0169] "Interactive response" refers to generative AI responding immediately to real-time questions and requests from users and returning appropriate information and instructions.

[0170] A "hands-on project" is a project in which users learn practical skills through specific tasks and activities in a virtual reality space.

[0171] "Progress" is data that indicates how far a user has progressed in their studies or project tasks.

[0172] A "dataset" is a collection of data needed to solve a specific problem.

[0173] A "tool" is software or equipment that a user uses to accomplish a specific task.

[0174] An "evaluation report" is a report summarizing the results of a comprehensive evaluation based on the user's learning progress and test results.

[0175] "Entertainment content" refers to content that provides users with fun and excitement, and includes games, movies, music videos, and the like.

[0176] "Personalized educational content" refers to learning materials that are optimized to suit the interests and learning styles of individual users.

[0177] An "entertainment experience" is a fun, engaging, interactive activity that users experience through virtual reality spaces and generative artificial intelligence.

[0178] This invention provides an educational platform that integrates generative artificial intelligence and virtual reality space. This system allows users to learn in a virtual reality space (hereinafter referred to as VR space) while engaging in interactive dialogue using generative artificial intelligence (hereinafter referred to as AI), allowing them to efficiently acquire practical skills. Specific embodiments of this invention are described below.

[0179] Hardware and software used

[0180] Hardware: Smartphones, smart glasses, head-mounted displays (HMDs), computer servers

[0181] Software: VR platform software, generative artificial intelligence module, user database management system

[0182] User Login

[0183] The user logs in to the authentication server by entering their account information. The server checks the entered information against the user database, and if authentication is successful, it acquires the user's learning history and setting information and stores them in the session.

[0184] Initializing the VR space

[0185] Based on the configuration information obtained from the server, the device initializes the VR space. During this process, a unique theme and layout are set and an interface is created in the user's language of choice. This allows the user to begin learning in a customized VR environment complete with various menus, dashboards, avatars, and more.

[0186] Select and view learning content

[0187] The user selects from a list of learning themes and courses provided. For example, they can choose "Python Programming" or "Film Production Technology." The server sends detailed information about the selected course (curriculum, required materials, goals, etc.) to the generative AI module, which retrieves the necessary learning materials. The device displays the learning content in the VR space based on the retrieved materials and environment settings. For example, if the user selects a programming course, a code editor and virtual console will appear in the VR space.

[0188] Interactive Dialogue

[0189] When a user has a question while studying, they can ask the AI ​​using voice input or the chat function. The server analyzes the user's question using generative AI and provides appropriate answers and explanations in real time. This feedback is presented to the user visually or audibly.

[0190] As a specific example, if a user asks, "Please tell me how to arrange the lighting in this scene," the AI ​​will provide a detailed explanation of the appropriate lighting arrangement techniques and equipment to use.

[0191] Practical project implementation

[0192] Users begin practical projects involving specific tasks and activities within the VR space. The server monitors their progress and automatically provides datasets and tools as needed. For example, if a user is working on a data analysis project, the server will provide analysis script templates once the dataset has been preprocessed.

[0193] Communication and Feedback

[0194] Users can collaborate with other learners to complete collaborative tasks. The server provides an environment that facilitates communication between users, and the device displays the collaboration status in real time. Based on the learning progress and level of understanding, the system provides appropriate feedback and assistance as needed by the user.

[0195] Assessment of learning

[0196] When the user has completed the learning, he / she can choose to take a self-assessment test. The server will conduct a comprehensive evaluation based on the user's learning history and test results, generate an evaluation report and present it to the user. This will help the user check their progress and select the next learning step or recommended course.

[0197] This system not only improves the user's learning efficiency, but also enables them to acquire practical skills and knowledge in the entertainment field, thereby enriching the user's learning experience.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] The user logs in by entering account information (username and password).

[0201] Input: Username and Password.

[0202] Data processing: The server receives the user's input information and compares it with the user database.

[0203] Output: Authentication result (success / failure), user learning history and setting information when authentication is successful.

[0204] Step 2:

[0205] The server authenticates the user's learning history and setting information and stores it in the session.

[0206] Input: User learning history and setting information upon successful authentication.

[0207] Data processing: Convert learning history and setting information into session data.

[0208] Output: The user session is initialized and the learning history and settings are saved.

[0209] Step 3:

[0210] The device initializes the VR space based on the acquired setting information.

[0211] Input: User settings information (theme, layout, language).

[0212] Data calculation: The VR platform analyzes the setting information and constructs a VR space based on it.

[0213] Output: A customized VR space (menus, dashboards, avatars, etc.).

[0214] Step 4:

[0215] The user selects a learning topic from a list of learning topics and courses provided.

[0216] Input: Study topic, course list, user selection.

[0217] Data processing: The server retrieves detailed information about the selected course (curriculum, required materials, objectives).

[0218] Output: Detailed information about the selected course of study.

[0219] Step 5:

[0220] The device displays learning content in the VR space based on the acquired learning materials and environment setting information.

[0221] Input: Study materials, environment setting information.

[0222] Data processing: Convert learning materials and environment setting information into a format suitable for the VR space.

[0223] Output: Learning content in VR space (e.g., code editor, virtual console).

[0224] Step 6:

[0225] When a user has a question while studying, they can ask the AI ​​using voice input or the chat function.

[0226] Input: A question asked by the user through voice commands or text input.

[0227] Data computation: The server analyzes the question through generative artificial intelligence and generates an answer.

[0228] Output: Real-time answers and explanations from the AI.

[0229] Step 7:

[0230] The device will provide the user with visual or audio feedback from the AI.

[0231] Input: Answer from generative artificial intelligence.

[0232] Data processing: Convert the response data into a format that is easy for users to understand.

[0233] Output: Visual or audio feedback on the user interface.

[0234] Step 8:

[0235] Users initiate practical projects with specific tasks in a VR space and monitor their progress.

[0236] Input: A user-selected practical project task.

[0237] Data Computing: The server monitors the progress and provides the necessary datasets and tools.

[0238] Outputs: User progress data and provided datasets and tools.

[0239] Step 9:

[0240] After completing the learning process, the user can choose to take a self-assessment test. The server will then conduct a comprehensive evaluation and generate and present an evaluation report.

[0241] Input: User's learning completion status, self-assessment test results.

[0242] Data calculation: Comprehensive evaluation based on learning history and test results.

[0243] Output: Assessment report (learning marks, areas for improvement, recommended next courses).

[0244] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0245] This invention provides an educational platform that integrates generative artificial intelligence (AI) with a virtual reality space and also combines an emotion engine. While learning in a virtual reality space, users can use the AI ​​to engage in interactive dialogue and efficiently acquire practical skills. The emotion engine also recognizes the user's emotions, personalizing and optimizing the learning experience.

[0246] Initial Setup

[0247] 1. User login

[0248] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[0249] 2. Initializing the virtual reality space

[0250] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space and starts up the emotion engine. This creates an environment that is easy for the user to learn.

[0251] Select learning content

[0252] 1. Selecting a study topic

[0253] The user selects the topic they want to study from a list of learning themes and courses provided on the platform. For example, the user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, objectives, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[0254] 2. Preparing the virtual environment

[0255] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[0256] Start learning

[0257] 1. Start learning

[0258] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[0259] 2. Starting the Emotion Engine

[0260] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and operation patterns to grasp their emotional state. For example, if the user is tired or excited, the device provides feedback based on that state. The server receives the emotional data in real time and transmits this information to the generative AI.

[0261] 3. Interactive dialogue

[0262] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0263] Practical project implementation

[0264] 1. Project-Based Learning

[0265] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0266] 2. Team collaboration and sharing

[0267] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0268] Communication and Feedback

[0269] 1. Dialogue and feedback

[0270] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0271] 2. Adaptive feedback based on emotional data

[0272] The server provides adaptive feedback based on the user's emotional data. For example, if the user is feeling stressed, the generative AI will provide advice on how to relax and adjust the pace of learning. The device will then visually display this feedback, helping the user to continue learning in a stable emotional state.

[0273] Termination and Evaluation

[0274] 1. Completion of study

[0275] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[0276] 2. Presentation of evaluation results

[0277] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[0278] As described above, the educational platform of the present invention combines generative artificial intelligence, virtual reality space, and an emotion engine to provide an efficient and effective learning environment and assist users in acquiring skills.

[0279] The processing flow will be explained below.

[0280] Step 1: User Login

[0281] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[0282] Step 2: Initializing the Virtual Reality Space

[0283] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space and starts up the emotion engine. This creates an environment that is easy for the user to learn.

[0284] Step 3: Choose a study topic

[0285] The user selects the topic they want to study from a list of learning themes and courses provided on the platform. For example, the user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, objectives, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[0286] Step 4: Preparing the virtual environment

[0287] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[0288] Step 5: Start learning

[0289] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[0290] Step 6: Start the Emotion Engine

[0291] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and operation patterns to grasp their emotional state. For example, if the user is tired or excited, the device provides feedback based on that state. The server receives the emotional data in real time and transmits this information to the generative AI.

[0292] Step 7: Interactive Dialogue

[0293] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0294] Step 8: Project-Based Learning

[0295] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0296] Step 9: Team collaboration and sharing

[0297] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0298] Step 10: Dialogue and feedback

[0299] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0300] Step 11: Adaptive feedback based on emotional data

[0301] The server provides adaptive feedback based on the user's emotional data. For example, if the user is feeling stressed, the generative AI will provide advice on how to relax and adjust the pace of learning. The device will then visually display this feedback, helping the user to continue learning in a stable emotional state.

[0302] Step 12: Ending the training

[0303] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[0304] Step 13: Presenting the evaluation results

[0305] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[0306] Through these steps, the educational platform of the present invention combines generative artificial intelligence, virtual reality space, and emotion engine to provide an efficient and effective learning environment, allowing users to gain deeper understanding and practical skills through personalized learning experiences.

[0307] Example 2

[0308] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0309] In today's educational environment, it is extremely difficult to provide an optimal educational experience for each individual learner. Furthermore, traditional online education systems do not adequately provide adaptive feedback based on learners' emotions and progress. This makes it difficult for learners to efficiently acquire skills and maintain sustained motivation.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0311] In this invention, the server includes means for authenticating user input information and acquiring the user's learning history and setting information, means for initializing the virtual reality environment based on the user's setting information, means for acquiring detailed information about the learning topic or course based on the user's selection and preparing the learning environment in the virtual reality space, means for the user to start learning and for the generative artificial intelligence to provide specific instructions and explanations, means for the terminal to include an emotion engine for analyzing the user's emotional state and providing feedback, means for the user to carry out a practical project in the virtual reality space and provide necessary data sets and tools while monitoring progress, and means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report. This allows learners to receive feedback in real time that is tailored to them, enabling an interactive and effective learning experience.

[0312] "Generative AI" is an AI system that generates appropriate answers and instructions based on user input and provides feedback in real time.

[0313] "Virtual reality space" means a virtual 3D environment that a user can experience visually and operationally, and is used for educational purposes.

[0314] The "emotion engine" is a system that identifies a user's emotional state by analyzing their facial expressions, tone of voice, and operation patterns, and provides feedback based on that.

[0315] "Login means" refers to the procedures and mechanisms by which a user accesses the system using their account information and is authenticated.

[0316] "Study history" refers to data that records a user's past learning activities and progress.

[0317] "Setting information" refers to information such as the theme, layout, and language selected by the user when using the system.

[0318] A "learning topic" is a specific field or topic that a user wants to learn about, and is provided as a course.

[0319] The "virtual environment initialization means" is a mechanism for preparing a virtual reality space based on the user's setting information and creating an environment for starting learning.

[0320] A "hands-on project" is a specific task or learning activity that a user performs in a virtual reality space.

[0321] "Study progress" refers to the progress or achievement achieved by a user in the course of performing a learning activity.

[0322] An "evaluation report" is a report that summarizes the results of a comprehensive evaluation based on the user's learning results.

[0323] This invention provides a system that provides an educational platform that integrates generative artificial intelligence (AI) with a virtual reality space and further combines it with an emotion engine. This allows users to learn in a virtual reality space while engaging in interactive dialogue using the AI, efficiently acquiring practical skills. The emotion engine also recognizes the user's emotions, allowing for personalized and optimized learning experiences.

[0324] Hardware and software used

[0325] The system uses the following major hardware and software:

[0326] Server: Performs data processing and authentication, stores learning history, and manages progress.

[0327] Terminal: A device (e.g., a VR headset, a PC) that allows a user to experience a virtual reality space.

[0328] Generative AI: Providing appropriate answers to user questions and prompts.

[0329] Emotion engine: A system that analyzes the user's facial expressions and tone of voice to understand their emotional state.

[0330] Database: Stores user account information, learning history, setting information, etc.

[0331] Program processing

[0332] A user first accesses the system using a terminal and logs in by entering their account information. The server receives this information and checks the authentication information against a database. If authentication is successful, the server retrieves the user's learning history and setting information and stores it in the session. The user is then notified via their terminal that they have successfully logged in.

[0333] Next, the device initializes the virtual reality space based on the user's settings. This space is configured with the theme, layout, and language used, and holograms and interfaces (menus, dashboards, avatars, etc.) within the virtual reality environment are placed. The emotion engine is also started up at this stage, creating a user-friendly learning environment.

[0334] Once a user selects a topic or course they want to study, the server sends the details (curriculum, required materials, goals, etc.) to the generative artificial intelligence. The device then configures the virtual environment based on this information and displays the appropriate content to the user. For example, if a programming course is selected, a virtual console and code editor will be displayed.

[0335] Once learning begins, the server monitors the user's progress in real time and provides feedback from the generative AI with instructions. If a user has questions during their learning, they can ask them using voice input or the chat function. The generative AI responds appropriately, providing answers and explanations to the questions in real time. For example, if a user asks, "How do I use a loop statement in Python?", the system will explain the question with specific code examples.

[0336] Furthermore, the emotion engine analyzes the user's facial expressions, tone of voice, and operation patterns, and transmits the user's emotional state to the server. Based on the emotional data, generative AI provides adaptive feedback. For example, if the system determines that the user is tired, it displays a message to adjust the learning pace.

[0337] Examples of concrete examples and prompts

[0338] These steps can be further explained through examples.

[0339] Example 1: If a user enters the prompt "Please give me some sample code for a loop statement in Python," the generative artificial intelligence will generate the following answer:

[0340] for variable in list:

[0341] Write the repetition process here

[0342] print(variable)

[0343] Example 2: If a user types, "I want to learn how to clean a dataset. Please tell me the specific steps," the generative AI might suggest the following steps:

[0344] 1. Check for missing values ​​in your data

[0345] 2. Remove or impute missing values ​​as needed

[0346] 3. Check your data for outliers

[0347] 4. Treat or filter outliers

[0348] 5. Format the data

[0349] In this way, the educational platform of the present invention provides each user with an optimal learning experience by combining generative artificial intelligence, a virtual reality space, and an emotion engine.

[0350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0351] Step 1:

[0352] A user accesses the system using a terminal, enters account information (user name and password), and clicks the login button. The server receives this input information and checks the authentication information against the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful.

[0353] Input: Username and Password

[0354] Data processing: Retrieving and verifying authentication information from the database

[0355] Output: Authentication results, learning history, and configuration information

[0356] Step 2:

[0357] The device initializes the virtual reality environment based on the user's settings. Specifically, it applies the selected theme, layout, language, and other settings, and arranges the interface (menu, dashboard, avatar, etc.) in the virtual reality space. The emotion engine also starts up at this stage.

[0358] Input: User settings information

[0359] Data processing: Acquire actual settings and render virtual reality space components

[0360] Output: Initialized virtual reality space

[0361] Step 3:

[0362] Users select a learning topic or course offered on the platform. The server receives the selected course information and sends the details (curriculum, materials, goals, etc.) to the generative AI.

[0363] Input: Select a study topic or course

[0364] Data manipulation: Get detailed information based on your selections

[0365] Output: Detailed information on curriculum, materials, goals, etc.

[0366] Step 4:

[0367] The device configures the virtual learning environment based on the acquired course information. For example, if a programming course is selected, the device configures the environment to display a virtual console and text editor.

