System

Abstract

A system is provided.SOLUTION: This system includes a means for checking the academic ability of a student, a means for automatically generating a proper curriculum on the basis of a check result, a means for generating learning contents based on the curriculum, a means for providing the learning contents to the student in an optimum form, a means for managing the learning progress and achievement degree of the student, a means for performing necessary correction according to the learning progress, and a learning support means using a virtual teacher in a metaverse.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] Modern educational institutions face major challenges, including teacher shortages and educational disparities, particularly unequal educational opportunities due to differences in urban and rural areas and household income. Furthermore, the increasing burden on teachers means that emotional education and the development of human qualities are not being adequately addressed. In these circumstances, there is a need to provide optimal learning support for each individual student and achieve efficient, high-quality education. [Means for solving the problem]

[0005] We provide an autonomous learning system that utilizes generative AI and big data. This system has a means for checking students' academic ability and a means for automatically generating an appropriate curriculum based on the check results. It also includes a means for generating learning content based on the curriculum and providing it to students in an optimal format, as well as a means for managing learning progress and achievement and making necessary corrections based on progress. Furthermore, it also includes a means for supporting learning using virtual tutors within the metaverse, a means for providing animated learning content for students who are weak at writing, and a means for providing customizable virtual tutors for a fee. These means can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers.

[0006] A "student achievement check instrument" is a combination of software and hardware used to assess a student's current academic achievement.

[0007] The "means for automatically generating an appropriate curriculum based on the check results" is a system that analyzes the results of academic ability checks and automatically generates the most suitable learning plan for each student based on an algorithm.

[0008] A "means for generating curriculum-based learning content" is a system that generates educational content such as necessary learning materials, workbooks, and videos according to an automatically generated curriculum.

[0009] "Means for providing learning content to students in an optimal form" refers to the interface and delivery means for providing the generated learning content in a format that is easy for students to use.

[0010] "Means for managing student learning progress and achievement" refers to a system for tracking learning progress and achievement in real time and recording and managing it in a database.

[0011] The "means of making necessary adjustments according to progress" is a system that analyzes students' learning progress data and modifies and adjusts the curriculum and learning content as necessary.

[0012] "Learning support using virtual teachers in the metaverse" is a system that uses VR and AR technology to provide learning guidance using an avatar that acts as a teacher in a virtual space.

[0013] "A means of providing learning content in animated format to students who are weak at reading" is a system that provides learning content in a visually easy-to-understand animated format to students who have difficulty understanding written text.

[0014] A "fee-based, customizable virtual teacher provisioning means" is a service and system for providing, for a fee, customized virtual teachers according to the specific needs of students and teachers. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. The invention consists of the following main functions and steps.

[0037] Academic ability check function

[0038] First, the server loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user. The user (student) answers these questions and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic achievement.

[0039] (Example)

[0040] Students answer math problems on their devices, and the answers are sent to the server, which then determines whether the answers are correct and evaluates the student's level of math comprehension.

[0041] Personalize your learning content

[0042] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The device displays the generated content to the user, thereby providing optimal learning content for each student.

[0043] (Example)

[0044] The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[0045] Achievement and progress management

[0046] The device records the user's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[0047] (Example)

[0048] The device records the student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[0049] Anime-style content

[0050] The server generates learning content in the form of animation for students who are not good at reading texts, and the device displays this content to the user, allowing them to progress through their studies in a visually easy-to-understand format.

[0051] (Example)

[0052] The server generates animated content for multiplication for students who have difficulty understanding letters and sends it to the device, which displays it and allows students to learn by watching the animation.

[0053] The Metaverse and Virtual Teachers

[0054] The server generates a virtual learning environment in the metaverse and operates a virtual teacher for each user. The user (student) wears VR glasses, logs into the metaverse, and interacts with the virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[0055] (Example)

[0056] Students wear VR glasses and participate in a virtual class in the metaverse, while the server controls a virtual teacher who answers students' questions and provides instruction.

[0057] Paid services

[0058] The server manages the provision of paid customizable virtual tutor services and presents additional content and customization options to users as needed. Users select these options to receive additional services.

[0059] (Example)

[0060] The user selects a customization option for the virtual teacher and purchases additional content for a fee. The server generates this customization data and transmits it to the terminal.

[0061] The specific embodiment of the present invention has been described above. This system can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers.

[0062] The processing flow will be explained below.

[0063] Academic ability check function

[0064] Step 1:

[0065] The server loads student profile information and past academic performance data from a database.

[0066] Specific operation: Using an SQL query, retrieve student data corresponding to the user ID from the database.

[0067] Step 2:

[0068] The terminal displays the academic ability check questions received from the server to the user.

[0069] Specific operation: Renders problem data obtained from the server via an HTTP request on the screen using HTML and CSS.

[0070] Step 3:

[0071] The user (student) answers the academic ability check questions displayed on the terminal.

[0072] Specific actions: Enter answers using the form and click the submit button.

[0073] Step 4:

[0074] The terminal transmits the user's response data to the server.

[0075] Specific behavior: Uses JavaScript to convert form data into JSON format and sends it to the server via a POST request.

[0076] Step 5:

[0077] The server analyzes the received response data and evaluates the student's academic ability.

[0078] Specific operation: Calculates the accuracy rate and answer speed using scripts such as Python and Java, and generates an academic ability evaluation score.

[0079] Personalize your learning content

[0080] Step 1:

[0081] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[0082] Specific actions: Utilizing generative AI to formulate learning items and sequences and build a curriculum.

[0083] Step 2:

[0084] The server generates learning content based on the generated curriculum.

[0085] Specific operation: Generates multimedia content such as text, images, and videos and sends them to the device via API.

[0086] Step 3:

[0087] The terminal displays the study content sent from the server to the user.

[0088] What it does: Parses JSON data and renders it into UI components with HTML and CSS.

[0089] Achievement and progress management

[0090] Step 1:

[0091] The device records the user's learning progress and achievement in real time.

[0092] What it does: Uses JavaScript to track actions and saves the data to a real-time database.

[0093] Step 2:

[0094] The terminal transmits the recorded progress data to the server at regular intervals.

[0095] Specific operation: Using Ajax, progress data is sent to the server in batch processing.

[0096] Step 3:

[0097] The server analyzes the collected progress data and suggests next learning steps and necessary corrections.

[0098] What it does: It uses machine learning algorithms to analyze data and generate feedback scores and suggestions.

[0099] Step 4:

[0100] The terminal displays the feedback content received from the server to the user.

[0101] Specific behavior: Display the feedback data in the UI and notify the user using popups or notifications.

[0102] Anime-style content

[0103] Step 1:

[0104] The server generates animated learning content based on student profiles and academic data.

[0105] Specific operation: Use the animation generation engine to create animation content based on a scenario.

[0106] Step 2:

[0107] The server transmits the generated animation content to the terminal.

[0108] Specific operation: Upload the generated animation file to the content management system and send a link to the device.

[0109] Step 3:

[0110] The terminal displays the content in the form of an animation to the user.

[0111] Specific operation: Play the anime file using a video player.

[0112] The Metaverse and Virtual Teachers

[0113] Step 1:

[0114] The server generates a learning environment within the metaverse and customizes it for each user.

[0115] Specific actions: Perform 3D modeling and scene setup to prepare a personalized learning environment for each user.

[0116] Step 2:

[0117] Users (students) put on VR glasses and log in to the metaverse.

[0118] Specific operation: Enter authentication information and launch the VR application.

[0119] Step 3:

[0120] The server operates the virtual teacher and interacts with the user.

[0121] Specific behavior: Executes the avatar teacher script, provides instruction to students, and answers questions.

[0122] Step 4:

[0123] The device handles interactions within the metaverse and provides appropriate feedback to the user.

[0124] Specific Actions: Collect and analyze data in real time and take appropriate action within the 3D environment.

[0125] Paid services

[0126] Step 1:

[0127] The server presents the user with paid service options.

[0128] Specific operations: Generates information about fee plans and additional features for paid services and sends it to the device.

[0129] Step 2:

[0130] The user selects a paid service as needed and receives customization and educational materials provided by the server.

[0131] What it does: Complete the online subscription process and activate the selected services.

[0132] Step 3:

[0133] The terminal displays the selected paid service to the user.

[0134] Specific behavior: Displays access links and download options for paid content.

[0135] In this way, by explaining the specific operations at each processing step, the processing flow of the program for the autonomous learning service can be explained in detail.

[0136] Example 1

[0137] 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."

[0138] Traditional educational systems have struggled to provide personalized learning tailored to each student's learning progress and level of understanding. They also lacked appropriate learning support for students who struggled with writing, and real-time learning support in virtual environments was limited. Furthermore, the lack of real-time recording of learning progress and prompt, appropriate feedback led to problems that reduced learning effectiveness.

[0139] 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.

[0140] In this invention, the server includes a means for checking students' academic ability, a means for automatically generating an appropriate curriculum based on the check results, and a means for generating learning content based on the curriculum. This enables personalized learning based on each student's level of understanding. The server also includes a means for generating academic ability check questions using an AI model, a means for recording learning progress data in real time using a terminal, a means for providing learning content in animation format, and a means for accessing a virtual learning environment using a VR device. This enables appropriate learning support for students who are weak at writing and real-time learning support in a virtual environment, as well as real-time recording of learning progress and rapid feedback.

[0141] A "means for checking students' academic ability" is a system that measures students' academic ability by having students input answers and evaluating those answers.

[0142] The "means for automatically generating an appropriate curriculum based on the check results" is a system that analyzes the results of students' academic ability checks and generates the optimal learning plan for each individual student based on that.

[0143] The "means for generating curriculum-based learning content" is a system that creates specific learning materials and problem sets based on an automatically generated curriculum.

[0144] "Means to provide learning content in the most optimal form to students" refers to a system that displays and delivers learning content in accordance with each student's learning style and progress.

[0145] "Means for managing students' learning progress and achievement" refers to a system that records and analyzes students' learning progress and achievement in real time.

[0146] "Means for making necessary corrections according to learning progress" refers to a system that provides necessary adjustments and advice based on students' learning data.

[0147] "Learning support means using a virtual teacher in the metaverse" is a system in which a virtual teacher provides guidance and support to students in a virtual reality space.

[0148] "Means for generating academic ability check questions using an AI model" refers to a system that uses artificial intelligence to automatically generate questions to measure students' academic ability.

[0149] "Means for recording learning progress data in real time using a terminal" refers to a system that records learning progress in real time via the device used by the student.

[0150] "Means for providing learning content in animation format" refers to a system that provides learning content using animations that are visually easy for students to understand.

[0151] "Means for accessing a virtual learning environment using a VR device" refers to a system that uses a virtual reality device to access a virtual learning space and conduct learning there.

[0152] MODE FOR CARRYING OUT THE INVENTION

[0153] This invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. The following describes a specific implementation of this system.

[0154] Hardware and software used

[0155] 1. Server: Use the following cloud platforms:

[0156] Amazon Web Services (AWS)

[0157] Microsoft Azure

[0158] 2. Devices: Use the following devices and software:

[0159] Tablet devices, PCs

[0160] Chrome browser

[0161] 3. Database: The following database management systems are used:

[0162] MySQL

[0163] PostgreSQL

[0164] 4. AI model: The following generative AI model is used:

[0165] OpenAI GPT-4

[0166] 5. VR Devices: Use the following virtual reality devices:

[0167] Oculus Rift

[0168] HTC Vive

[0169] Program processing

[0170] Academic ability check function

[0171] The server loads the student's profile information and past academic performance data from the database. Specifically, it retrieves the student's name, age, past grades, etc. from the MySQL database. The server then uses the generative AI model to generate academic performance check questions and sends them to the device. For example, the following prompt sentence is input to the AI ​​model:

[0172] Generate five math problems appropriate for your students' ages.