[0368] Input: Course details

[0369] Data processing: Setting up a virtual environment tailored to the course content

[0370] Output: Configured virtual learning environment

[0371] Step 5:

[0372] Once the user clicks the button to start learning, the server begins monitoring the user's progress in real time, and the generative AI provides appropriate instructions and explanations in response to the user's questions and inquiries.

[0373] Input: Enter the input to start learning

[0374] Data processing: Real-time monitoring of progress and generating answers to questions

[0375] Output: Real-time progress feedback and answers

[0376] Step 6:

[0377] The device runs an emotion engine that analyzes the user's facial expressions, tone of voice, and operation patterns, and sends the results of this analysis to a server, which provides adaptive feedback tailored to the device's learning.

[0378] Input: User facial expressions, tone of voice, and operation patterns

[0379] Data processing: Emotional state analysis

[0380] Output: Feedback based on emotional state

[0381] Step 7:

[0382] When users ask questions or concerns to the generative AI using voice input or chat, the server analyzes the questions and provides appropriate answers and explanations in real time. The answer is then presented to the user visually or audibly on the device.

[0383] Input: User question

[0384] Data processing: parsing questions and generating answers

[0385] Output: Visual or audio response

[0386] Step 8:

[0387] Users start hands-on projects in a virtual reality space and follow instructions to complete tasks, while the server tracks their progress and provides the necessary datasets and tools.

[0388] Enter: Project Start

[0389] Data processing: Tracking progress and providing the tools you need

[0390] Output: Support during the project

[0391] Step 9:

[0392] When a user collaborates with other learners on a project, the server provides a communication environment (chat rooms, shared documents, etc.), and the terminal displays the situation in real time.

[0393] Input: Start of collaboration

[0394] Data processing: Providing a communication environment and real-time display

[0395] Output: Supporting collaboration

[0396] Step 10:

[0397] Users interact with other learners and AI assistants to ask for feedback, and the server analyzes the interaction and provides additional information and resources.

[0398] Input: Requests for interaction or feedback

[0399] Data processing: Analysis of conversation content and information provision

[0400] Output: Providing additional information and resources

[0401] Step 11:

[0402] The server provides adaptive feedback based on emotional data: if the user is feeling stressed, the generative AI provides relaxation advice, which is visually displayed on the device.

[0403] Input: Emotion data

[0404] Data processing: Emotion data analysis

[0405] Output: Adaptive feedback display

[0406] Step 12:

[0407] When the user completes the learning and clicks the finish button, the server performs a comprehensive evaluation and generates an evaluation report. The terminal presents the report to the user and displays the next learning steps and recommended courses.

[0408] Input: Input for the end of training

[0409] Data processing: Assessment of learning outcomes and report generation

[0410] Output: Assessment report and next steps

[0411] (Application example 2)

[0412] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0413] Conventional educational platforms lacked the means to individually optimize learning content in real time or provide appropriate feedback based on the user's emotional state. Furthermore, there were no appropriate systems for efficient and effective autonomous vehicle operation training. Furthermore, even if practical learning was possible in a virtual reality space, the means for interactively resolving user questions were limited.

[0414] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for authenticating user input information and acquiring the user's learning history and setting information, means for acquiring detailed information on learning themes and courses based on the user's selection and preparing a learning environment in a virtual reality space, means for a generative artificial intelligence to provide appropriate answers and explanations to the user's questions, means for the user to carry out practical projects in the virtual reality space and provide necessary data sets and tools while monitoring the user's progress, means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report, means for learning how to operate an autonomous vehicle and how to respond in an emergency using smart glasses and receiving appropriate instructions from the generative artificial intelligence in real time, means for recognizing the user's emotions through facial expression analysis and individually optimizing the learning content, and means for resolving questions through the user's voice input and engaging in interactive dialogue using the generative artificial intelligence. This enables users to perform optimized learning in real time and efficiently acquire autonomous vehicle operation skills.

[0415] "Generative AI" is AI that has the ability to generate new data and information based on specified conditions.

[0416] A "virtual reality space" is a three-dimensional virtual environment created using computer technology, in which a user can immerse themselves in an environment that mimics physical reality.

[0417] An "educational platform" is a system that provides an online environment where users can select and study various learning content and courses.

[0418] "User input information" refers to data provided by the user to the system, and examples include username, password, and choice of study topic.

[0419] "Study history" is data showing the record of the user's learning so far and the progress of that learning.

[0420] "Setting information" is data related to customizing the learning environment based on the user's preferences.

[0421] "Detailed information about the study topic or course" refers to information such as the curriculum, required materials, and study goals related to the selected study topic or course.

[0422] "Practical projects" refer to tasks or assignments that users actually undertake within a virtual reality space.

[0423] "Progress" refers to the state of a user that indicates how far they have progressed in their studies or projects.

[0424] A "dataset" is a collection of data for use in a study or project.

[0425] "Tools" refers to software and hardware that assist users in their learning.

[0426] An "assessment report" is a comprehensive assessment document generated based on learning progress and test results.

[0427] "Smart glasses" are wearable devices that integrate a display device for providing visual information with input devices such as sensors.

[0428] "Facial expression analysis" is a technology that analyzes a user's facial expressions to understand their emotional state.

[0429] "Emotional state" indicates the emotional state that the user is currently feeling, and includes, for example, stress or joy.

[0430] "Individualized optimization" refers to providing optimal learning content and feedback based on each user's specific conditions and circumstances.

[0431] "Voice input" refers to using a microphone to collect the user's voice and input it as data into the system.

[0432] "Interactive dialogue" refers to the two-way exchange of information between the user and the system in real time.

[0433] An "autonomous vehicle" is a vehicle that can automatically perform driving operations using a computer system.

[0434] This invention provides an educational platform in a virtual reality space using generative artificial intelligence, particularly for learning how to operate autonomous vehicles and respond in emergencies. Users can use smart glasses to get a real-time optimized learning experience.

[0435] The main hardware and software used to realize this system include:

[0436] Hardware: Smart glasses, sensors (for facial expression analysis), microphone (for voice input)

[0437] Software: Unity (virtual reality space creation), OpenCV (facial expression analysis), Dialogflow (voice analysis), TensorFlow (generative artificial intelligence), EmotionAPI (emotion engine)

[0438] The server authenticates the user's input information and obtains the user's learning history and setting information. When the user logs in, the server checks the input account information against the database, and if authentication is successful, obtains the user's history information and stores it in the session. This allows the user to continue learning based on their previous progress.

[0439] Next, the device initializes the virtual reality space based on the user's settings. For example, it uses Unity to create a realistic driving simulation environment. At the same time, it also configures learning materials and tools and activates the emotion engine (Emotion API).

[0440] The system obtains detailed information about the learning topic or course selected by the user and prepares a learning environment in the virtual reality space. For example, if a user selects to learn about driving skills or emergency braking techniques, the system obtains the documents necessary for that course from the server and reflects them in the simulation environment.

[0441] When the user begins learning, generative artificial intelligence (TensorFlow) provides appropriate instructions and answers in real time. If the user does not understand a particular driving maneuver, the system will provide interactive assistance and clarify any questions.

[0442] Furthermore, the smart glasses' camera is used to perform facial expression analysis (OpenCV) and use the Emotion API to understand the user's emotional state. For example, if the user is feeling stressed, the system will adjust the learning pace and provide advice on how to relax.

[0443] After a specific learning session is completed, the server will conduct a comprehensive evaluation based on the user's learning progress and test results, and generate and present an evaluation report, allowing the user to check their learning status and plan their next learning steps.

[0444] As a concrete example, the following prompt sentence is given to a generative AI model:

[0445] Username: Learner

[0446] Scenario: You want to learn how to brake in an emergency.

[0447] Progress: Basic braking techniques learned in previous session

[0448] Emotional state: High stress level

[0449] Prompt for generative AI: Guide learners through next steps on how to brake in an emergency. Include advice on reducing stress.

[0450] In this way, the system can provide users with an individually optimized learning experience, enabling them to efficiently master the skills of operating an autonomous vehicle.

[0451] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0452] Step 1:

[0453] The server authenticates the user's input information. The user uses the smart glasses to enter their account information (username and password) and clicks the login button. The server compares the entered account information with the database, and if authentication is successful, obtains the user's learning history and setting information. This data processing includes searching the database for authentication information, checking for a match, and extracting learning history and setting information, and the authentication result and history / setting information are obtained as output.

[0454] Step 2:

[0455] The device initializes the virtual reality space based on the user's configuration information. Specifically, it uses Unity to build a virtual driving simulation environment. Based on the user's configuration information (theme, layout, language used, etc.), the device places the necessary holograms and interfaces (menus, avatars, etc.) in the virtual reality space and starts the emotion engine (Emotion API). This initialization operation generates a customized learning environment as its output based on the reading of the configuration information and the placement of virtual objects.

[0456] Step 3:

[0457] The user selects the topic they want to learn from a list of learning topics and courses provided on the platform. For example, they select "Emergency Braking Techniques." The device retrieves detailed information corresponding to the selected learning topic from the server. The server then sends the selected course curriculum and necessary materials to the generative AI model, preparing the appropriate resources. This data processing includes analyzing the question data and extracting the appropriate information.

[0458] Step 4:

[0459] The device prepares a learning environment in a virtual reality space based on the acquired learning materials. For example, it displays a braking operation interface and guides on a driving simulation screen. It also runs an emotion engine in parallel to prepare for analyzing the user's facial expressions. This includes visual design using Unity and initial settings for facial expression analysis using OpenCV, and the output provides an environment that is easy for the user to learn.

[0460] Step 5:

[0461] When the user clicks the "Start Learning" button, learning begins in the virtual reality space. The server monitors the user's progress in real time and provides feedback from the generative AI. Specifically, while the user is performing an emergency braking operation, the generative AI model explains the appropriate operation method and points to note. This step is based on real-time analysis of progress data and generation of feedback, with immediate instructions and guidance generated as the output.

[0462] Step 6:

[0463] The device analyzes the user's facial expressions through the smart glasses' camera. It uses OpenCV to capture subtle facial movements and analyzes the data with the Emotion API. This analysis determines whether the user is feeling stressed or tired. This data processing involves collecting and analyzing facial expression data, and the output is a judgment of the user's emotional state.

[0464] Step 7:

[0465] If a user has a question during learning, they can input it through the microphone, and Dialogflow will analyze the voice. For example, if the user asks, "I don't know how to brake in an emergency," the server sends the question to the generative AI model, which generates an appropriate answer or explanation. Based on the collected voice data and natural language processing, the answer is provided as voice or text.

[0466] Step 8:

[0467] Once the learning is complete, the server performs a comprehensive evaluation based on the user's learning progress and test results, and generates an evaluation report. The server collects and analyzes the progress data and test results, and visually presents the evaluation report to the user. This data processing includes statistical analysis of the progress data and report generation, and the evaluation results are provided as the output.

[0468] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0469] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0470] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0471] [Second embodiment]

[0472] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0473] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0474] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0475] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0476] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0477] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0478] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0479] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0480] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0481] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0482] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0483] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0484] This invention provides an educational platform that integrates generative artificial intelligence (AI) and virtual reality (VR) spaces. Users can learn in a virtual reality space while engaging in interactive dialogues using AI, enabling them to efficiently acquire practical skills.

[0485] Initial Setup

[0486] 1. User login

[0487] The user enters their account information (user name and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session.

[0488] 2. Initializing the virtual reality space

[0489] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space.

[0490] Select learning content

[0491] 1. Selecting a study topic

[0492] The user selects the topic they wish to study from a list of learning themes and courses provided on the platform. For example, the user selects a course such as "Python programming" or "data science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative AI. The server then obtains the necessary learning materials from the generative AI, formats the data, and sends it to the device.

[0493] 2. Preparing the virtual environment

[0494] The device prepares for learning based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a rich virtual learning environment and displays it to the user.

[0495] Start learning

[0496] 1. Start learning

[0497] The user clicks a button in the virtual reality interface to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI. For example, when the user uses the "print function," the generative AI explains how to use it.

[0498] 2. Interactive dialogue

[0499] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0500] Practical project implementation

[0501] 1. Project-Based Learning

[0502] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0503] 2. Team collaboration and sharing

[0504] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0505] Communication and Feedback

[0506] 1. Dialogue and feedback

[0507] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0508] Termination and Evaluation

[0509] 1. Completion of study

[0510] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report that includes, for example, the project's evaluation score and areas for improvement.

[0511] 2. Presentation of evaluation results

[0512] The device visually presents the assessment results to the user and displays next learning steps and recommended courses. For example, the device displays an assessment report and a list of next courses to be taken on the screen.

[0513] As described above, the educational platform of the present invention combines generative artificial intelligence and virtual reality space to provide an efficient and effective learning environment and assist users in acquiring skills.

[0514] The processing flow will be explained below.

[0515] Step 1: User Login

[0516] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[0517] Step 2: Initializing the Virtual Reality Space

[0518] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space, creating an environment that is easy for the user to learn.

[0519] Step 3: Choose a study topic

[0520] Users select the topic they want to study from a list of learning themes and courses provided on the platform. For example, a user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[0521] Step 4: Preparing the virtual environment

[0522] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[0523] Step 5: Start learning

[0524] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[0525] Step 6: Interactive Dialogue

[0526] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0527] Step 7: Project-Based Learning

[0528] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0529] Step 8: Team collaboration and sharing

[0530] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0531] Step 9: Dialogue and feedback

[0532] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0533] Step 10: Ending the training

[0534] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[0535] Step 11: Present the evaluation results

[0536] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[0537] Through the above steps, the educational platform of the present invention provides users with an efficient and effective learning environment.

[0538] Example 1

[0539] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0540] In conventional online education systems, learners often receive knowledge one-way, limiting the ability to acquire practical skills. Furthermore, there is no mechanism for responding to users' questions in real time, which can impede learning progress. Furthermore, limited opportunities for cooperation and communication between learners make it difficult to progress with collaborative learning tasks or projects.

[0541] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0542] In this invention, the server includes: means for authenticating user input information and acquiring the user's learning history and setting information; means for acquiring detailed information about learning themes and courses based on the user's selections and preparing a learning environment in a virtual reality space; means for a generative artificial intelligence (AI) to provide appropriate answers and explanations to the user's questions; means for monitoring the user's progress in real time and providing feedback from the AI; means for the user to carry out a practical project in the virtual reality space and provide necessary data sets and tools while monitoring the progress; means for the AI ​​to provide an analysis script template according to the project progress; and means for conducting a comprehensive evaluation based on the user's learning progress and test results, generating and presenting an evaluation report. This enables real-time feedback and question resolution, improving learners' understanding and enabling them to effectively acquire practical skills. It also promotes cooperation among learners, facilitating smooth collaborative work.

[0543] "User" refers to an individual who uses the system to study or carry out a project.

[0544] "Input information" refers to data including authentication information and setting information that a user provides to the system.

[0545] "Learning history" refers to historical data such as the courses a user has taken on the system and their progress.

[0546] "Setting information" refers to setting data such as the theme, layout, and language selected by the user for learning in the virtual reality space.

[0547] "Virtual reality space" refers to a three-dimensional virtual environment generated by a computer.

[0548] "Generative artificial intelligence" refers to an AI model that generates appropriate answers and explanations in response to user questions and requests.

[0549] "Detailed information" refers to information about the selected study topic or course, such as the curriculum, required materials, and objectives.

[0550] A "project" refers to a practical assignment or set of tasks that a user completes in a virtual reality space.

[0551] "Progress" refers to the user's achievements and current status as they progress through their studies or projects.

[0552] A "dataset" refers to a collection of data required to carry out a project or task.

[0553] "Tools" refers to software or features within a virtual environment that users use to advance their learning or projects.

[0554] "Script templates" refer to template code or programs provided by generative artificial intelligence as the project progresses.

[0555] "Comprehensive evaluation" refers to an overall evaluation based on the user's learning progress and test results.

[0556] "Evaluation report" refers to a report summarizing the results of the comprehensive evaluation.

[0557] "Voice input" refers to voice instructions or questions given by the user to the system through a microphone.

[0558] "Chat function" refers to a function that allows a user to enter text and interact with the system and other users.