[0173] The device displays academic ability check questions to the user. For example, a math problem is displayed on a tablet screen. The user (student) answers the question, for example, by entering "2 + 2 = 4." The device then sends the user's answer to the server. The server analyzes the answer data and evaluates the student's academic ability. The analysis method is to calculate the percentage of correct answers and quantify the level of understanding.

[0174] Personalize your learning content

[0175] The server automatically generates an appropriate curriculum based on the results of the academic ability check. This process uses a generative AI model to determine the "next thing to learn." The server then generates learning content based on the automatically generated curriculum and sends it to the device. The generated content can be in the form of a PDF file or video link. The device then displays the personalized learning content to the user.

[0176] Specifically, the generative AI model is fed with the following prompt sentence:

[0177] Generate practice content for math problems based on students' understanding.

[0178] Achievement and progress management

[0179] The device records the user's learning progress data in real time. For example, it records how long it took to solve each problem. The recorded data is sent to the server at the end of the learning session. The server analyzes the progress data and suggests the next learning step. For example, it may suggest, "Try a slightly more difficult problem."

[0180] A concrete example is the following prompt:

[0181] This week's study time was a total of 10 hours. Let's do our best next week!

[0182] Anime-style content

[0183] The server generates learning content in the form of animation for students who are not good at reading. The generated animation content includes video file formats and streaming links, and is sent to the terminal. The terminal displays this content to the user.

[0184] For example, use the following prompt:

[0185] Create an animation that visually explains the concept of multiplication.

[0186] The Metaverse and Virtual Teachers

[0187] The server generates a virtual learning environment in the metaverse, and users (students) log in wearing VR devices. The server operates the virtual teacher, and the device processes this interaction and provides appropriate feedback to the user. Learning support is provided in real time using the VR device.

[0188] Examples of prompt statements include:

[0189] Put on your VR glasses and move on to the next virtual experiment.

[0190] Paid services

[0191] The server presents paid customization options, and users (students and parents) select and purchase additional services. For example, payment can be made by credit card. The server generates customized additional content and sends it to the device.

[0192] A concrete example is the following prompt:

[0193] Use our paid service to get specialized instruction on applied math problems.

[0194] As described above, this system enables personalized learning based on each student's level of understanding, contributing to improving the quality of education.

[0195] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0196] Step 1:

[0197] The server loads student profile information and past academic performance data. Specifically, it retrieves information such as student name, age, and past grades from a MySQL database. The input is student identification information such as ID, and the output is this data. Data processing involves extracting the necessary information using database queries and formatting it into a structured data format.

[0198] Step 2:

[0199] The server uses a generative AI model to generate academic ability check questions. The input is information such as the student's age and academic level, and the output is the generated academic ability check questions. To process the data, the following prompt is input to the AI ​​model: "Generate five arithmetic problems appropriate for the student's age."

[0200] Step 3:

[0201] The server sends the generated academic ability check questions to the terminal. The input is the generated question data, and the output is the sent question data. For data processing, the question data is serialized in JSON format or similar and sent to the terminal over the network.

[0202] Step 4:

[0203] The terminal displays the academic ability check questions received from the server to the user. The input is the received question data, and the output is the question displayed on the user interface. Specifically, the terminal deserializes the question data and displays it on the screen.

[0204] Step 5:

[0205] The user answers the academic ability check questions. The input is the displayed question, and the output is the user's answer data. In concrete terms, the user inputs the answer on the terminal.

[0206] Step 6:

[0207] The terminal sends the user's answer to the server. The input is the user's answer data, and the output is the answer data to be sent to the server. For data processing, the answer data is serialized in JSON format or similar and sent to the server via the network.

[0208] Step 7:

[0209] The server analyzes the response data and evaluates the student's academic ability. The input is the user's response data, and the output is the academic ability evaluation result. Data processing involves determining whether the answer is correct or incorrect, and quantifying the accuracy rate and level of understanding.

[0210] Step 8:

[0211] The server automatically generates an appropriate curriculum based on the results of the academic ability check. The input is the academic ability assessment results, and the output is the generated curriculum. Data processing uses an AI model to determine what should be learned next.

[0212] Step 9:

[0213] The server generates learning content based on the generated curriculum and sends it to the terminal. The input is the curriculum data, and the output is the transmitted learning content. Data processing involves converting the learning content into PDF files, video links, etc., and sending them over the network.

[0214] Step 10:

[0215] The terminal displays personalized learning content to the user. The input is the transmitted learning content, and the output is the content displayed on the user interface. Specifically, the terminal deserializes the content into a display format and displays it.

[0216] Step 11:

[0217] The device records the user's learning progress data in real time. The input is the user's learning behavior, and the output is the recorded progress data. Specific operations include recording the start and end times of learning and the time spent on each problem.

[0218] Step 12:

[0219] The device sends the recorded progress data to the server. The input is the progress data, and the output is the data to be sent to the server. The data is processed by serializing the progress data in JSON format or similar and sending it over the network.

[0220] Step 13:

[0221] The server analyzes the progress data and proposes the next learning step. The input is the progress data and the output is the proposal. Data processing involves analyzing the learning progress and recommending what to learn next.

[0222] Step 14:

[0223] The server sends the proposal to the terminal. The input is the proposal, and the output is the data sent to the terminal. The proposal is serialized and sent over the network as data processing.