[0559] The present invention provides a system that provides an educational platform that integrates generative artificial intelligence and virtual reality space, allowing users to learn in a virtual reality space while engaging in interactive dialogue using generative artificial intelligence, thereby efficiently acquiring practical skills.

[0560] Initial Setup

[0561] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from a database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. This process often uses a common authentication system or database management system. For example, MySQL or PostgreSQL can be used for database management.

[0562] The device then initializes the virtual reality space based on the user's settings (e.g., theme, layout, language, etc.). Rather than choosing a fixed layout or theme, virtual reality development platforms such as Unity and Unreal Engine are used to provide dynamically customizable content.

[0563] Select learning content

[0564] Users select the topic they want to learn from a list of learning themes and courses offered on the platform. For example, a user selects a course such as "Python programming" or "data science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative AI model. The server then receives the necessary learning materials from the generative AI model, formats the data, and sends it to the device. This allows users to easily access the materials they need.

[0565] Start learning

[0566] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback with instructions from the generative AI model. For example, when the user uses the "print function," the generative AI model explains how to use it. This allows the user to receive real-time feedback and instantly understand the learning content.

[0567] If a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, a user might ask, "I don't know how to use a loop in Python." The server analyzes the user's question through a generative AI model and provides appropriate answers and explanations in real time, allowing users to quickly resolve problems they encounter during their learning.

[0568] Practical project implementation

[0569] Users begin a practical project in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. Once data preprocessing is complete, the generative AI model provides a script template for analysis. In this way, users can learn independently while receiving support as they progress through the project.

[0570] Users can also share parts of projects with other learners and work together on tasks. For example, a user can collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users. Specifically, it is possible to use chat rooms and shared document functions. This allows users to collaborate in real time and effectively advance their learning.

[0571] Communication and Feedback

[0572] Users can interact with other learners and AI assistants to resolve their questions. For example, if a user requests feedback from other learners about their progress, the server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction, enhancing the learning experience. Specifically, comments from other learners and feedback from AI could be displayed on the screen.

[0573] Termination and Evaluation

[0574] When the user has completed their learning, they click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report. This may include a report that includes the project's evaluation scores and areas for improvement. This report will serve as a reflection on the user's learning and can be used to plan their next learning.

[0575] The device will visually present the assessment results to the user and display next learning steps or recommended courses. Specifically, the device could display an assessment report and a list of next courses on the screen, allowing users to review their learning outcomes and plan their next steps.

[0576] Examples of concrete examples and prompts

[0577] For example, if a user logs into a virtual reality course on "Python Programming," they could use the following prompt:

[0578] "How do I use a for loop in Python?"

[0579] "What's the next step in your data science project?"

[0580] Please give me a concrete example of how to use the print function.

[0581] In this way, the educational platform of the present invention combines generative artificial intelligence and virtual reality space to provide an efficient and effective learning environment and assist users in acquiring skills.

[0582] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0583] Step 1:

[0584] The user accesses the platform's login screen. The user enters their username and password and clicks the login button. Input: Username and password, Output: Login request. The server receives the entered information, retrieves authentication information from the database, and performs verification. Input: Login request, Output: Authentication result. If authentication is successful, the server retrieves the user's learning history and setting information and saves it in the session. Output: Learning history and setting information.

[0585] Step 2:

[0586] The device initializes the virtual reality space based on the user's setting information received from the server. Input: Learning history and setting information, Output: Initialization settings of the virtual reality space. The device customizes the virtual space based on the user's setting information (e.g., theme, layout, language used, etc.) and arranges interfaces such as menus, dashboards, and avatars. Specifically, the virtual reality environment is constructed using Unity or Unreal Engine. Output: Customized virtual reality space.

[0587] Step 3:

[0588] The user selects the topic or course they wish to study from the menu. Input: User's course selection, Output: Information about the selected topic or course. The server obtains detailed information about the selected topic and sends it to the generative AI model. Input: Detailed information about the selected topic, Output: Request to generate learning materials. The generative AI model generates the necessary learning materials and sends them back to the server. Input: Generation request, Output: Learning materials. The server sends the formatted data to the terminal, which displays the learning materials on the user's screen. Output: Learning materials displayed to the user.

[0589] Step 4:

[0590] The user clicks a button to begin learning. Input: User's instruction to start, Output: Request to start learning. The server monitors the user's progress in real time and provides feedback with instructions from the generative AI model. Input: User's progress data, Output: Feedback request to the generative AI model. For example, when the user uses the "print function," the generative AI model explains how to use it specifically. Output: Specific feedback to the user.

[0591] Step 5:

[0592] When a user has a question or concern, they use voice input or chat functionality to ask the virtual assistant a question. Input: User's question (voice or text), Output: Question request. The server sends this question to the generative AI model and obtains an appropriate answer. Input: Question request, Output: Answer from the generative AI model. The device presents this answer to the user visually or audibly. Output: Display of answer to user.

[0593] Step 6:

[0594] The user starts a practical project in the virtual reality space, and the server tracks its progress. Input: Instruction to start the project, Output: Collection of project progress data. The server automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, the generative AI model provides a script template for analysis. Input: Current progress data, Output: Analysis script template.

[0595] Step 7:

[0596] Users work together with other learners in a virtual reality space to complete tasks. Input: Start of collaboration, Output: Communication data for task allocation. The server supports communication between users and provides chat room and shared document functions. Input: Collaboration request, Output: Access information for chat rooms and shared documents. The terminal displays the progress of the collaboration in real time and updates the information. Output: Collaboration information updated in real time.

[0597] Step 8:

[0598] When learning is complete, the user clicks the finish button. Input: Finish instruction, Output: Learning end request. The server performs a comprehensive evaluation based on the user's learning history and test results, and generates an evaluation report. Input: Learning history and test results, Output: Evaluation report. The terminal visually presents the evaluation results to the user and displays the next learning step and recommended courses. Output: Display of evaluation results and recommended courses.

[0599] (Application example 1)

[0600] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0601] Traditional educational platforms tend to be static and have limited interactive content, making it difficult to personalize content based on users' interests and level of understanding. This limits their effectiveness, particularly in areas such as entertainment and practical skills. Furthermore, the lack of real-time, interactive feedback makes it difficult to improve users' learning efficiency. Furthermore, the lack of mechanisms to encourage collaboration with other learners makes it difficult for learners to interact with each other or collaborate.

[0602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0603] In this invention, the server includes: means for authenticating user input information and acquiring the user's learning history and setting information; means for acquiring detailed information about learning themes and courses based on the user's selection and preparing a learning environment in a virtual reality space; means for a generative artificial intelligence to provide appropriate answers and explanations to the user's questions; means for the user to carry out practical projects in the virtual reality space and provide necessary data sets and tools while monitoring their progress; means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report; means for the user to learn entertainment content or acquire production skills based on their knowledge and skills; means for interactively responding to user input through the generative artificial intelligence and providing real-time responses via video and audio; and means for providing personalized educational content and entertainment experiences based on the user's interests. This not only significantly improves the user's learning efficiency but also enables them to acquire practical skills and knowledge in the entertainment field. Furthermore, it promotes cooperation and interaction with other learners, enhancing the learning experience through collaborative work.

[0604] "Generative AI" is an AI technology that generates new information and answers based on user input.

[0605] A "virtual reality space" is a realistic computer-generated virtual environment that a user experiences using a dedicated device.

[0606] An "educational platform" refers to a system or service provided to help learners acquire specific knowledge or skills.

[0607] "User input information" refers to data such as account information, setting information, questions, and feedback that a user provides to the system.

[0608] "Study history" is data that records what a user has studied in the past and their progress.

[0609] "Setting information" is data including individual settings such as themes, layouts, and languages ​​used that are specified by the user for the system.

[0610] "Study topic" refers to the content or field that a user is studying, such as programming or data science.

[0611] "Course details" refers to information including a curriculum based on a specific learning theme, required materials, learning objectives, etc.

[0612] "Learning environment" refers to the entire setup, including consoles, holograms, and interfaces, required for users to learn within a virtual reality space.

[0613] "Interactive response" refers to generative AI responding immediately to real-time questions and requests from users and returning appropriate information and instructions.

[0614] A "hands-on project" is a project in which users learn practical skills through specific tasks and activities in a virtual reality space.

[0615] "Progress" is data that indicates how far a user has progressed in their studies or project tasks.

[0616] A "dataset" is a collection of data needed to solve a specific problem.

[0617] A "tool" is software or equipment that a user uses to accomplish a specific task.

[0618] An "evaluation report" is a report summarizing the results of a comprehensive evaluation based on the user's learning progress and test results.

[0619] "Entertainment content" refers to content that provides users with fun and excitement, and includes games, movies, music videos, and the like.

[0620] "Personalized educational content" refers to learning materials that are optimized to suit the interests and learning styles of individual users.

[0621] An "entertainment experience" is a fun, engaging, interactive activity that users experience through virtual reality spaces and generative artificial intelligence.

[0622] This invention provides an educational platform that integrates generative artificial intelligence and virtual reality space. This system allows users to learn in a virtual reality space (hereinafter referred to as VR space) while engaging in interactive dialogue using generative artificial intelligence (hereinafter referred to as AI), allowing them to efficiently acquire practical skills. Specific embodiments of this invention are described below.

[0623] Hardware and software used

[0624] Hardware: Smartphones, smart glasses, head-mounted displays (HMDs), computer servers

[0625] Software: VR platform software, generative artificial intelligence module, user database management system

[0626] User Login

[0627] The user logs in to the authentication server by entering their account information. The server checks the entered information against the user database, and if authentication is successful, it acquires the user's learning history and setting information and stores them in the session.

[0628] Initializing the VR space

[0629] Based on the configuration information obtained from the server, the device initializes the VR space. During this process, a unique theme and layout are set and an interface is created in the user's language of choice. This allows the user to begin learning in a customized VR environment complete with various menus, dashboards, avatars, and more.

[0630] Select and view learning content

[0631] The user selects from a list of learning themes and courses provided. For example, they can choose "Python Programming" or "Film Production Technology." The server sends detailed information about the selected course (curriculum, required materials, goals, etc.) to the generative AI module, which retrieves the necessary learning materials. The device displays the learning content in the VR space based on the retrieved materials and environment settings. For example, if the user selects a programming course, a code editor and virtual console will appear in the VR space.

[0632] Interactive Dialogue

[0633] When a user has a question while studying, they can ask the AI ​​using voice input or the chat function. The server analyzes the user's question using generative AI and provides appropriate answers and explanations in real time. This feedback is presented to the user visually or audibly.

[0634] As a specific example, if a user asks, "Please tell me how to arrange the lighting in this scene," the AI ​​will provide a detailed explanation of the appropriate lighting arrangement techniques and equipment to use.

[0635] Practical project implementation

[0636] Users begin practical projects involving specific tasks and activities within the VR space. The server monitors their progress and automatically provides datasets and tools as needed. For example, if a user is working on a data analysis project, the server will provide analysis script templates once the dataset has been preprocessed.

[0637] Communication and Feedback

[0638] Users can collaborate with other learners to complete collaborative tasks. The server provides an environment that facilitates communication between users, and the device displays the collaboration status in real time. Based on the learning progress and level of understanding, the system provides appropriate feedback and assistance as needed by the user.

[0639] Assessment of learning

[0640] When the user has completed the learning, he / she can choose to take a self-assessment test. The server will conduct a comprehensive evaluation based on the user's learning history and test results, generate an evaluation report and present it to the user. This will help the user check their progress and select the next learning step or recommended course.

[0641] This system not only improves the user's learning efficiency, but also enables them to acquire practical skills and knowledge in the entertainment field, thereby enriching the user's learning experience.

[0642] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0643] Step 1:

[0644] The user logs in by entering account information (username and password).

[0645] Input: Username and Password.

[0646] Data processing: The server receives the user's input information and compares it with the user database.

[0647] Output: Authentication result (success / failure), user learning history and setting information when authentication is successful.

[0648] Step 2:

[0649] The server authenticates the user's learning history and setting information and stores it in the session.

[0650] Input: User learning history and setting information upon successful authentication.

[0651] Data processing: Convert learning history and setting information into session data.

[0652] Output: The user session is initialized and the learning history and settings are saved.

[0653] Step 3:

[0654] The device initializes the VR space based on the acquired setting information.

[0655] Input: User settings information (theme, layout, language).

[0656] Data calculation: The VR platform analyzes the setting information and constructs a VR space based on it.

[0657] Output: A customized VR space (menus, dashboards, avatars, etc.).

[0658] Step 4:

[0659] The user selects a learning topic from a list of learning topics and courses provided.

[0660] Input: Study topic, course list, user selection.

[0661] Data processing: The server retrieves detailed information about the selected course (curriculum, required materials, objectives).

[0662] Output: Detailed information about the selected course of study.

[0663] Step 5:

[0664] The device displays learning content in the VR space based on the acquired learning materials and environment setting information.

[0665] Input: Study materials, environment setting information.

[0666] Data processing: Convert learning materials and environment setting information into a format suitable for the VR space.

[0667] Output: Learning content in VR space (e.g., code editor, virtual console).

[0668] Step 6:

[0669] When a user has a question while studying, they can ask the AI ​​using voice input or the chat function.

[0670] Input: A question asked by the user through voice commands or text input.

[0671] Data computation: The server analyzes the question through generative artificial intelligence and generates an answer.

[0672] Output: Real-time answers and explanations from the AI.

[0673] Step 7:

[0674] The device will provide the user with visual or audio feedback from the AI.

[0675] Input: Answer from generative artificial intelligence.

[0676] Data processing: Convert the response data into a format that is easy for users to understand.

[0677] Output: Visual or audio feedback on the user interface.

[0678] Step 8:

[0679] Users initiate practical projects with specific tasks in a VR space and monitor their progress.

[0680] Input: A user-selected practical project task.

[0681] Data Computing: The server monitors the progress and provides the necessary datasets and tools.

[0682] Outputs: User progress data and provided datasets and tools.

[0683] Step 9:

[0684] After completing the learning process, the user can choose to take a self-assessment test. The server will then conduct a comprehensive evaluation and generate and present an evaluation report.

[0685] Input: User's learning completion status, self-assessment test results.

[0686] Data calculation: Comprehensive evaluation based on learning history and test results.

[0687] Output: Assessment report (learning marks, areas for improvement, recommended next courses).

[0688] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0689] This invention provides an educational platform that integrates generative artificial intelligence (AI) with a virtual reality space and also combines an emotion engine. While learning in a virtual reality space, users can use the AI ​​to engage in interactive dialogue and efficiently acquire practical skills. The emotion engine also recognizes the user's emotions, personalizing and optimizing the learning experience.

[0690] Initial Setup

[0691] 1. User login

[0692] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[0693] 2. Initializing the virtual reality space

[0694] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space and starts up the emotion engine. This creates an environment that is easy for the user to learn.

[0695] Select learning content

[0696] 1. Selecting a study topic

[0697] The user selects the topic they want to study from a list of learning themes and courses provided on the platform. For example, the user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, objectives, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[0698] 2. Preparing the virtual environment

[0699] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[0700] Start learning

[0701] 1. Start learning

[0702] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[0703] 2. Starting the Emotion Engine

[0704] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and operation patterns to grasp their emotional state. For example, if the user is tired or excited, the device provides feedback based on that state. The server receives the emotional data in real time and transmits this information to the generative AI.

[0705] 3. Interactive dialogue

[0706] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0707] Practical project implementation

[0708] 1. Project-Based Learning

[0709] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0710] 2. Team collaboration and sharing

[0711] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0712] Communication and Feedback

[0713] 1. Dialogue and feedback

[0714] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0715] 2. Adaptive feedback based on emotional data

[0716] The server provides adaptive feedback based on the user's emotional data. For example, if the user is feeling stressed, the generative AI will provide advice on how to relax and adjust the pace of learning. The device will then visually display this feedback, helping the user to continue learning in a stable emotional state.

[0717] Termination and Evaluation

[0718] 1. Completion of study

[0719] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[0720] 2. Presentation of evaluation results

[0721] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[0722] As described above, the educational platform of the present invention combines generative artificial intelligence, virtual reality space, and an emotion engine to provide an efficient and effective learning environment and assist users in acquiring skills.