[0224] Step 15:

[0225] The terminal displays the suggestion to the user. The input is the suggestion, and the output is the suggestion displayed on the user interface. Specifically, the suggestion is deserialized and displayed on the screen.

[0226] (Application example 1)

[0227] 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."

[0228] In today's educational environment, it is difficult to use a uniform teaching method to accommodate students with diverse learning styles and levels of understanding, which can lead to a decline in educational effectiveness and a decrease in motivation to learn. Furthermore, providing optimal learning content to each student is difficult to do manually, increasing the burden on teachers. Furthermore, with the spread of distance education, there is a demand for methods to provide learning support at the same level as face-to-face classes.

[0229] 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.

[0230] In this invention, the server includes means for checking students' academic abilities, means for automatically generating an appropriate curriculum based on the check results, means for generating learning content based on the curriculum, means for providing the learning content in an optimal form to the student via a smartphone application, means for managing learning progress and achievement, means for making necessary corrections according to the learning progress, means for learning support using a virtual teacher in the metaverse, and means for providing real-time interaction with the virtual teacher. This provides an optimized learning experience for each student, improves the quality of education, reduces the burden on teachers, and makes it possible to provide support equivalent to face-to-face classes in a distance learning environment.

[0231] "Student" refers to a person engaged in a learning activity.

[0232] "Academic ability" is an indicator of the degree of acquisition of knowledge and skills in a particular academic field.

[0233] "Checking" refers to the act of evaluating and confirming academic ability, progress, and other status.

[0234] "Curriculum" refers to the entire educational content organized to achieve specific educational objectives.

[0235] "Automatic generation" refers to the process in which the system autonomously generates the necessary data and information without the need for manual operation.

[0236] "Learning content" refers to a collection of instructional materials or information provided to achieve a specific learning goal.

[0237] "Providing" refers to the act of distributing or supplying learning materials, services, etc. to learners.

[0238] "Progress" is a state that indicates how much progress has been made in learning activities.

[0239] "Degree of achievement" refers to the level that indicates the degree to which a set learning goal has been achieved.

[0240] "Management" refers to monitoring and controlling the status of progress and achievement, and taking appropriate measures.

[0241] "Correction" refers to correcting or improving an error or deficiency.

[0242] The "metaverse" refers to a virtual space built on the Internet where users can interact through their own avatars.

[0243] A "virtual teacher" refers to an avatar or AI system that teaches students in a virtual space in place of a real teacher.

[0244] "Smartphone application" refers to a software program that runs on a smartphone.

[0245] "Real-time" refers to a state in which a particular operation or process is performed immediately without delay.

[0246] "Interaction" refers to the flow of mutual communication and operations between a user and a system or avatar.

[0247] This invention is an educational support system that utilizes generative AI and big data, and is realized through a smartphone application, a server, and a metaverse environment. Specific embodiments of the system are described below.

[0248] Hardware and Software Configuration

[0249] Server: A high-performance server is used to manage user data, analyze academic ability, and create an automated curriculum using generative AI models. This server is a system equipped with a database, AI models, and APIs.

[0250] Smartphone: Functions as an interface for learners (users). A dedicated application is installed on the smartphone, and users use it to check their academic ability, access learning content, and manage their progress.

[0251] Metaverse environment: A system for providing learning support in a virtual space, allowing real-time interaction with a virtual teacher. This environment is accessed via VR devices.

[0252] System Operation

[0253] 1. Academic ability check

[0254] The server generates questions for an academic ability check based on the user's profile information and past academic ability data. The smartphone application presents these questions to the user and sends the user's answers to the server. The server analyzes the answer data and evaluates the user's academic ability. The evaluation results are used in the next step.

[0255] 2. Providing personalized learning content

[0256] Based on the results of the academic ability check, the server uses a generative AI model to automatically generate an optimal curriculum for the user. Based on that curriculum, specific learning content is created and sent to a smartphone application. The user can then use this content to progress through their studies via the application.

[0257] 3. Tracking learning progress and achievement

[0258] The smartphone application records the user's learning progress and achievement in real time. This data is periodically sent to the server, which analyzes it and suggests necessary corrections and next learning steps. The user receives feedback and continues learning.

[0259] 4. Learning Support in the Metaverse

[0260] The server operates a virtual teacher in the metaverse environment and realizes real-time interaction with the user. The user can log in to the metaverse using a VR device and receive guidance and answers to questions from the virtual teacher.

[0261] 5. Provision of paid services

[0262] The server also provides users with customization options and additional content for a fee, allowing them to customize their virtual teacher. This data is sent to a smartphone application, allowing users to enhance their learning with additional, specialized content.

[0263] Examples and prompts

[0264] For example, there is a system in which a user with user ID "12345" takes an academic ability check on their smartphone, sends the results to a server, and receives learning content appropriate for the user. The following is a prompt for this specific example system:

[0265] Please write Python code to have the user with user ID "12345" take an academic ability check on their smartphone, send the results to the server, and receive learning content appropriate for the user.

[0266] By inputting this prompt into a generative AI model, it is expected that appropriate code will be generated and used as part of the system.

[0267] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0268] Step 1:

[0269] The server loads the user's profile information and past academic achievement data from a database.

[0270] Input: User ID, user profile information in database and past academic achievement data.

[0271] Data manipulation and calculations: Performing database queries to retrieve required information and load user records.

[0272] Output: User profile information and past academic achievement data.

[0273] Step 2:

[0274] Based on the loaded data, the server automatically generates questions for academic ability tests using a generative AI model.

[0275] Input: User profile information and past academic achievement data.

[0276] Data processing and calculation: Generative AI models generate questions appropriate to current academic ability.

[0277] Output: A set of questions for academic ability check.

[0278] Step 3:

[0279] The terminal (smartphone) displays the academic ability test questions received from the server to the user.

[0280] Input: A set of questions for the academic ability check sent from the server.

[0281] Data processing and calculation: Converting received data into a display format and applying it to the user interface.

[0282] Output: Academic ability test questions displayed in the user interface.

[0283] Step 4:

[0284] The user answers the academic ability check questions through the terminal and transmits the answers to the server.

[0285] Input: User response data.

[0286] Data processing and calculation: Collect response data and send it to the server.

[0287] Output: Response data.

[0288] Step 5:

[0289] The server analyzes the received response data and evaluates the user's academic ability.

[0290] Input: User response data.

[0291] Data processing and calculation: Analyze the user's academic ability by applying algorithms to determine whether the answers are correct and to evaluate academic ability.

[0292] Output: Academic assessment results.

[0293] Step 6:

[0294] Based on the academic assessment results, the server uses a generative AI model to automatically generate the optimal curriculum for the user.

[0295] Input: Academic assessment results.

[0296] Data processing and computation: Using generative AI models to create curriculum.

[0297] Output: Personalized curriculum.

[0298] Step 7:

[0299] The server creates specific learning content based on the generated curriculum and transmits it to the terminal.

[0300] Enter: personalized curriculum.

[0301] Data processing and calculation: Generate teaching materials and content corresponding to each learning step.

[0302] Output: Learning content.

[0303] Step 8:

[0304] The terminal displays the learning content received from the server to the user.

[0305] Input: Learning content sent from the server.

[0306] Data processing and calculation: Converting received data into a display format and applying it to the user interface.

[0307] Output: Learning content displayed in a user interface

[0308] Step 9:

[0309] The device records the user's learning progress and achievement in real time and periodically transmits the data to the server.

[0310] Input: User learning activity data.

[0311] Data processing and calculation: Analyzes learning progress and achievement level and sends the data to the server.

[0312] Output: Progress data sent to the server.

[0313] Step 10:

[0314] The server analyzes the collected progress data and suggests corrections to the learning content and the next learning step.

[0315] Input: Learning progress and achievement data.

[0316] Data processing and calculations: Perform calculations based on progress data to determine necessary corrections and next steps.

[0317] Output: Suggested revisions to what you learned and suggested next steps.

[0318] Step 11:

[0319] Users wear VR devices and participate in virtual classes in the metaverse, while the server controls a virtual teacher who interacts with users in real time.

[0320] Input: User login data, Virtual Learning Environment data.

[0321] Data processing and computation: Controlling interactions in the virtual space and responding to user questions.

[0322] Output: A learning experience with a virtual teacher within the metaverse.

[0323] Step 12:

[0324] The server offers customizable virtual teachers and additional content options for a fee.

[0325] Input: User request data, customization options data.

[0326] Data Processing and Computing: Manage data related to paid services and generate customized virtual teachers and content.

[0327] Output: Customized virtual teacher data and additional content.

[0328] 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.

[0329] The present invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the quality of education can be further improved. An embodiment of the present invention is described in detail below.

[0330] Academic ability check function

[0331] First, the server loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user. The user (student) answers these questions and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic achievement.

[0332] (Example)

[0333] Students answer math problems on their devices, and the answers are sent to the server, which then determines whether the answers are correct and evaluates the student's level of math comprehension.

[0334] Personalize your learning content

[0335] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The device displays the generated content to the user, thereby providing optimal learning content for each student.

[0336] (Example)

[0337] The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[0338] Achievement and progress management

[0339] The device records the user's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[0340] (Example)

[0341] The device records the student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[0342] Anime-style content

[0343] The server generates learning content in the form of animation for students who are not good at reading texts, and the device displays this content to the user, allowing them to progress through their studies in a visually easy-to-understand format.

[0344] (Example)

[0345] The server generates animated content for multiplication for students who have difficulty understanding letters and sends it to the device, which displays it and allows students to learn by watching the animation.

[0346] The Metaverse and Virtual Teachers

[0347] The server generates a virtual learning environment in the metaverse and customizes it for each user. Users (students) wear VR glasses, log in to the metaverse, and interact with a virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[0348] (Example)

[0349] Students wear VR glasses and participate in a virtual class in the metaverse, while the server controls a virtual teacher who answers students' questions and provides instruction.

[0350] Emotion engine integration

[0351] The server uses an emotion engine to analyze the user's emotions in real time. The device inputs the user's facial expressions and voice into the emotion engine and sends the analysis results to the server. The server dynamically adjusts the learning content and the behavior of the virtual teacher based on the emotion analysis.

[0352] (Example)

[0353] If a user shows a tired expression while studying, the emotion engine recognizes the emotion as "fatigue" and sends it to the server. Based on this information, the server can provide content that is easier to study or change the behavior of the virtual teacher to offer encouraging words.

[0354] Paid services

[0355] The server manages the provision of paid customizable virtual tutor services and presents additional content and customization options to users as needed. Users select these options to receive additional services.

[0356] (Example)

[0357] The user selects a customization option for the virtual teacher and purchases additional content for a fee. The server generates this customization data and transmits it to the terminal.

[0358] A specific embodiment of the present invention has been described above. This system can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers. Furthermore, the integration of an emotion engine makes it possible to provide a more personalized learning experience.

[0359] The processing flow will be explained below.

[0360] Academic ability check function

[0361] Step 1:

[0362] The server loads student profile information and past academic performance data from a database.

[0363] Specific operation: Using an SQL query, retrieve student data corresponding to the user ID from the database.

[0364] Step 2:

[0365] The terminal displays the academic ability check questions received from the server to the user.

[0366] Specific operation: Renders problem data obtained from the server via an HTTP request on the screen using HTML and CSS.

[0367] Step 3:

[0368] The user (student) answers the academic ability check questions displayed on the terminal.

[0369] Specific actions: Enter answers using the form and click the submit button.