[0723] The processing flow will be explained below.

[0724] Step 1: User Login

[0725] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[0726] Step 2: Initializing the Virtual Reality Space

[0727] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space and starts up the emotion engine. This creates an environment that is easy for the user to learn.

[0728] Step 3: Choose a study topic

[0729] The user selects the topic they want to study from a list of learning themes and courses provided on the platform. For example, the user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, objectives, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[0730] Step 4: Preparing the virtual environment

[0731] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[0732] Step 5: Start learning

[0733] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[0734] Step 6: Start the Emotion Engine

[0735] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and operation patterns to grasp their emotional state. For example, if the user is tired or excited, the device provides feedback based on that state. The server receives the emotional data in real time and transmits this information to the generative AI.

[0736] Step 7: Interactive Dialogue

[0737] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0738] Step 8: Project-Based Learning

[0739] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0740] Step 9: Team collaboration and sharing

[0741] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0742] Step 10: Dialogue and feedback

[0743] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0744] Step 11: Adaptive feedback based on emotional data

[0745] The server provides adaptive feedback based on the user's emotional data. For example, if the user is feeling stressed, the generative AI will provide advice on how to relax and adjust the pace of learning. The device will then visually display this feedback, helping the user to continue learning in a stable emotional state.

[0746] Step 12: Ending the training

[0747] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[0748] Step 13: Presenting the evaluation results

[0749] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[0750] Through these steps, the educational platform of the present invention combines generative artificial intelligence, virtual reality space, and emotion engine to provide an efficient and effective learning environment, allowing users to gain deeper understanding and practical skills through personalized learning experiences.

[0751] Example 2

[0752] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0753] In today's educational environment, it is extremely difficult to provide an optimal educational experience for each individual learner. Furthermore, traditional online education systems do not adequately provide adaptive feedback based on learners' emotions and progress. This makes it difficult for learners to efficiently acquire skills and maintain sustained motivation.

[0754] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0755] In this invention, the server includes means for authenticating user input information and acquiring the user's learning history and setting information, means for initializing the virtual reality environment based on the user's setting information, means for acquiring detailed information about the learning topic or course based on the user's selection and preparing the learning environment in the virtual reality space, means for the user to start learning and for the generative artificial intelligence to provide specific instructions and explanations, means for the terminal to include an emotion engine for analyzing the user's emotional state and providing feedback, means for the user to carry out a practical project in the virtual reality space and provide necessary data sets and tools while monitoring progress, and means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report. This allows learners to receive feedback in real time that is tailored to them, enabling an interactive and effective learning experience.

[0756] "Generative AI" is an AI system that generates appropriate answers and instructions based on user input and provides feedback in real time.

[0757] "Virtual reality space" means a virtual 3D environment that a user can experience visually and operationally, and is used for educational purposes.

[0758] The "emotion engine" is a system that identifies a user's emotional state by analyzing their facial expressions, tone of voice, and operation patterns, and provides feedback based on that.

[0759] "Login means" refers to the procedures and mechanisms by which a user accesses the system using their account information and is authenticated.

[0760] "Study history" refers to data that records a user's past learning activities and progress.

[0761] "Setting information" refers to information such as the theme, layout, and language selected by the user when using the system.

[0762] A "learning topic" is a specific field or topic that a user wants to learn about, and is provided as a course.

[0763] The "virtual environment initialization means" is a mechanism for preparing a virtual reality space based on the user's setting information and creating an environment for starting learning.

[0764] A "hands-on project" is a specific task or learning activity that a user performs in a virtual reality space.

[0765] "Study progress" refers to the progress or achievement achieved by a user in the course of performing a learning activity.

[0766] An "evaluation report" is a report that summarizes the results of a comprehensive evaluation based on the user's learning results.

[0767] This invention provides a system that provides an educational platform that integrates generative artificial intelligence (AI) with a virtual reality space and further combines it with an emotion engine. This allows users to learn in a virtual reality space while engaging in interactive dialogue using the AI, efficiently acquiring practical skills. The emotion engine also recognizes the user's emotions, allowing for personalized and optimized learning experiences.

[0768] Hardware and software used

[0769] The system uses the following major hardware and software:

[0770] Server: Performs data processing and authentication, stores learning history, and manages progress.

[0771] Terminal: A device (e.g., a VR headset, a PC) that allows a user to experience a virtual reality space.

[0772] Generative AI: Providing appropriate answers to user questions and prompts.

[0773] Emotion engine: A system that analyzes the user's facial expressions and tone of voice to understand their emotional state.

[0774] Database: Stores user account information, learning history, setting information, etc.

[0775] Program processing

[0776] A user first accesses the system using a terminal and logs in by entering their account information. The server receives this information and checks the authentication information against a database. If authentication is successful, the server retrieves the user's learning history and setting information and stores it in the session. The user is then notified via their terminal that they have successfully logged in.

[0777] Next, the device initializes the virtual reality space based on the user's settings. This space is configured with the theme, layout, and language used, and holograms and interfaces (menus, dashboards, avatars, etc.) within the virtual reality environment are placed. The emotion engine is also started up at this stage, creating a user-friendly learning environment.

[0778] Once a user selects a topic or course they want to study, the server sends the details (curriculum, required materials, goals, etc.) to the generative artificial intelligence. The device then configures the virtual environment based on this information and displays the appropriate content to the user. For example, if a programming course is selected, a virtual console and code editor will be displayed.

[0779] Once learning begins, the server monitors the user's progress in real time and provides feedback from the generative AI with instructions. If a user has questions during their learning, they can ask them using voice input or the chat function. The generative AI responds appropriately, providing answers and explanations to the questions in real time. For example, if a user asks, "How do I use a loop statement in Python?", the system will explain the question with specific code examples.

[0780] Furthermore, the emotion engine analyzes the user's facial expressions, tone of voice, and operation patterns, and transmits the user's emotional state to the server. Based on the emotional data, generative AI provides adaptive feedback. For example, if the system determines that the user is tired, it displays a message to adjust the learning pace.

[0781] Examples of concrete examples and prompts

[0782] These steps can be further explained through examples.

[0783] Example 1: If a user enters the prompt "Please give me some sample code for a loop statement in Python," the generative artificial intelligence will generate the following answer:

[0784] for variable in list:

[0785] Write the repetition process here

[0786] print(variable)

[0787] Example 2: If a user types, "I want to learn how to clean a dataset. Please tell me the specific steps," the generative AI might suggest the following steps:

[0788] 1. Check for missing values ​​in your data

[0789] 2. Remove or impute missing values ​​as needed

[0790] 3. Check your data for outliers

[0791] 4. Treat or filter outliers

[0792] 5. Format the data

[0793] In this way, the educational platform of the present invention provides each user with an optimal learning experience by combining generative artificial intelligence, a virtual reality space, and an emotion engine.

[0794] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0795] Step 1:

[0796] A user accesses the system using a terminal, enters account information (user name and password), and clicks the login button. The server receives this input information and checks the authentication information against the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful.

[0797] Input: Username and Password

[0798] Data processing: Retrieving and verifying authentication information from the database

[0799] Output: Authentication results, learning history, and configuration information

[0800] Step 2:

[0801] The device initializes the virtual reality environment based on the user's settings. Specifically, it applies the selected theme, layout, language, and other settings, and arranges the interface (menu, dashboard, avatar, etc.) in the virtual reality space. The emotion engine also starts up at this stage.

[0802] Input: User settings information

[0803] Data processing: Acquire actual settings and render virtual reality space components

[0804] Output: Initialized virtual reality space

[0805] Step 3:

[0806] Users select a learning topic or course offered on the platform. The server receives the selected course information and sends the details (curriculum, materials, goals, etc.) to the generative AI.

[0807] Input: Select a study topic or course

[0808] Data manipulation: Get detailed information based on your selections

[0809] Output: Detailed information on curriculum, materials, goals, etc.

[0810] Step 4:

[0811] The device configures the virtual learning environment based on the acquired course information. For example, if a programming course is selected, the device configures the environment to display a virtual console and text editor.

[0812] Input: Course details

[0813] Data processing: Setting up a virtual environment tailored to the course content

[0814] Output: Configured virtual learning environment

[0815] Step 5:

[0816] Once the user clicks the button to start learning, the server begins monitoring the user's progress in real time, and the generative AI provides appropriate instructions and explanations in response to the user's questions and inquiries.

[0817] Input: Enter the input to start learning

[0818] Data processing: Real-time monitoring of progress and generating answers to questions

[0819] Output: Real-time progress feedback and answers

[0820] Step 6:

[0821] The device runs an emotion engine that analyzes the user's facial expressions, tone of voice, and operation patterns, and sends the results of this analysis to a server, which provides adaptive feedback tailored to the device's learning.

[0822] Input: User facial expressions, tone of voice, and operation patterns

[0823] Data processing: Emotional state analysis

[0824] Output: Feedback based on emotional state

[0825] Step 7:

[0826] When users ask questions or concerns to the generative AI using voice input or chat, the server analyzes the questions and provides appropriate answers and explanations in real time. The answer is then presented to the user visually or audibly on the device.

[0827] Input: User question

[0828] Data processing: parsing questions and generating answers

[0829] Output: Visual or audio response

[0830] Step 8:

[0831] Users start hands-on projects in a virtual reality space and follow instructions to complete tasks, while the server tracks their progress and provides the necessary datasets and tools.

[0832] Enter: Project Start

[0833] Data processing: Tracking progress and providing the tools you need

[0834] Output: Support during the project

[0835] Step 9:

[0836] When a user collaborates with other learners on a project, the server provides a communication environment (chat rooms, shared documents, etc.), and the terminal displays the situation in real time.

[0837] Input: Start of collaboration

[0838] Data processing: Providing a communication environment and real-time display

[0839] Output: Supporting collaboration

[0840] Step 10:

[0841] Users interact with other learners and AI assistants to ask for feedback, and the server analyzes the interaction and provides additional information and resources.

[0842] Input: Requests for interaction or feedback

[0843] Data processing: Analysis of conversation content and information provision

[0844] Output: Providing additional information and resources

[0845] Step 11:

[0846] The server provides adaptive feedback based on emotional data: if the user is feeling stressed, the generative AI provides relaxation advice, which is visually displayed on the device.

[0847] Input: Emotion data

[0848] Data processing: Emotion data analysis

[0849] Output: Adaptive feedback display

[0850] Step 12:

[0851] When the user completes the learning and clicks the finish button, the server performs a comprehensive evaluation and generates an evaluation report. The terminal presents the report to the user and displays the next learning steps and recommended courses.

[0852] Input: Input for the end of training

[0853] Data processing: Assessment of learning outcomes and report generation

[0854] Output: Assessment report and next steps

[0855] (Application example 2)

[0856] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0857] Conventional educational platforms lacked the means to individually optimize learning content in real time or provide appropriate feedback based on the user's emotional state. Furthermore, there were no appropriate systems for efficient and effective autonomous vehicle operation training. Furthermore, even if practical learning was possible in a virtual reality space, the means for interactively resolving user questions were limited.

[0858] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for authenticating user input information and acquiring the user's learning history and setting information, means for acquiring detailed information on learning themes and courses based on the user's selection and preparing a learning environment in a virtual reality space, means for a generative artificial intelligence to provide appropriate answers and explanations to the user's questions, means for the user to carry out practical projects in the virtual reality space and provide necessary data sets and tools while monitoring the user's progress, means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report, means for learning how to operate an autonomous vehicle and how to respond in an emergency using smart glasses and receiving appropriate instructions from the generative artificial intelligence in real time, means for recognizing the user's emotions through facial expression analysis and individually optimizing the learning content, and means for resolving questions through the user's voice input and engaging in interactive dialogue using the generative artificial intelligence. This enables users to perform optimized learning in real time and efficiently acquire autonomous vehicle operation skills.

[0859] "Generative AI" is AI that has the ability to generate new data and information based on specified conditions.

[0860] A "virtual reality space" is a three-dimensional virtual environment created using computer technology, in which a user can immerse themselves in an environment that mimics physical reality.

[0861] An "educational platform" is a system that provides an online environment where users can select and study various learning content and courses.

[0862] "User input information" refers to data provided by the user to the system, and examples include username, password, and choice of study topic.

[0863] "Study history" is data showing the record of the user's learning so far and the progress of that learning.

[0864] "Setting information" is data related to customizing the learning environment based on the user's preferences.

[0865] "Detailed information about the study topic or course" refers to information such as the curriculum, required materials, and study goals related to the selected study topic or course.

[0866] "Practical projects" refer to tasks or assignments that users actually undertake within a virtual reality space.

[0867] "Progress" refers to the state of a user that indicates how far they have progressed in their studies or projects.

[0868] A "dataset" is a collection of data for use in a study or project.

[0869] "Tools" refers to software and hardware that assist users in their learning.

[0870] An "assessment report" is a comprehensive assessment document generated based on learning progress and test results.

[0871] "Smart glasses" are wearable devices that integrate a display device for providing visual information with input devices such as sensors.

[0872] "Facial expression analysis" is a technology that analyzes a user's facial expressions to understand their emotional state.

[0873] "Emotional state" indicates the emotional state that the user is currently feeling, and includes, for example, stress or joy.

[0874] "Individualized optimization" refers to providing optimal learning content and feedback based on each user's specific conditions and circumstances.

[0875] "Voice input" refers to using a microphone to collect the user's voice and input it as data into the system.

[0876] "Interactive dialogue" refers to the two-way exchange of information between the user and the system in real time.

[0877] An "autonomous vehicle" is a vehicle that can automatically perform driving operations using a computer system.

[0878] This invention provides an educational platform in a virtual reality space using generative artificial intelligence, particularly for learning how to operate autonomous vehicles and respond in emergencies. Users can use smart glasses to get a real-time optimized learning experience.

[0879] The main hardware and software used to realize this system include:

[0880] Hardware: Smart glasses, sensors (for facial expression analysis), microphone (for voice input)

[0881] Software: Unity (virtual reality space creation), OpenCV (facial expression analysis), Dialogflow (voice analysis), TensorFlow (generative artificial intelligence), EmotionAPI (emotion engine)

[0882] The server authenticates the user's input information and obtains the user's learning history and setting information. When the user logs in, the server checks the input account information against the database, and if authentication is successful, obtains the user's history information and stores it in the session. This allows the user to continue learning based on their previous progress.

[0883] Next, the device initializes the virtual reality space based on the user's settings. For example, it uses Unity to create a realistic driving simulation environment. At the same time, it also configures learning materials and tools and activates the emotion engine (Emotion API).

[0884] The system obtains detailed information about the learning topic or course selected by the user and prepares a learning environment in the virtual reality space. For example, if a user selects to learn about driving skills or emergency braking techniques, the system obtains the documents necessary for that course from the server and reflects them in the simulation environment.

[0885] When the user begins learning, generative artificial intelligence (TensorFlow) provides appropriate instructions and answers in real time. If the user does not understand a particular driving maneuver, the system will provide interactive assistance and clarify any questions.

[0886] Furthermore, the smart glasses' camera is used to perform facial expression analysis (OpenCV) and use the Emotion API to understand the user's emotional state. For example, if the user is feeling stressed, the system will adjust the learning pace and provide advice on how to relax.

[0887] After a specific learning session is completed, the server will conduct a comprehensive evaluation based on the user's learning progress and test results, and generate and present an evaluation report, allowing the user to check their learning status and plan their next learning steps.

[0888] As a concrete example, the following prompt sentence is given to a generative AI model:

[0889] Username: Learner

[0890] Scenario: You want to learn how to brake in an emergency.

[0891] Progress: Basic braking techniques learned in previous session

[0892] Emotional state: High stress level

[0893] Prompt for generative AI: Guide learners through next steps on how to brake in an emergency. Include advice on reducing stress.

[0894] In this way, the system can provide users with an individually optimized learning experience, enabling them to efficiently master the skills of operating an autonomous vehicle.

[0895] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0896] Step 1:

[0897] The server authenticates the user's input information. The user uses the smart glasses to enter their account information (username and password) and clicks the login button. The server compares the entered account information with the database, and if authentication is successful, obtains the user's learning history and setting information. This data processing includes searching the database for authentication information, checking for a match, and extracting learning history and setting information, and the authentication result and history / setting information are obtained as output.

[0898] Step 2:

[0899] The device initializes the virtual reality space based on the user's configuration information. Specifically, it uses Unity to build a virtual driving simulation environment. Based on the user's configuration information (theme, layout, language used, etc.), the device places the necessary holograms and interfaces (menus, avatars, etc.) in the virtual reality space and starts the emotion engine (Emotion API). This initialization operation generates a customized learning environment as its output based on the reading of the configuration information and the placement of virtual objects.

[0900] Step 3:

[0901] The user selects the topic they want to learn from a list of learning topics and courses provided on the platform. For example, they select "Emergency Braking Techniques." The device retrieves detailed information corresponding to the selected learning topic from the server. The server then sends the selected course curriculum and necessary materials to the generative AI model, preparing the appropriate resources. This data processing includes analyzing the question data and extracting the appropriate information.

[0902] Step 4:

[0903] The device prepares a learning environment in a virtual reality space based on the acquired learning materials. For example, it displays a braking operation interface and guides on a driving simulation screen. It also runs an emotion engine in parallel to prepare for analyzing the user's facial expressions. This includes visual design using Unity and initial settings for facial expression analysis using OpenCV, and the output provides an environment that is easy for the user to learn.

[0904] Step 5:

[0905] When the user clicks the "Start Learning" button, learning begins in the virtual reality space. The server monitors the user's progress in real time and provides feedback from the generative AI. Specifically, while the user is performing an emergency braking operation, the generative AI model explains the appropriate operation method and points to note. This step is based on real-time analysis of progress data and generation of feedback, with immediate instructions and guidance generated as the output.

[0906] Step 6:

[0907] The device analyzes the user's facial expressions through the smart glasses' camera. It uses OpenCV to capture subtle facial movements and analyzes the data with the Emotion API. This analysis determines whether the user is feeling stressed or tired. This data processing involves collecting and analyzing facial expression data, and the output is a judgment of the user's emotional state.

[0908] Step 7:

[0909] If a user has a question during learning, they can input it through the microphone, and Dialogflow will analyze the voice. For example, if the user asks, "I don't know how to brake in an emergency," the server sends the question to the generative AI model, which generates an appropriate answer or explanation. Based on the collected voice data and natural language processing, the answer is provided as voice or text.

[0910] Step 8:

[0911] Once the learning is complete, the server performs a comprehensive evaluation based on the user's learning progress and test results, and generates an evaluation report. The server collects and analyzes the progress data and test results, and visually presents the evaluation report to the user. This data processing includes statistical analysis of the progress data and report generation, and the evaluation results are provided as the output.

[0912] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0913] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0914] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0915] [Third embodiment]

[0916] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0917] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0918] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0919] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0920] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0921] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0922] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0923] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0924] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0925] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0926] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0927] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0928] This invention provides an educational platform that integrates generative artificial intelligence (AI) and virtual reality (VR) spaces. Users can learn in a virtual reality space while engaging in interactive dialogues using AI, enabling them to efficiently acquire practical skills.

[0929] Initial Setup

[0930] 1. User login

[0931] The user enters their account information (user name and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session.

[0932] 2. Initializing the virtual reality space

[0933] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space.

[0934] Select learning content

[0935] 1. Selecting a study topic

[0936] The user selects the topic they wish to study from a list of learning themes and courses provided on the platform. For example, the user selects a course such as "Python programming" or "data science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative AI. The server then obtains the necessary learning materials from the generative AI, formats the data, and sends it to the device.

[0937] 2. Preparing the virtual environment

[0938] The device prepares for learning based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a rich virtual learning environment and displays it to the user.

[0939] Start learning

[0940] 1. Start learning

[0941] The user clicks a button in the virtual reality interface to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI. For example, when the user uses the "print function," the generative AI explains how to use it.

[0942] 2. Interactive dialogue

[0943] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0944] Practical project implementation

[0945] 1. Project-Based Learning

[0946] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0947] 2. Team collaboration and sharing

[0948] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0949] Communication and Feedback

[0950] 1. Dialogue and feedback

[0951] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0952] Termination and Evaluation

[0953] 1. Completion of study

[0954] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report that includes, for example, the project's evaluation score and areas for improvement.

[0955] 2. Presentation of evaluation results

[0956] The device visually presents the assessment results to the user and displays next learning steps and recommended courses. For example, the device displays an assessment report and a list of next courses to be taken on the screen.

[0957] As described above, the educational platform of the present invention combines generative artificial intelligence and virtual reality space to provide an efficient and effective learning environment and assist users in acquiring skills.

[0958] The processing flow will be explained below.

[0959] Step 1: User Login

[0960] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[0961] Step 2: Initializing the Virtual Reality Space

[0962] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space, creating an environment that is easy for the user to learn.

[0963] Step 3: Choose a study topic

[0964] Users select the topic they want to study from a list of learning themes and courses provided on the platform. For example, a user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[0965] Step 4: Preparing the virtual environment

[0966] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[0967] Step 5: Start learning

[0968] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[0969] Step 6: Interactive Dialogue

[0970] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[0971] Step 7: Project-Based Learning

[0972] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[0973] Step 8: Team collaboration and sharing

[0974] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[0975] Step 9: Dialogue and feedback

[0976] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[0977] Step 10: Ending the training

[0978] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[0979] Step 11: Present the evaluation results

[0980] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[0981] Through the above steps, the educational platform of the present invention provides users with an efficient and effective learning environment.

[0982] Example 1

[0983] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0984] In conventional online education systems, learners often receive knowledge one-way, limiting the ability to acquire practical skills. Furthermore, there is no mechanism for responding to users' questions in real time, which can impede learning progress. Furthermore, limited opportunities for cooperation and communication between learners make it difficult to progress with collaborative learning tasks or projects.

[0985] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0986] In this invention, the server includes: means for authenticating user input information and acquiring the user's learning history and setting information; means for acquiring detailed information about learning themes and courses based on the user's selections and preparing a learning environment in a virtual reality space; means for a generative artificial intelligence (AI) to provide appropriate answers and explanations to the user's questions; means for monitoring the user's progress in real time and providing feedback from the AI; means for the user to carry out a practical project in the virtual reality space and provide necessary data sets and tools while monitoring the progress; means for the AI ​​to provide an analysis script template according to the project progress; and means for conducting a comprehensive evaluation based on the user's learning progress and test results, generating and presenting an evaluation report. This enables real-time feedback and question resolution, improving learners' understanding and enabling them to effectively acquire practical skills. It also promotes cooperation among learners, facilitating smooth collaborative work.

[0987] "User" refers to an individual who uses the system to study or carry out a project.

[0988] "Input information" refers to data including authentication information and setting information that a user provides to the system.

[0989] "Learning history" refers to historical data such as the courses a user has taken on the system and their progress.

[0990] "Setting information" refers to setting data such as the theme, layout, and language selected by the user for learning in the virtual reality space.

[0991] "Virtual reality space" refers to a three-dimensional virtual environment generated by a computer.

[0992] "Generative artificial intelligence" refers to an AI model that generates appropriate answers and explanations in response to user questions and requests.

[0993] "Detailed information" refers to information about the selected study topic or course, such as the curriculum, required materials, and objectives.

[0994] A "project" refers to a practical assignment or set of tasks that a user completes in a virtual reality space.

[0995] "Progress" refers to the user's achievements and current status as they progress through their studies or projects.

[0996] A "dataset" refers to a collection of data required to carry out a project or task.

[0997] "Tools" refers to software or features within a virtual environment that users use to advance their learning or projects.

[0998] "Script templates" refer to template code or programs provided by generative artificial intelligence as the project progresses.

[0999] "Comprehensive evaluation" refers to an overall evaluation based on the user's learning progress and test results.

[1000] "Evaluation report" refers to a report summarizing the results of the comprehensive evaluation.

[1001] "Voice input" refers to voice instructions or questions given by the user to the system through a microphone.

[1002] "Chat function" refers to a function that allows a user to enter text and interact with the system and other users.

[1003] The present invention provides a system that provides an educational platform that integrates generative artificial intelligence and virtual reality space, allowing users to learn in a virtual reality space while engaging in interactive dialogue using generative artificial intelligence, thereby efficiently acquiring practical skills.

[1004] Initial Setup

[1005] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from a database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. This process often uses a common authentication system or database management system. For example, MySQL or PostgreSQL can be used for database management.

[1006] The device then initializes the virtual reality space based on the user's settings (e.g., theme, layout, language, etc.). Rather than choosing a fixed layout or theme, virtual reality development platforms such as Unity and Unreal Engine are used to provide dynamically customizable content.

[1007] Select learning content

[1008] Users select the topic they want to learn from a list of learning themes and courses offered on the platform. For example, a user selects a course such as "Python programming" or "data science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative AI model. The server then receives the necessary learning materials from the generative AI model, formats the data, and sends it to the device. This allows users to easily access the materials they need.

[1009] Start learning

[1010] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback with instructions from the generative AI model. For example, when the user uses the "print function," the generative AI model explains how to use it. This allows the user to receive real-time feedback and instantly understand the learning content.

[1011] If a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, a user might ask, "I don't know how to use a loop in Python." The server analyzes the user's question through a generative AI model and provides appropriate answers and explanations in real time, allowing users to quickly resolve problems they encounter during their learning.

[1012] Practical project implementation

[1013] Users begin a practical project in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. Once data preprocessing is complete, the generative AI model provides a script template for analysis. In this way, users can learn independently while receiving support as they progress through the project.

[1014] Users can also share parts of projects with other learners and work together on tasks. For example, a user can collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users. Specifically, it is possible to use chat rooms and shared document functions. This allows users to collaborate in real time and effectively advance their learning.

[1015] Communication and Feedback

[1016] Users can interact with other learners and AI assistants to resolve their questions. For example, if a user requests feedback from other learners about their progress, the server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction, enhancing the learning experience. Specifically, comments from other learners and feedback from AI could be displayed on the screen.

[1017] Termination and Evaluation

[1018] When the user has completed their learning, they click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report. This may include a report that includes the project's evaluation scores and areas for improvement. This report will serve as a reflection on the user's learning and can be used to plan their next learning.

[1019] The device will visually present the assessment results to the user and display next learning steps or recommended courses. Specifically, the device could display an assessment report and a list of next courses on the screen, allowing users to review their learning outcomes and plan their next steps.

[1020] Examples of concrete examples and prompts

[1021] For example, if a user logs into a virtual reality course on "Python Programming," they could use the following prompt:

[1022] "How do I use a for loop in Python?"

[1023] "What's the next step in your data science project?"

[1024] Please give me a concrete example of how to use the print function.

[1025] In this way, the educational platform of the present invention combines generative artificial intelligence and virtual reality space to provide an efficient and effective learning environment and assist users in acquiring skills.

[1026] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1027] Step 1:

[1028] The user accesses the platform's login screen. The user enters their username and password and clicks the login button. Input: Username and password, Output: Login request. The server receives the entered information, retrieves authentication information from the database, and performs verification. Input: Login request, Output: Authentication result. If authentication is successful, the server retrieves the user's learning history and setting information and saves it in the session. Output: Learning history and setting information.

[1029] Step 2:

[1030] The device initializes the virtual reality space based on the user's setting information received from the server. Input: Learning history and setting information, Output: Initialization settings of the virtual reality space. The device customizes the virtual space based on the user's setting information (e.g., theme, layout, language used, etc.) and arranges interfaces such as menus, dashboards, and avatars. Specifically, the virtual reality environment is constructed using Unity or Unreal Engine. Output: Customized virtual reality space.

[1031] Step 3:

[1032] The user selects the topic or course they wish to study from the menu. Input: User's course selection, Output: Information about the selected topic or course. The server obtains detailed information about the selected topic and sends it to the generative AI model. Input: Detailed information about the selected topic, Output: Request to generate learning materials. The generative AI model generates the necessary learning materials and sends them back to the server. Input: Generation request, Output: Learning materials. The server sends the formatted data to the terminal, which displays the learning materials on the user's screen. Output: Learning materials displayed to the user.

[1033] Step 4:

[1034] The user clicks a button to begin learning. Input: User's instruction to start, Output: Request to start learning. The server monitors the user's progress in real time and provides feedback with instructions from the generative AI model. Input: User's progress data, Output: Feedback request to the generative AI model. For example, when the user uses the "print function," the generative AI model explains how to use it specifically. Output: Specific feedback to the user.

[1035] Step 5:

[1036] When a user has a question or concern, they use voice input or chat functionality to ask the virtual assistant a question. Input: User's question (voice or text), Output: Question request. The server sends this question to the generative AI model and obtains an appropriate answer. Input: Question request, Output: Answer from the generative AI model. The device presents this answer to the user visually or audibly. Output: Display of answer to user.

[1037] Step 6:

[1038] The user starts a practical project in the virtual reality space, and the server tracks its progress. Input: Instruction to start the project, Output: Collection of project progress data. The server automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, the generative AI model provides a script template for analysis. Input: Current progress data, Output: Analysis script template.

[1039] Step 7:

[1040] Users work together with other learners in a virtual reality space to complete tasks. Input: Start of collaboration, Output: Communication data for task allocation. The server supports communication between users and provides chat room and shared document functions. Input: Collaboration request, Output: Access information for chat rooms and shared documents. The terminal displays the progress of the collaboration in real time and updates the information. Output: Collaboration information updated in real time.

[1041] Step 8:

[1042] When learning is complete, the user clicks the finish button. Input: Finish instruction, Output: Learning end request. The server performs a comprehensive evaluation based on the user's learning history and test results, and generates an evaluation report. Input: Learning history and test results, Output: Evaluation report. The terminal visually presents the evaluation results to the user and displays the next learning step and recommended courses. Output: Display of evaluation results and recommended courses.

[1043] (Application example 1)

[1044] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1045] Traditional educational platforms tend to be static and have limited interactive content, making it difficult to personalize content based on users' interests and level of understanding. This limits their effectiveness, particularly in areas such as entertainment and practical skills. Furthermore, the lack of real-time, interactive feedback makes it difficult to improve users' learning efficiency. Furthermore, the lack of mechanisms to encourage collaboration with other learners makes it difficult for learners to interact with each other or collaborate.

[1046] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1047] In this invention, the server includes: means for authenticating user input information and acquiring the user's learning history and setting information; means for acquiring detailed information about learning themes and courses based on the user's selection and preparing a learning environment in a virtual reality space; means for a generative artificial intelligence to provide appropriate answers and explanations to the user's questions; means for the user to carry out practical projects in the virtual reality space and provide necessary data sets and tools while monitoring their progress; means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report; means for the user to learn entertainment content or acquire production skills based on their knowledge and skills; means for interactively responding to user input through the generative artificial intelligence and providing real-time responses via video and audio; and means for providing personalized educational content and entertainment experiences based on the user's interests. This not only significantly improves the user's learning efficiency but also enables them to acquire practical skills and knowledge in the entertainment field. Furthermore, it promotes cooperation and interaction with other learners, enhancing the learning experience through collaborative work.

[1048] "Generative AI" is an AI technology that generates new information and answers based on user input.

[1049] A "virtual reality space" is a realistic computer-generated virtual environment that a user experiences using a dedicated device.

[1050] An "educational platform" refers to a system or service provided to help learners acquire specific knowledge or skills.

[1051] "User input information" refers to data such as account information, setting information, questions, and feedback that a user provides to the system.

[1052] "Study history" is data that records what a user has studied in the past and their progress.

[1053] "Setting information" is data including individual settings such as themes, layouts, and languages ​​used that are specified by the user for the system.

[1054] "Study topic" refers to the content or field that a user is studying, such as programming or data science.

[1055] "Course details" refers to information including a curriculum based on a specific learning theme, required materials, learning objectives, etc.

[1056] "Learning environment" refers to the entire setup, including consoles, holograms, and interfaces, required for users to learn within a virtual reality space.

[1057] "Interactive response" refers to generative AI responding immediately to real-time questions and requests from users and returning appropriate information and instructions.

[1058] A "hands-on project" is a project in which users learn practical skills through specific tasks and activities in a virtual reality space.