[0370] Step 4:

[0371] The terminal transmits the user's response data to the server.

[0372] Specific behavior: Uses JavaScript to convert form data into JSON format and sends it to the server via a POST request.

[0373] Step 5:

[0374] The server analyzes the received response data and evaluates the student's academic ability.

[0375] Specific operation: Calculates the accuracy rate and answer speed using scripts such as Python and Java, and generates an academic ability evaluation score.

[0376] Personalize your learning content

[0377] Step 1:

[0378] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[0379] Specific actions: Utilizing generative AI to formulate learning items and sequences and build a curriculum.

[0380] Step 2:

[0381] The server generates learning content based on the generated curriculum.

[0382] Specific operation: Generates multimedia content such as text, images, and videos and sends them to the device via API.

[0383] Step 3:

[0384] The terminal displays the study content sent from the server to the user.

[0385] What it does: Parses JSON data and renders it into UI components with HTML and CSS.

[0386] Achievement and progress management

[0387] Step 1:

[0388] The device records the user's learning progress and achievement in real time.

[0389] What it does: Uses JavaScript to track actions and saves the data to a real-time database.

[0390] Step 2:

[0391] The terminal transmits the recorded progress data to the server at regular intervals.

[0392] Specific operation: Using Ajax, progress data is sent to the server in batch processing.

[0393] Step 3:

[0394] The server analyzes the collected progress data and suggests next learning steps and necessary corrections.

[0395] What it does: It uses machine learning algorithms to analyze data and generate feedback scores and suggestions.

[0396] Step 4:

[0397] The terminal displays the feedback content received from the server to the user.

[0398] Specific behavior: Display the feedback data in the UI and notify the user using popups or notifications.

[0399] Anime-style content

[0400] Step 1:

[0401] The server generates animated learning content based on student profiles and academic data.

[0402] Specific operation: Use the animation generation engine to create animation content based on a scenario.

[0403] Step 2:

[0404] The server transmits the generated animation content to the terminal.

[0405] Specific operation: Upload the generated animation file to the content management system and send a link to the device.

[0406] Step 3:

[0407] The terminal displays the content in the form of an animation to the user.

[0408] Specific operation: Play the anime file using a video player.

[0409] The Metaverse and Virtual Teachers

[0410] Step 1:

[0411] The server generates a learning environment within the metaverse and customizes it for each user.

[0412] Specific actions: Perform 3D modeling and scene setup to prepare a personalized learning environment for each user.

[0413] Step 2:

[0414] Users (students) put on VR glasses and log in to the metaverse.

[0415] Specific operation: Enter authentication information and launch the VR application.

[0416] Step 3:

[0417] The server operates the virtual teacher and interacts with the user.

[0418] Specific behavior: Executes the avatar teacher script, provides instruction to students, and answers questions.

[0419] Step 4:

[0420] The device handles interactions within the metaverse and provides appropriate feedback to the user.

[0421] Specific Actions: Collect and analyze data in real time and take appropriate action within the 3D environment.

[0422] Emotion engine integration

[0423] Step 1:

[0424] The server uses an emotion engine to analyze the user's emotions in real time.

[0425] Specific operation: Using an emotion analysis algorithm, emotional information is extracted from the user's facial expressions and voice.

[0426] Step 2:

[0427] The device inputs the user's facial expressions and voice into an emotion engine and sends the analysis results to the server.

[0428] Specific operation: Uses camera and microphone devices to collect the user's facial expressions and voice data and send it to an analysis engine.

[0429] Step 3:

[0430] The server dynamically adjusts the learning content and the behavior of the virtual teacher based on sentiment analysis.

[0431] Specific actions: Based on the emotional data, a script is executed to adjust the difficulty of the learning content and the behavior of the virtual teacher appropriately.

[0432] Paid services

[0433] Step 1:

[0434] The server presents the user with paid service options.

[0435] Specific operations: Generates information about fee plans and additional features for paid services and sends it to the device.

[0436] Step 2:

[0437] The user selects a paid service as needed and receives customization and educational materials provided by the server.

[0438] What it does: Complete the online subscription process and activate the selected services.

[0439] Step 3:

[0440] The terminal displays the selected paid service to the user.

[0441] Specific behavior: Displays access links and download options for paid content.

[0442] This clarifies the operation of a system that integrates an autonomous learning service and an emotion engine by providing a detailed explanation of the specific operations and flow at each processing step.

[0443] Example 2

[0444] 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."

[0445] The current education system makes it difficult to provide a personalized learning environment that reflects each student's individual academic ability and progress. Furthermore, there is no system that dynamically adjusts learning content based on a student's emotions and level of understanding. As a result, the quality of education declines and problems arise, such as a decrease in motivation to learn. Furthermore, effective learning support is not provided for students who are weak at writing or who are falling behind in specific areas. Furthermore, learning support using virtual reality environments is insufficient, and there is no customizable system that can meet specific learning needs.

[0446] 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.

[0447] In this invention, the server includes means for checking students' academic abilities, means for automatically generating an appropriate curriculum based on the check results, means for generating learning content based on the curriculum, means for providing the learning content in an optimal form to the student, means for managing the student's learning progress and achievement level, means for making necessary corrections according to the learning progress, means for supporting learning using a virtual teacher in a virtual reality environment, and means for analyzing the student's emotions and dynamically adjusting the learning content and the behavior of the virtual teacher based on the analysis results. This makes it possible to provide an optimized learning experience for each student, increase their motivation to learn, and improve the quality of education.

[0448] A "means for checking academic ability" is a device or program that has the function of generating questions to evaluate students' academic ability and collecting and analyzing students' answers to those questions.

[0449] A "means for automatically generating a curriculum" is a device or program that has the function of automatically creating an optimal learning plan that meets the individual learning needs of each student based on the results of an academic ability check.