[1059] "Progress" is data that indicates how far a user has progressed in their studies or project tasks.

[1060] A "dataset" is a collection of data needed to solve a specific problem.

[1061] A "tool" is software or equipment that a user uses to accomplish a specific task.

[1062] An "evaluation report" is a report summarizing the results of a comprehensive evaluation based on the user's learning progress and test results.

[1063] "Entertainment content" refers to content that provides users with fun and excitement, and includes games, movies, music videos, and the like.

[1064] "Personalized educational content" refers to learning materials that are optimized to suit the interests and learning styles of individual users.

[1065] An "entertainment experience" is a fun, engaging, interactive activity that users experience through virtual reality spaces and generative artificial intelligence.

[1066] This invention provides an educational platform that integrates generative artificial intelligence and virtual reality space. This system allows users to learn in a virtual reality space (hereinafter referred to as VR space) while engaging in interactive dialogue using generative artificial intelligence (hereinafter referred to as AI), allowing them to efficiently acquire practical skills. Specific embodiments of this invention are described below.

[1067] Hardware and software used

[1068] Hardware: Smartphones, smart glasses, head-mounted displays (HMDs), computer servers

[1069] Software: VR platform software, generative artificial intelligence module, user database management system

[1070] User Login

[1071] The user logs in to the authentication server by entering their account information. The server checks the entered information against the user database, and if authentication is successful, it acquires the user's learning history and setting information and stores them in the session.

[1072] Initializing the VR space

[1073] Based on the configuration information obtained from the server, the device initializes the VR space. During this process, a unique theme and layout are set and an interface is created in the user's language of choice. This allows the user to begin learning in a customized VR environment complete with various menus, dashboards, avatars, and more.

[1074] Select and view learning content

[1075] The user selects from a list of learning themes and courses provided. For example, they can choose "Python Programming" or "Film Production Technology." The server sends detailed information about the selected course (curriculum, required materials, goals, etc.) to the generative AI module, which retrieves the necessary learning materials. The device displays the learning content in the VR space based on the retrieved materials and environment settings. For example, if the user selects a programming course, a code editor and virtual console will appear in the VR space.

[1076] Interactive Dialogue

[1077] When a user has a question while studying, they can ask the AI ​​using voice input or the chat function. The server analyzes the user's question using generative AI and provides appropriate answers and explanations in real time. This feedback is presented to the user visually or audibly.

[1078] As a specific example, if a user asks, "Please tell me how to arrange the lighting in this scene," the AI ​​will provide a detailed explanation of the appropriate lighting arrangement techniques and equipment to use.

[1079] Practical project implementation

[1080] Users begin practical projects involving specific tasks and activities within the VR space. The server monitors their progress and automatically provides datasets and tools as needed. For example, if a user is working on a data analysis project, the server will provide analysis script templates once the dataset has been preprocessed.

[1081] Communication and Feedback

[1082] Users can collaborate with other learners to complete collaborative tasks. The server provides an environment that facilitates communication between users, and the device displays the collaboration status in real time. Based on the learning progress and level of understanding, the system provides appropriate feedback and assistance as needed by the user.

[1083] Assessment of learning

[1084] When the user has completed the learning, he / she can choose to take a self-assessment test. The server will conduct a comprehensive evaluation based on the user's learning history and test results, generate an evaluation report and present it to the user. This will help the user check their progress and select the next learning step or recommended course.

[1085] This system not only improves the user's learning efficiency, but also enables them to acquire practical skills and knowledge in the entertainment field, thereby enriching the user's learning experience.

[1086] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1087] Step 1:

[1088] The user logs in by entering account information (username and password).

[1089] Input: Username and Password.

[1090] Data processing: The server receives the user's input information and compares it with the user database.

[1091] Output: Authentication result (success / failure), user learning history and setting information when authentication is successful.

[1092] Step 2:

[1093] The server authenticates the user's learning history and setting information and stores it in the session.

[1094] Input: User learning history and setting information upon successful authentication.

[1095] Data processing: Convert learning history and setting information into session data.

[1096] Output: The user session is initialized and the learning history and settings are saved.

[1097] Step 3:

[1098] The device initializes the VR space based on the acquired setting information.

[1099] Input: User settings information (theme, layout, language).

[1100] Data calculation: The VR platform analyzes the setting information and constructs a VR space based on it.

[1101] Output: A customized VR space (menus, dashboards, avatars, etc.).

[1102] Step 4:

[1103] The user selects a learning topic from a list of learning topics and courses provided.

[1104] Input: Study topic, course list, user selection.

[1105] Data processing: The server retrieves detailed information about the selected course (curriculum, required materials, objectives).

[1106] Output: Detailed information about the selected course of study.

[1107] Step 5:

[1108] The device displays learning content in the VR space based on the acquired learning materials and environment setting information.

[1109] Input: Study materials, environment setting information.

[1110] Data processing: Convert learning materials and environment setting information into a format suitable for the VR space.

[1111] Output: Learning content in VR space (e.g., code editor, virtual console).

[1112] Step 6:

[1113] When a user has a question while studying, they can ask the AI ​​using voice input or the chat function.

[1114] Input: A question asked by the user through voice commands or text input.

[1115] Data computation: The server analyzes the question through generative artificial intelligence and generates an answer.

[1116] Output: Real-time answers and explanations from the AI.

[1117] Step 7:

[1118] The device will provide the user with visual or audio feedback from the AI.

[1119] Input: Answer from generative artificial intelligence.

[1120] Data processing: Convert the response data into a format that is easy for users to understand.

[1121] Output: Visual or audio feedback on the user interface.

[1122] Step 8:

[1123] Users initiate practical projects with specific tasks in a VR space and monitor their progress.

[1124] Input: A user-selected practical project task.

[1125] Data Computing: The server monitors the progress and provides the necessary datasets and tools.

[1126] Outputs: User progress data and provided datasets and tools.

[1127] Step 9:

[1128] After completing the learning process, the user can choose to take a self-assessment test. The server will then conduct a comprehensive evaluation and generate and present an evaluation report.

[1129] Input: User's learning completion status, self-assessment test results.

[1130] Data calculation: Comprehensive evaluation based on learning history and test results.

[1131] Output: Assessment report (learning marks, areas for improvement, recommended next courses).

[1132] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1133] This invention provides an educational platform that integrates generative artificial intelligence (AI) with a virtual reality space and also combines an emotion engine. While learning in a virtual reality space, users can use the AI ​​to engage in interactive dialogue and efficiently acquire practical skills. The emotion engine also recognizes the user's emotions, personalizing and optimizing the learning experience.

[1134] Initial Setup

[1135] 1. User login

[1136] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[1137] 2. Initializing the virtual reality space

[1138] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space and starts up the emotion engine. This creates an environment that is easy for the user to learn.

[1139] Select learning content

[1140] 1. Selecting a study topic

[1141] The user selects the topic they want to study from a list of learning themes and courses provided on the platform. For example, the user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, objectives, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[1142] 2. Preparing the virtual environment

[1143] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[1144] Start learning

[1145] 1. Start learning

[1146] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[1147] 2. Starting the Emotion Engine

[1148] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and operation patterns to grasp their emotional state. For example, if the user is tired or excited, the device provides feedback based on that state. The server receives the emotional data in real time and transmits this information to the generative AI.

[1149] 3. Interactive dialogue

[1150] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[1151] Practical project implementation

[1152] 1. Project-Based Learning

[1153] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[1154] 2. Team collaboration and sharing

[1155] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[1156] Communication and Feedback

[1157] 1. Dialogue and feedback

[1158] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[1159] 2. Adaptive feedback based on emotional data

[1160] The server provides adaptive feedback based on the user's emotional data. For example, if the user is feeling stressed, the generative AI will provide advice on how to relax and adjust the pace of learning. The device will then visually display this feedback, helping the user to continue learning in a stable emotional state.

[1161] Termination and Evaluation

[1162] 1. Completion of study

[1163] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[1164] 2. Presentation of evaluation results

[1165] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[1166] As described above, the educational platform of the present invention combines generative artificial intelligence, virtual reality space, and an emotion engine to provide an efficient and effective learning environment and assist users in acquiring skills.

[1167] The processing flow will be explained below.

[1168] Step 1: User Login

[1169] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[1170] Step 2: Initializing the Virtual Reality Space

[1171] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space and starts up the emotion engine. This creates an environment that is easy for the user to learn.

[1172] Step 3: Choose a study topic

[1173] The user selects the topic they want to study from a list of learning themes and courses provided on the platform. For example, the user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, objectives, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[1174] Step 4: Preparing the virtual environment

[1175] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[1176] Step 5: Start learning

[1177] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[1178] Step 6: Start the Emotion Engine

[1179] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and operation patterns to grasp their emotional state. For example, if the user is tired or excited, the device provides feedback based on that state. The server receives the emotional data in real time and transmits this information to the generative AI.

[1180] Step 7: Interactive Dialogue

[1181] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[1182] Step 8: Project-Based Learning

[1183] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[1184] Step 9: Team collaboration and sharing

[1185] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[1186] Step 10: Dialogue and feedback

[1187] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[1188] Step 11: Adaptive feedback based on emotional data

[1189] The server provides adaptive feedback based on the user's emotional data. For example, if the user is feeling stressed, the generative AI will provide advice on how to relax and adjust the pace of learning. The device will then visually display this feedback, helping the user to continue learning in a stable emotional state.

[1190] Step 12: Ending the training

[1191] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[1192] Step 13: Presenting the evaluation results

[1193] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[1194] Through these steps, the educational platform of the present invention combines generative artificial intelligence, virtual reality space, and emotion engine to provide an efficient and effective learning environment, allowing users to gain deeper understanding and practical skills through personalized learning experiences.

[1195] Example 2

[1196] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1197] In today's educational environment, it is extremely difficult to provide an optimal educational experience for each individual learner. Furthermore, traditional online education systems do not adequately provide adaptive feedback based on learners' emotions and progress. This makes it difficult for learners to efficiently acquire skills and maintain sustained motivation.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1199] In this invention, the server includes means for authenticating user input information and acquiring the user's learning history and setting information, means for initializing the virtual reality environment based on the user's setting information, means for acquiring detailed information about the learning topic or course based on the user's selection and preparing the learning environment in the virtual reality space, means for the user to start learning and for the generative artificial intelligence to provide specific instructions and explanations, means for the terminal to include an emotion engine for analyzing the user's emotional state and providing feedback, means for the user to carry out a practical project in the virtual reality space and provide necessary data sets and tools while monitoring progress, and means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report. This allows learners to receive feedback in real time that is tailored to them, enabling an interactive and effective learning experience.

[1200] "Generative AI" is an AI system that generates appropriate answers and instructions based on user input and provides feedback in real time.

[1201] "Virtual reality space" means a virtual 3D environment that a user can experience visually and operationally, and is used for educational purposes.

[1202] The "emotion engine" is a system that identifies a user's emotional state by analyzing their facial expressions, tone of voice, and operation patterns, and provides feedback based on that.

[1203] "Login means" refers to the procedures and mechanisms by which a user accesses the system using their account information and is authenticated.

[1204] "Study history" refers to data that records a user's past learning activities and progress.

[1205] "Setting information" refers to information such as the theme, layout, and language selected by the user when using the system.

[1206] A "learning topic" is a specific field or topic that a user wants to learn about, and is provided as a course.

[1207] The "virtual environment initialization means" is a mechanism for preparing a virtual reality space based on the user's setting information and creating an environment for starting learning.

[1208] A "hands-on project" is a specific task or learning activity that a user performs in a virtual reality space.

[1209] "Study progress" refers to the progress or achievement achieved by a user in the course of performing a learning activity.

[1210] An "evaluation report" is a report that summarizes the results of a comprehensive evaluation based on the user's learning results.

[1211] This invention provides a system that provides an educational platform that integrates generative artificial intelligence (AI) with a virtual reality space and further combines it with an emotion engine. This allows users to learn in a virtual reality space while engaging in interactive dialogue using the AI, efficiently acquiring practical skills. The emotion engine also recognizes the user's emotions, allowing for personalized and optimized learning experiences.

[1212] Hardware and software used

[1213] The system uses the following major hardware and software:

[1214] Server: Performs data processing and authentication, stores learning history, and manages progress.

[1215] Terminal: A device (e.g., a VR headset, a PC) that allows a user to experience a virtual reality space.

[1216] Generative AI: Providing appropriate answers to user questions and prompts.

[1217] Emotion engine: A system that analyzes the user's facial expressions and tone of voice to understand their emotional state.

[1218] Database: Stores user account information, learning history, setting information, etc.

[1219] Program processing

[1220] A user first accesses the system using a terminal and logs in by entering their account information. The server receives this information and checks the authentication information against a database. If authentication is successful, the server retrieves the user's learning history and setting information and stores it in the session. The user is then notified via their terminal that they have successfully logged in.

[1221] Next, the device initializes the virtual reality space based on the user's settings. This space is configured with the theme, layout, and language used, and holograms and interfaces (menus, dashboards, avatars, etc.) within the virtual reality environment are placed. The emotion engine is also started up at this stage, creating a user-friendly learning environment.

[1222] Once a user selects a topic or course they want to study, the server sends the details (curriculum, required materials, goals, etc.) to the generative artificial intelligence. The device then configures the virtual environment based on this information and displays the appropriate content to the user. For example, if a programming course is selected, a virtual console and code editor will be displayed.

[1223] Once learning begins, the server monitors the user's progress in real time and provides feedback from the generative AI with instructions. If a user has questions during their learning, they can ask them using voice input or the chat function. The generative AI responds appropriately, providing answers and explanations to the questions in real time. For example, if a user asks, "How do I use a loop statement in Python?", the system will explain the question with specific code examples.

[1224] Furthermore, the emotion engine analyzes the user's facial expressions, tone of voice, and operation patterns, and transmits the user's emotional state to the server. Based on the emotional data, generative AI provides adaptive feedback. For example, if the system determines that the user is tired, it displays a message to adjust the learning pace.

[1225] Examples of concrete examples and prompts

[1226] These steps can be further explained through examples.

[1227] Example 1: If a user enters the prompt "Please give me some sample code for a loop statement in Python," the generative artificial intelligence will generate the following answer:

[1228] for variable in list:

[1229] Write the repetition process here

[1230] print(variable)

[1231] Example 2: If a user types, "I want to learn how to clean a dataset. Please tell me the specific steps," the generative AI might suggest the following steps:

[1232] 1. Check for missing values ​​in your data

[1233] 2. Remove or impute missing values ​​as needed

[1234] 3. Check your data for outliers

[1235] 4. Treat or filter outliers

[1236] 5. Format the data

[1237] In this way, the educational platform of the present invention provides each user with an optimal learning experience by combining generative artificial intelligence, a virtual reality space, and an emotion engine.

[1238] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1239] Step 1:

[1240] A user accesses the system using a terminal, enters account information (user name and password), and clicks the login button. The server receives this input information and checks the authentication information against the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful.

[1241] Input: Username and Password

[1242] Data processing: Retrieving and verifying authentication information from the database

[1243] Output: Authentication results, learning history, and configuration information

[1244] Step 2:

[1245] The device initializes the virtual reality environment based on the user's settings. Specifically, it applies the selected theme, layout, language, and other settings, and arranges the interface (menu, dashboard, avatar, etc.) in the virtual reality space. The emotion engine also starts up at this stage.

[1246] Input: User settings information

[1247] Data processing: Acquire actual settings and render virtual reality space components

[1248] Output: Initialized virtual reality space

[1249] Step 3:

[1250] Users select a learning topic or course offered on the platform. The server receives the selected course information and sends the details (curriculum, materials, goals, etc.) to the generative AI.

[1251] Input: Select a study topic or course

[1252] Data manipulation: Get detailed information based on your selections

[1253] Output: Detailed information on curriculum, materials, goals, etc.

[1254] Step 4:

[1255] The device configures the virtual learning environment based on the acquired course information. For example, if a programming course is selected, the device configures the environment to display a virtual console and text editor.

[1256] Input: Course details

[1257] Data processing: Setting up a virtual environment tailored to the course content

[1258] Output: Configured virtual learning environment

[1259] Step 5:

[1260] Once the user clicks the button to start learning, the server begins monitoring the user's progress in real time, and the generative AI provides appropriate instructions and explanations in response to the user's questions and inquiries.