[0450] A "means for generating learning content" is a device or program that has the function of creating and providing specific learning materials and exercises based on the generated curriculum.

[0451] A "means for providing learning content" is a device or program that has the function of effectively displaying or delivering the generated learning content to students.

[0452] A "means for managing learning progress and achievement" is a device or program that has the function of recording and analyzing the progress of students' learning activities and the degree of goal achievement.

[0453] The "means for making necessary modifications according to learning progress" refers to a device or program that has the function of dynamically modifying and adjusting learning plans and teaching materials suitable for students based on learning progress data.

[0454] A "learning support means using a virtual teacher in a virtual reality environment" is a device or program that has the function of allowing a virtual teacher to provide real-time guidance and support to students in a virtual environment created using virtual reality technology.

[0455] "Means for analyzing students' emotions and dynamically adjusting learning content and the behavior of the virtual teacher based on the analysis results" refers to a device or program that uses an emotion analysis engine to evaluate students' emotional states in real time and automatically change the learning content and the behavior of the virtual teacher based on the evaluation results.

[0456] This invention is an autonomous learning system that utilizes generative AI models and big data to support learning in educational settings. This system checks students' academic ability in educational settings such as elementary and junior high schools and provides individually optimized learning content to improve students' motivation and learning efficiency. In addition, by combining it with a sentiment analysis engine, it is possible to provide a more individually optimized learning experience.

[0457] First, the server loads the student's profile information and past academic achievement data from a database. This data is managed using an SQL database server (e.g., MySQL, PostgreSQL), and the retrieved data is temporarily stored in memory. The server then sends a prompt to the generative AI model to generate academic achievement test questions based on the loaded data. An example of a prompt used in this case is, "Please generate math questions based on the student's past grades." For example, OpenAI's GPT-4 is used as the generative AI model.

[0458] Next, the server sends the generated academic ability check questions to the terminal. The terminal displays the received academic ability check questions to the user (student). At this time, the terminal uses HTML and JavaScript to generate a screen for displaying the questions. The user (student) answers the academic ability check questions displayed on the terminal and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic ability.

[0459] Once the academic ability assessment is complete, the server automatically generates an appropriate curriculum based on the results. This curriculum is tailored to each student's individual learning needs, and the server creates the curriculum by sending a prompt to the generative AI model: "Please generate content that will reinforce the student's weaknesses." The server then creates learning content based on the generated curriculum and sends it to the device. The device then displays the generated learning content to the user (student). This process makes it possible to provide learning content that is optimized for each individual student.

[0460] The system also has the function of managing students' learning progress and achievement. The device records the user's (student's) learning progress and achievement in real time and periodically sends the data to the server. The server analyzes the collected progress data and suggests the next learning step or necessary corrections. The server also generates feedback and provides it to the user via the device to support the student's learning.

[0461] Furthermore, the server has the ability to generate animated learning content for students who are not good at reading. The server sends a prompt to the generative AI model, such as "Please generate an explanation of multiplication in animated format," and sends the generated animated content to the device. The device displays this content, allowing students to progress through their studies in a visually easy-to-understand format.

[0462] In addition, the server has a learning support function using a virtual teacher in a virtual reality environment. Users (students) wear VR glasses and log in to the metaverse, where they interact with the virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[0463] By integrating an emotion analysis engine, the server can analyze the user's emotions in real time and dynamically adjust the learning content and the behavior of the virtual teacher based on the results. For example, if a user looks tired while studying, the emotion analysis engine will recognize that emotion as "tired" and send it to the server. Based on this information, the server can provide content that is less difficult to learn or change the behavior of the virtual teacher to offer encouraging words.

[0464] Furthermore, the server manages the provision of virtual tutor services that can be customized for a fee, and presents additional content and customization options to the user as needed. The user can select these options to receive additional services. For example, the user can select a virtual tutor customization option for a fee and purchase dedicated additional content. The server generates this customization data and transmits it to the terminal, thereby providing a learning environment tailored to the user's needs.

[0465] As described above, by combining a generative AI model and a sentiment analysis engine, this system can provide a learning experience tailored to each student and improve the quality of education, thereby eliminating educational disparities and reducing the burden on teachers.

[0466] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0467] Program processing steps

[0468] Step 1: Loading the Data

[0469] The server loads student profile information and past academic performance data from a database.

[0470] Input: Student ID or user credentials.

[0471] Processing: The server uses SQL queries to retrieve student profile information and past academic performance data from the database and temporarily store them in memory.

[0472] Output: Loaded student profile information and academic performance data.

[0473] Specific operation: Parses data obtained from an SQL database server (e.g., MySQL, PostgreSQL) and loads it into memory.

[0474] Step 2: Generate ability test questions

[0475] The server generates academic ability check questions based on the loaded data.

[0476] Input: Loaded student profile information and academic data.

[0477] Processing: The server sends a prompt (e.g., "Generate math problems based on the student's past performance") to the generative AI model and formats the generated problems.

[0478] Output: The generated ability check questions.

[0479] Specific operation: Format the problem obtained from a generative AI model (e.g., GPT-4) in text format so that it can be used in the next step.

[0480] Step 3: Distribution of academic ability test questions

[0481] The server transmits the generated academic ability check questions to the terminal.

[0482] Input: Generated achievement check questions.

[0483] Processing: The server uses a REST API to send the generated questions to the terminal in JSON format.

[0484] Output: The academic ability test questions sent to the device.

[0485] Specific operation: Send data to the terminal using an HTTP request.

[0486] Step 4: Viewing the Academic Ability Test Questions

[0487] The terminal displays the academic ability check questions received from the server to the user (student).

[0488] Input: Academic ability check questions sent from the server.

[0489] Processing: The terminal uses HTML and JavaScript to generate a screen that displays the academic ability check questions to the user.