[1261] Input: Enter the input to start learning

[1262] Data processing: Real-time monitoring of progress and generating answers to questions

[1263] Output: Real-time progress feedback and answers

[1264] Step 6:

[1265] The device runs an emotion engine that analyzes the user's facial expressions, tone of voice, and operation patterns, and sends the results of this analysis to a server, which provides adaptive feedback tailored to the device's learning.

[1266] Input: User facial expressions, tone of voice, and operation patterns

[1267] Data processing: Emotional state analysis

[1268] Output: Feedback based on emotional state

[1269] Step 7:

[1270] When users ask questions or concerns to the generative AI using voice input or chat, the server analyzes the questions and provides appropriate answers and explanations in real time. The answer is then presented to the user visually or audibly on the device.

[1271] Input: User question

[1272] Data processing: parsing questions and generating answers

[1273] Output: Visual or audio response

[1274] Step 8:

[1275] Users start hands-on projects in a virtual reality space and follow instructions to complete tasks, while the server tracks their progress and provides the necessary datasets and tools.

[1276] Enter: Project Start

[1277] Data processing: Tracking progress and providing the tools you need

[1278] Output: Support during the project

[1279] Step 9:

[1280] When a user collaborates with other learners on a project, the server provides a communication environment (chat rooms, shared documents, etc.), and the terminal displays the situation in real time.

[1281] Input: Start of collaboration

[1282] Data processing: Providing a communication environment and real-time display

[1283] Output: Supporting collaboration

[1284] Step 10:

[1285] Users interact with other learners and AI assistants to ask for feedback, and the server analyzes the interaction and provides additional information and resources.

[1286] Input: Requests for interaction or feedback

[1287] Data processing: Analysis of conversation content and information provision

[1288] Output: Providing additional information and resources

[1289] Step 11:

[1290] The server provides adaptive feedback based on emotional data: if the user is feeling stressed, the generative AI provides relaxation advice, which is visually displayed on the device.

[1291] Input: Emotion data

[1292] Data processing: Emotion data analysis

[1293] Output: Adaptive feedback display

[1294] Step 12:

[1295] When the user completes the learning and clicks the finish button, the server performs a comprehensive evaluation and generates an evaluation report. The terminal presents the report to the user and displays the next learning steps and recommended courses.

[1296] Input: Input for the end of training

[1297] Data processing: Assessment of learning outcomes and report generation

[1298] Output: Assessment report and next steps

[1299] (Application example 2)

[1300] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1301] Conventional educational platforms lacked the means to individually optimize learning content in real time or provide appropriate feedback based on the user's emotional state. Furthermore, there were no appropriate systems for efficient and effective autonomous vehicle operation training. Furthermore, even if practical learning was possible in a virtual reality space, the means for interactively resolving user questions were limited.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for authenticating user input information and acquiring the user's learning history and setting information, means for acquiring detailed information on learning themes and courses based on the user's selection and preparing a learning environment in a virtual reality space, means for a generative artificial intelligence to provide appropriate answers and explanations to the user's questions, means for the user to carry out practical projects in the virtual reality space and provide necessary data sets and tools while monitoring the user's progress, means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report, means for learning how to operate an autonomous vehicle and how to respond in an emergency using smart glasses and receiving appropriate instructions from the generative artificial intelligence in real time, means for recognizing the user's emotions through facial expression analysis and individually optimizing the learning content, and means for resolving questions through the user's voice input and engaging in interactive dialogue using the generative artificial intelligence. This enables users to perform optimized learning in real time and efficiently acquire autonomous vehicle operation skills.

[1303] "Generative AI" is AI that has the ability to generate new data and information based on specified conditions.

[1304] A "virtual reality space" is a three-dimensional virtual environment created using computer technology, in which a user can immerse themselves in an environment that mimics physical reality.

[1305] An "educational platform" is a system that provides an online environment where users can select and study various learning content and courses.

[1306] "User input information" refers to data provided by the user to the system, and examples include username, password, and choice of study topic.

[1307] "Study history" is data showing the record of the user's learning so far and the progress of that learning.

[1308] "Setting information" is data related to customizing the learning environment based on the user's preferences.

[1309] "Detailed information about the study topic or course" refers to information such as the curriculum, required materials, and study goals related to the selected study topic or course.

[1310] "Practical projects" refer to tasks or assignments that users actually undertake within a virtual reality space.

[1311] "Progress" refers to the state of a user that indicates how far they have progressed in their studies or projects.

[1312] A "dataset" is a collection of data for use in a study or project.

[1313] "Tools" refers to software and hardware that assist users in their learning.

[1314] An "assessment report" is a comprehensive assessment document generated based on learning progress and test results.

[1315] "Smart glasses" are wearable devices that integrate a display device for providing visual information with input devices such as sensors.

[1316] "Facial expression analysis" is a technology that analyzes a user's facial expressions to understand their emotional state.

[1317] "Emotional state" indicates the emotional state that the user is currently feeling, and includes, for example, stress or joy.

[1318] "Individualized optimization" refers to providing optimal learning content and feedback based on each user's specific conditions and circumstances.

[1319] "Voice input" refers to using a microphone to collect the user's voice and input it as data into the system.

[1320] "Interactive dialogue" refers to the two-way exchange of information between the user and the system in real time.

[1321] An "autonomous vehicle" is a vehicle that can automatically perform driving operations using a computer system.

[1322] This invention provides an educational platform in a virtual reality space using generative artificial intelligence, particularly for learning how to operate autonomous vehicles and respond in emergencies. Users can use smart glasses to get a real-time optimized learning experience.

[1323] The main hardware and software used to realize this system include:

[1324] Hardware: Smart glasses, sensors (for facial expression analysis), microphone (for voice input)

[1325] Software: Unity (virtual reality space creation), OpenCV (facial expression analysis), Dialogflow (voice analysis), TensorFlow (generative artificial intelligence), EmotionAPI (emotion engine)

[1326] The server authenticates the user's input information and obtains the user's learning history and setting information. When the user logs in, the server checks the input account information against the database, and if authentication is successful, obtains the user's history information and stores it in the session. This allows the user to continue learning based on their previous progress.

[1327] Next, the device initializes the virtual reality space based on the user's settings. For example, it uses Unity to create a realistic driving simulation environment. At the same time, it also configures learning materials and tools and activates the emotion engine (Emotion API).

[1328] The system obtains detailed information about the learning topic or course selected by the user and prepares a learning environment in the virtual reality space. For example, if a user selects to learn about driving skills or emergency braking techniques, the system obtains the documents necessary for that course from the server and reflects them in the simulation environment.

[1329] When the user begins learning, generative artificial intelligence (TensorFlow) provides appropriate instructions and answers in real time. If the user does not understand a particular driving maneuver, the system will provide interactive assistance and clarify any questions.

[1330] Furthermore, the smart glasses' camera is used to perform facial expression analysis (OpenCV) and use the Emotion API to understand the user's emotional state. For example, if the user is feeling stressed, the system will adjust the learning pace and provide advice on how to relax.

[1331] After a specific learning session is completed, the server will conduct a comprehensive evaluation based on the user's learning progress and test results, and generate and present an evaluation report, allowing the user to check their learning status and plan their next learning steps.

[1332] As a concrete example, the following prompt sentence is given to a generative AI model:

[1333] Username: Learner

[1334] Scenario: You want to learn how to brake in an emergency.

[1335] Progress: Basic braking techniques learned in previous session

[1336] Emotional state: High stress level

[1337] Prompt for generative AI: Guide learners through next steps on how to brake in an emergency. Include advice on reducing stress.

[1338] In this way, the system can provide users with an individually optimized learning experience, enabling them to efficiently master the skills of operating an autonomous vehicle.

[1339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1340] Step 1:

[1341] The server authenticates the user's input information. The user uses the smart glasses to enter their account information (username and password) and clicks the login button. The server compares the entered account information with the database, and if authentication is successful, obtains the user's learning history and setting information. This data processing includes searching the database for authentication information, checking for a match, and extracting learning history and setting information, and the authentication result and history / setting information are obtained as output.

[1342] Step 2:

[1343] The device initializes the virtual reality space based on the user's configuration information. Specifically, it uses Unity to build a virtual driving simulation environment. Based on the user's configuration information (theme, layout, language used, etc.), the device places the necessary holograms and interfaces (menus, avatars, etc.) in the virtual reality space and starts the emotion engine (Emotion API). This initialization operation generates a customized learning environment as its output based on the reading of the configuration information and the placement of virtual objects.

[1344] Step 3:

[1345] The user selects the topic they want to learn from a list of learning topics and courses provided on the platform. For example, they select "Emergency Braking Techniques." The device retrieves detailed information corresponding to the selected learning topic from the server. The server then sends the selected course curriculum and necessary materials to the generative AI model, preparing the appropriate resources. This data processing includes analyzing the question data and extracting the appropriate information.

[1346] Step 4:

[1347] The device prepares a learning environment in a virtual reality space based on the acquired learning materials. For example, it displays a braking operation interface and guides on a driving simulation screen. It also runs an emotion engine in parallel to prepare for analyzing the user's facial expressions. This includes visual design using Unity and initial settings for facial expression analysis using OpenCV, and the output provides an environment that is easy for the user to learn.

[1348] Step 5:

[1349] When the user clicks the "Start Learning" button, learning begins in the virtual reality space. The server monitors the user's progress in real time and provides feedback from the generative AI. Specifically, while the user is performing an emergency braking operation, the generative AI model explains the appropriate operation method and points to note. This step is based on real-time analysis of progress data and generation of feedback, with immediate instructions and guidance generated as the output.

[1350] Step 6:

[1351] The device analyzes the user's facial expressions through the smart glasses' camera. It uses OpenCV to capture subtle facial movements and analyzes the data with the Emotion API. This analysis determines whether the user is feeling stressed or tired. This data processing involves collecting and analyzing facial expression data, and the output is a judgment of the user's emotional state.

[1352] Step 7:

[1353] If a user has a question during learning, they can input it through the microphone, and Dialogflow will analyze the voice. For example, if the user asks, "I don't know how to brake in an emergency," the server sends the question to the generative AI model, which generates an appropriate answer or explanation. Based on the collected voice data and natural language processing, the answer is provided as voice or text.

[1354] Step 8:

[1355] Once the learning is complete, the server performs a comprehensive evaluation based on the user's learning progress and test results, and generates an evaluation report. The server collects and analyzes the progress data and test results, and visually presents the evaluation report to the user. This data processing includes statistical analysis of the progress data and report generation, and the evaluation results are provided as the output.

[1356] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1357] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1358] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1359] [Fourth embodiment]

[1360] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1361] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1362] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1363] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1364] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1365] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1366] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1367] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1368] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1369] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1370] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1371] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1372] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1373] This invention provides an educational platform that integrates generative artificial intelligence (AI) and virtual reality (VR) spaces. Users can learn in a virtual reality space while engaging in interactive dialogues using AI, enabling them to efficiently acquire practical skills.

[1374] Initial Setup

[1375] 1. User login

[1376] The user enters their account information (user name and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session.

[1377] 2. Initializing the virtual reality space

[1378] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space.

[1379] Select learning content

[1380] 1. Selecting a study topic

[1381] The user selects the topic they wish to study from a list of learning themes and courses provided on the platform. For example, the user selects a course such as "Python programming" or "data science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative AI. The server then obtains the necessary learning materials from the generative AI, formats the data, and sends it to the device.

[1382] 2. Preparing the virtual environment

[1383] The device prepares for learning based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a rich virtual learning environment and displays it to the user.

[1384] Start learning

[1385] 1. Start learning

[1386] The user clicks a button in the virtual reality interface to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI. For example, when the user uses the "print function," the generative AI explains how to use it.

[1387] 2. Interactive dialogue

[1388] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[1389] Practical project implementation

[1390] 1. Project-Based Learning

[1391] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[1392] 2. Team collaboration and sharing

[1393] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[1394] Communication and Feedback

[1395] 1. Dialogue and feedback

[1396] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[1397] Termination and Evaluation

[1398] 1. Completion of study

[1399] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report that includes, for example, the project's evaluation score and areas for improvement.

[1400] 2. Presentation of evaluation results

[1401] The device visually presents the assessment results to the user and displays next learning steps and recommended courses. For example, the device displays an assessment report and a list of next courses to be taken on the screen.

[1402] As described above, the educational platform of the present invention combines generative artificial intelligence and virtual reality space to provide an efficient and effective learning environment and assist users in acquiring skills.

[1403] The processing flow will be explained below.

[1404] Step 1: User Login

[1405] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from the database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. The terminal notifies the user that login was successful and prepares to proceed to the next step.

[1406] Step 2: Initializing the Virtual Reality Space

[1407] The device initializes the virtual reality space based on the user's settings (e.g., theme, layout, language used, etc.). The device then places holograms and interfaces (menus, dashboards, avatars, etc.) in the virtual reality space, creating an environment that is easy for the user to learn.

[1408] Step 3: Choose a study topic

[1409] Users select the topic they want to study from a list of learning themes and courses provided on the platform. For example, a user clicks on a course such as "Python Programming" or "Data Science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative artificial intelligence. This prepares the resources necessary for the selected course.

[1410] Step 4: Preparing the virtual environment

[1411] The device prepares the learning environment based on the acquired learning materials and environment setting information. For example, if the user selects a programming course, the device displays a code editor and a virtual console. The device provides a visually rich virtual learning environment, helping the user to smoothly progress through the learning process.

[1412] Step 5: Start learning

[1413] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback from the generative AI with instructions. For example, when the user uses the "print function" for the first time, the generative AI explains how to use it specifically. This allows the user to proceed with their learning while clearing up any questions they may have.

[1414] Step 6: Interactive Dialogue

[1415] When a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, the user might ask, "I don't know how to use a loop statement in Python." The server analyzes the user's question through generative AI and provides appropriate answers and explanations in real time. The device then presents the answers obtained from the generative AI to the user visually or audibly.

[1416] Step 7: Project-Based Learning

[1417] Users begin practical projects in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, a generative AI system provides a script template for analysis.

[1418] Step 8: Team collaboration and sharing

[1419] Users share parts of a project with other learners and work together on tasks. For example, a user may collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users (such as chat rooms and shared document functions). The device displays the collaboration status in real time, helping to ensure the collaboration progresses smoothly.

[1420] Step 9: Dialogue and feedback

[1421] The user interacts with other learners and AI assistants to resolve any questions. For example, the user may ask other learners for feedback on their progress. The server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction to enhance the learning experience. For example, comments from other learners and feedback from AI are displayed on the screen.

[1422] Step 10: Ending the training

[1423] When the user has completed their learning, they can click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report, including, for example, the project's evaluation score and areas for improvement.

[1424] Step 11: Present the evaluation results

[1425] The device visually presents the evaluation results to the user and displays the next learning steps and recommended courses. For example, the device displays the evaluation report and a list of next courses on the screen. This allows the user to check their learning results and plan their next learning.

[1426] Through the above steps, the educational platform of the present invention provides users with an efficient and effective learning environment.

[1427] Example 1

[1428] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1429] In conventional online education systems, learners often receive knowledge one-way, limiting the ability to acquire practical skills. Furthermore, there is no mechanism for responding to users' questions in real time, which can impede learning progress. Furthermore, limited opportunities for cooperation and communication between learners make it difficult to progress with collaborative learning tasks or projects.

[1430] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1431] In this invention, the server includes: means for authenticating user input information and acquiring the user's learning history and setting information; means for acquiring detailed information about learning themes and courses based on the user's selections and preparing a learning environment in a virtual reality space; means for a generative artificial intelligence (AI) to provide appropriate answers and explanations to the user's questions; means for monitoring the user's progress in real time and providing feedback from the AI; means for the user to carry out a practical project in the virtual reality space and provide necessary data sets and tools while monitoring the progress; means for the AI ​​to provide an analysis script template according to the project progress; and means for conducting a comprehensive evaluation based on the user's learning progress and test results, generating and presenting an evaluation report. This enables real-time feedback and question resolution, improving learners' understanding and enabling them to effectively acquire practical skills. It also promotes cooperation among learners, facilitating smooth collaborative work.

[1432] "User" refers to an individual who uses the system to study or carry out a project.

[1433] "Input information" refers to data including authentication information and setting information that a user provides to the system.

[1434] "Learning history" refers to historical data such as the courses a user has taken on the system and their progress.

[1435] "Setting information" refers to setting data such as the theme, layout, and language selected by the user for learning in the virtual reality space.

[1436] "Virtual reality space" refers to a three-dimensional virtual environment generated by a computer.

[1437] "Generative artificial intelligence" refers to an AI model that generates appropriate answers and explanations in response to user questions and requests.

[1438] "Detailed information" refers to information about the selected study topic or course, such as the curriculum, required materials, and objectives.

[1439] A "project" refers to a practical assignment or set of tasks that a user completes in a virtual reality space.

[1440] "Progress" refers to the user's achievements and current status as they progress through their studies or projects.

[1441] A "dataset" refers to a collection of data required to carry out a project or task.

[1442] "Tools" refers to software or features within a virtual environment that users use to advance their learning or projects.

[1443] "Script templates" refer to template code or programs provided by generative artificial intelligence as the project progresses.

[1444] "Comprehensive evaluation" refers to an overall evaluation based on the user's learning progress and test results.

[1445] "Evaluation report" refers to a report summarizing the results of the comprehensive evaluation.

[1446] "Voice input" refers to voice instructions or questions given by the user to the system through a microphone.

[1447] "Chat function" refers to a function that allows a user to enter text and interact with the system and other users.

[1448] The present invention provides a system that provides an educational platform that integrates generative artificial intelligence and virtual reality space, allowing users to learn in a virtual reality space while engaging in interactive dialogue using generative artificial intelligence, thereby efficiently acquiring practical skills.

[1449] Initial Setup

[1450] The user enters their account information (username and password) and clicks the login button. The server receives the entered account information and verifies the authentication information from a database. If authentication is successful, the server obtains the user's learning history and setting information and stores it in the session. This process often uses a common authentication system or database management system. For example, MySQL or PostgreSQL can be used for database management.

[1451] The device then initializes the virtual reality space based on the user's settings (e.g., theme, layout, language, etc.). Rather than choosing a fixed layout or theme, virtual reality development platforms such as Unity and Unreal Engine are used to provide dynamically customizable content.

[1452] Select learning content

[1453] Users select the topic they want to learn from a list of learning themes and courses offered on the platform. For example, a user selects a course such as "Python programming" or "data science." The server obtains detailed information about the selected course (curriculum, required materials, goals, etc.) and sends it to the generative AI model. The server then receives the necessary learning materials from the generative AI model, formats the data, and sends it to the device. This allows users to easily access the materials they need.

[1454] Start learning

[1455] The user clicks a button on the interface within the virtual reality space to begin learning. The server monitors the user's progress in real time and provides feedback with instructions from the generative AI model. For example, when the user uses the "print function," the generative AI model explains how to use it. This allows the user to receive real-time feedback and instantly understand the learning content.

[1456] If a user has questions or concerns, they can use the voice input or chat function to ask the virtual assistant. For example, a user might ask, "I don't know how to use a loop in Python." The server analyzes the user's question through a generative AI model and provides appropriate answers and explanations in real time, allowing users to quickly resolve problems they encounter during their learning.

[1457] Practical project implementation

[1458] Users begin a practical project in a virtual reality space and follow instructions to complete each task. For example, a user might start a data analysis project and preprocess a dataset. The server tracks the project's progress and automatically provides the necessary datasets and tools. Once data preprocessing is complete, the generative AI model provides a script template for analysis. In this way, users can learn independently while receiving support as they progress through the project.

[1459] Users can also share parts of projects with other learners and work together on tasks. For example, a user can collaborate with other learners to clean a dataset. The server provides an environment that facilitates sharing and communication between users. Specifically, it is possible to use chat rooms and shared document functions. This allows users to collaborate in real time and effectively advance their learning.

[1460] Communication and Feedback

[1461] Users can interact with other learners and AI assistants to resolve their questions. For example, if a user requests feedback from other learners about their progress, the server analyzes the interaction and provides additional information and resources to deepen the user's understanding. The device visually displays the feedback and interaction, enhancing the learning experience. Specifically, comments from other learners and feedback from AI could be displayed on the screen.

[1462] Termination and Evaluation

[1463] When the user has completed their learning, they click the finish button and choose to take a self-assessment test. The server will then conduct a comprehensive evaluation based on the user's learning history and test results, and generate a report. This may include a report that includes the project's evaluation scores and areas for improvement. This report will serve as a reflection on the user's learning and can be used to plan their next learning.

[1464] The device will visually present the assessment results to the user and display next learning steps or recommended courses. Specifically, the device could display an assessment report and a list of next courses on the screen, allowing users to review their learning outcomes and plan their next steps.

[1465] Examples of concrete examples and prompts

[1466] For example, if a user logs into a virtual reality course on "Python Programming," they could use the following prompt:

[1467] "How do I use a for loop in Python?"

[1468] "What's the next step in your data science project?"

[1469] Please give me a concrete example of how to use the print function.

[1470] In this way, the educational platform of the present invention combines generative artificial intelligence and virtual reality space to provide an efficient and effective learning environment and assist users in acquiring skills.

[1471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1472] Step 1:

[1473] The user accesses the platform's login screen. The user enters their username and password and clicks the login button. Input: Username and password, Output: Login request. The server receives the entered information, retrieves authentication information from the database, and performs verification. Input: Login request, Output: Authentication result. If authentication is successful, the server retrieves the user's learning history and setting information and saves it in the session. Output: Learning history and setting information.

[1474] Step 2:

[1475] The device initializes the virtual reality space based on the user's setting information received from the server. Input: Learning history and setting information, Output: Initialization settings of the virtual reality space. The device customizes the virtual space based on the user's setting information (e.g., theme, layout, language used, etc.) and arranges interfaces such as menus, dashboards, and avatars. Specifically, the virtual reality environment is constructed using Unity or Unreal Engine. Output: Customized virtual reality space.

[1476] Step 3:

[1477] The user selects the topic or course they wish to study from the menu. Input: User's course selection, Output: Information about the selected topic or course. The server obtains detailed information about the selected topic and sends it to the generative AI model. Input: Detailed information about the selected topic, Output: Request to generate learning materials. The generative AI model generates the necessary learning materials and sends them back to the server. Input: Generation request, Output: Learning materials. The server sends the formatted data to the terminal, which displays the learning materials on the user's screen. Output: Learning materials displayed to the user.

[1478] Step 4:

[1479] The user clicks a button to begin learning. Input: User's instruction to start, Output: Request to start learning. The server monitors the user's progress in real time and provides feedback with instructions from the generative AI model. Input: User's progress data, Output: Feedback request to the generative AI model. For example, when the user uses the "print function," the generative AI model explains how to use it specifically. Output: Specific feedback to the user.

[1480] Step 5:

[1481] When a user has a question or concern, they use voice input or chat functionality to ask the virtual assistant a question. Input: User's question (voice or text), Output: Question request. The server sends this question to the generative AI model and obtains an appropriate answer. Input: Question request, Output: Answer from the generative AI model. The device presents this answer to the user visually or audibly. Output: Display of answer to user.

[1482] Step 6:

[1483] The user starts a practical project in the virtual reality space, and the server tracks its progress. Input: Instruction to start the project, Output: Collection of project progress data. The server automatically provides the necessary datasets and tools. For example, once data preprocessing is complete, the generative AI model provides a script template for analysis. Input: Current progress data, Output: Analysis script template.

[1484] Step 7:

[1485] Users work together with other learners in a virtual reality space to complete tasks. Input: Start of collaboration, Output: Communication data for task allocation. The server supports communication between users and provides chat room and shared document functions. Input: Collaboration request, Output: Access information for chat rooms and shared documents. The terminal displays the progress of the collaboration in real time and updates the information. Output: Collaboration information updated in real time.

[1486] Step 8:

[1487] When learning is complete, the user clicks the finish button. Input: Finish instruction, Output: Learning end request. The server performs a comprehensive evaluation based on the user's learning history and test results, and generates an evaluation report. Input: Learning history and test results, Output: Evaluation report. The terminal visually presents the evaluation results to the user and displays the next learning step and recommended courses. Output: Display of evaluation results and recommended courses.

[1488] (Application example 1)

[1489] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1490] Traditional educational platforms tend to be static and have limited interactive content, making it difficult to personalize content based on users' interests and level of understanding. This limits their effectiveness, particularly in areas such as entertainment and practical skills. Furthermore, the lack of real-time, interactive feedback makes it difficult to improve users' learning efficiency. Furthermore, the lack of mechanisms to encourage collaboration with other learners makes it difficult for learners to interact with each other or collaborate.

[1491] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1492] In this invention, the server includes: means for authenticating user input information and acquiring the user's learning history and setting information; means for acquiring detailed information about learning themes and courses based on the user's selection and preparing a learning environment in a virtual reality space; means for a generative artificial intelligence to provide appropriate answers and explanations to the user's questions; means for the user to carry out practical projects in the virtual reality space and provide necessary data sets and tools while monitoring their progress; means for conducting a comprehensive evaluation based on the user's learning progress and test results and generating and presenting an evaluation report; means for the user to learn entertainment content or acquire production skills based on their knowledge and skills; means for interactively responding to user input through the generative artificial intelligence and providing real-time responses via video and audio; and means for providing personalized educational content and entertainment experiences based on the user's interests. This not only significantly improves the user's learning efficiency but also enables them to acquire practical skills and knowledge in the entertainment field. Furthermore, it promotes cooperation and interaction with other learners, enhancing the learning experience through collaborative work.

[1493] "Generative AI" is an AI technology that generates new information and answers based on user input.

[1494] A "virtual reality space" is a realistic computer-generated virtual environment that a user experiences using a dedicated device.

[1495] An "educational platform" refers to a system or service provided to help learners acquire specific knowledge or skills.

[1496] "User input information" refers to data such as account information, setting information, questions, and feedback that a user provides to the system.

[1497] "Study history" is data that records what a user has studied in the past and their progress.

[1498] "Setting information" is data including individual settings such as themes, layouts, and languages ​​used that are specified by the user for the system.

[1499] "Study topic" refers to the content or field that a user is studying, such as programming or data science.

[1500] "Course details" refers to information including a curriculum based on a specific learning theme, required materials, learning objectives, etc.

[1501] "Learning environment" refers to the entire setup, including consoles, holograms, and interfaces, required for users to learn within a virtual reality space.

[1502] "Interactive response" refers to generative AI responding immediately to real-time questions and requests from users and returning appropriate information and instructions.

[1503] A "hands-on project" is a project in which users learn practical skills through specific tasks and activities in a virtual reality space.

[1504] "Progress" is data that indicates how far a user has progressed in their studies or project tasks.

[1505] A "dataset" is a collection of data needed to solve a specific problem.

[1506] A "tool" is software or equipment that a user uses to accomplish a specific task.

[1507] An "evaluation report" is a report summarizing the results of a comprehensive evaluation based on the user's learning progress and test results.

[1508] "Entertainment content" refers to content that provides users with fun and excitement, and includes games, movies, music videos, and the like.

[1509] "Personalized educational content" refers to learning materials that are optimized to suit the interests and learning styles of individual users.

[1510] An "entertainment experience" is a fun, engaging, interactive activity that users experience through virtual reality spaces and generative artificial intelligence.

[1511] This invention provides an educational platform that integrates generative artificial intelligence and virtual reality space. This system allows users to learn in a virtual reality space (hereinafter referred to as VR space) while engaging in interactive dialogue using generative artificial intelligence (hereinafter referred to as AI), allowing them to efficiently acquire practical skills. Specific embodiments of this invention are described below.

[1512] Hardware and software used

[1513] Hardware: Smartphones, smart glasses, head-mounted displays (HMDs), computer servers

[1514] Software: VR platform software, generative artificial intelligence module, user database management system

[1515] User Login

[1516] The user logs in to the authentication server by entering their account information. The server checks the entered information against the user database, and if authentication is successful, it acquires the user's learning history and setting information and stores them in the session.

[1517] Initializing the VR space

[1518] Based on the configuration information obtained from the server, the device initializes the VR space. During this process, a unique theme and layout are set and an interface is created in the user's language of choice. This allows the user to begin learning in a customized VR environment complete with various menus, dashboards, avatars, and more.

[1519] Select and view learning content

[1520] The user selects from a list of learning themes and courses provided. For example, they can choose "Python Programming" or "Film Production Technology." The server sends detailed information about the selected course (curriculum, required materials, goals, etc.) to the generative AI module, which retrieves the necessary learning materials. The device displays the learning content in the VR space based on the retrieved materials and environment settings. For example, if the user selects a programming course, a code editor and virtual console will appear in the VR space.

[1521] Interactive Dialogue

[1522] When a user has a question while studying, they can ask the AI ​​using voice input or the chat function. The server analyzes the user's question using generative AI and provides appropriate answers and explanations in real time. This feedback is presented to the user visually or audibly.

[1523] As a specific example, if a user asks, "Please tell me how to arrange the lighting in this scene," the AI ​​will provide a detailed explanation of the appropriate lighting arrangement techniques and equipment to use.

[1524] Practical project implementation

[1525] Users begin practical projects involving specific tasks and activities within the VR space. The server monitors their progress and automatically provides datasets and tools as needed. For example, if a user is working on a data analysis project, the server will provide analysis script templates once the dataset has been preprocessed.

[1526] Communication and Feedback

[1527] Users can collaborate with other learners to complete collaborative tasks. The server provides an environment that facilitates communication between users, and the device displays the collaboration status in real time. Based on the learning progress and level of understanding, the system provides appropriate feedback and assistance as needed by the user.

[1528] Assessment of learning

[1529] When the user has completed the learning, he / she can choose to take a self-assessment test. The server will conduct a comprehensive evaluation based on the user's learning history and test results, generate an evaluation report and present it to the user. This will help the user check their progress and select the next learning step or recommended course.

[1530] This system not only improves the user's learning efficiency, but also enables them to acquire practical skills and knowledge in the entertainment field, thereby enriching the user's learning experience.

[1531] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1532] Step 1:

[1533] The user logs in by entering account information (username and password).

[1534] Input: Username and Password.

[1535] Data processing: The server receives the user's input information and compares it with the user database.

[1536] Output: Authentication result (success / failure), user learning history and setting information when authentication is successful.

[1537] Step 2:

[1538] The server authenticates the user's learning history and setting information and stores it in the session.

[1539] Input: User learning history and setting information upon successful authentication.

[1540] Data processing: Convert learning history and setting information into session data.

[1541] Output: The user session is initialized and the learning history and settings are saved.

[1542] Step 3:

[1543] The device initializes the VR space based on the acquired setting information.

[1544] Input: User settings information (theme, layout, language).

[1545] Data calculation: The VR platform analyzes the setting information and constructs a VR space based on it.

[1546] Output: A customized VR space (menus, dashboards, avatars, etc.).

[1547] Step 4:

[1548] The user selects a learning topic from a list of learning topics and courses provided.

[1549] Input: Study topic, course list, user selection.

[1550] Data processing: The server retrieves detailed information about the selected course (curriculum, required materials, objectives).

[1551] Output: Detailed information about the selected course of study...

Claims

1. An educational platform in a virtual reality space using generative artificial intelligence, means for authenticating user input information and acquiring user learning history and setting information; A means for acquiring detailed information about a learning topic or course based on a user's selection and preparing a learning environment in a virtual reality space; A means for generative AI to provide appropriate answers and explanations to users' questions, A means for users to carry out practical projects in a virtual reality space, monitoring progress and providing the necessary datasets and tools; A system including a means for conducting a comprehensive evaluation based on a user's learning progress and test results, and generating and presenting an evaluation report.

2. The system of claim 1 , further comprising means for allowing a user to collaborate with other learners in the virtual reality space to jointly complete a learning task.

3. The system of claim 1 further comprising means for providing a voice input or chat function in which the generative artificial intelligence responds interactively to a user's doubts or questions in real time.

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