[0490] Output: The assessment question that is displayed to the user.

[0491] Specific operation: A web page is generated to display the academic ability check questions, and an interface is provided to allow the user to answer them.

[0492] Step 5: Receiving and sending responses

[0493] The user (student) answers the academic ability check questions and sends the answers to the server via the terminal.

[0494] Input: The answers to the assessment questions entered by the user.

[0495] Processing: When the user enters an answer into the terminal and presses the send button, the terminal sends the answer data in JSON format to the server.

[0496] Output: The response data sent to the server.

[0497] Specific operation: When the user enters an answer and clicks the submit button, the device sends the data to the server in JSON format.

[0498] Step 6: Analyze the answers

[0499] The server analyzes the received response data and evaluates the student's academic ability.

[0500] Input: User response data.

[0501] Processing: The server compares the received answer data with the correct answer data, generates an evaluation score, and stores it in a database.

[0502] Output: Student achievement assessment results.

[0503] Specific operation: The server compares the answers with the correct answer database, calculates the number of correct and incorrect answers, and generates an academic ability evaluation score.

[0504] Step 7: Auto-generate curriculum

[0505] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[0506] Input: Student academic assessment results.

[0507] Processing: Based on the academic assessment results, the server sends a prompt to the generative AI model saying, "Please generate content that will reinforce the student's weaknesses," and generates a curriculum.

[0508] Output: The generated curriculum.

[0509] Specific actions: Format the content obtained from the generative AI model and organize it into a curriculum.

[0510] Step 8: Generate and deliver learning content

[0511] The server creates learning content based on the generated curriculum and transmits it to the terminal.

[0512] Input: The generated curriculum.

[0513] Processing: The server sends the prompt "Please generate learning content based on the curriculum" to the generative AI model and sends the resulting content to the terminal.

[0514] Output: The learning content sent to the device.

[0515] Specific operation: The generated learning content is formatted appropriately and sent to the terminal via an HTTP request.

[0516] Step 9: View learning content

[0517] The terminal displays the generated learning content to the user (student).

[0518] Input: The learning content sent from the server.

[0519] Processing: The device uses HTML and JavaScript to generate a screen that displays the learning content.

[0520] Output: The learning content that is displayed to the user.

[0521] Specific operations: Generate a web page that displays learning content and allows users to learn using that content.

[0522] These are the specific processing steps of this system. At each step, processing is performed based on the input data, and the optimal result is output. This system can improve the quality of education and provide a learning experience that is tailored to each individual student.

[0523] (Application example 2)

[0524] 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."

[0525] While conventional autonomous learning systems support personalized learning based on each student's academic ability and progress, they are unable to take into account the student's emotions and condition, thereby failing to maximize learning effectiveness. Furthermore, while interactive learning methods are needed in actual educational settings to maintain student motivation and concentration, these methods still have limitations. Similar challenges are emerging when providing education and entertainment within autonomous vehicles.

[0526] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0527] In this invention, the server includes a means for checking students' academic ability, a means for automatically generating an appropriate curriculum based on the check results, and a means for generating learning content based on the curriculum. This enables individually optimized learning based on each student's academic ability and progress. Furthermore, the server includes a means for analyzing students' emotions and dynamically adjusting learning content based on the analysis results, and a means for visually providing learning content using smart glasses, enabling a learning experience that takes into account the student's condition and environment. Furthermore, learning support is possible using a virtual teacher within the metaverse, and interactive communication with students can maintain and improve their motivation and concentration.

[0528] "Means for checking academic ability" refers to devices or systems that ask questions to assess students' current academic ability and level of understanding, and then compile and analyze the results.

[0529] "Means for automatically generating a curriculum" refers to a device or system that automatically designs and creates optimal learning content and order based on the results of individual student academic ability checks and progress.

[0530] "Means for generating learning content" refers to devices or systems that create specific teaching materials, practice questions, explanatory materials, etc. based on the curriculum.

[0531] "Means for providing learning content" refers to devices or systems that display and provide the generated learning content to students in an appropriate manner.

[0532] "Means for managing learning progress and achievement" refers to devices or systems that record and analyze students' learning history, progress, and level of understanding, and provide appropriate feedback.

[0533] "Means for making necessary corrections" refers to devices or systems that adjust or correct the curriculum or learning content when there are problems with students' learning progress or level of understanding.

[0534] A "learning support means using a virtual teacher" is a device or system that uses a virtual teacher character in the metaverse to provide instruction to students and answer their questions.

[0535] "Means for analyzing emotions" refers to devices or systems that analyze students' facial expressions, voice, etc., and evaluate their current emotional state.

[0536] "Means for dynamically adjusting learning content" refers to devices or systems that change or adjust the learning content and difficulty level in real time based on the analyzed emotional results.

[0537] A "means for visually providing learning content using smart glasses" is a device or system that visually presents learning content via smart glasses and enhances students' learning experience.

[0538] "Means for providing learning content in animation format" refers to devices or systems that use animation to visually explain and present learning content so that it is easy for students to understand.

[0539] The "means for providing a customizable virtual teacher" refers to a device or system that provides a service that allows users to adjust and customize the appearance and behavior of a virtual teacher according to their requests.

[0540] This invention is an autonomous learning system that checks the academic ability of students, automatically generates an appropriate curriculum, and provides learning content. Each element will be described in detail below.

[0541] Academic ability check function

[0542] The server first loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user, and the user (student) answers these questions. The answer data is sent to the server, which analyzes the received answer data and evaluates the student's academic achievement.

[0543] Example: A student answers math problems on a device, and the answer data is sent to a server, which determines whether the answer is correct or incorrect and evaluates the student's level of understanding.

[0544] Automatic curriculum generation function

[0545] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The terminal displays the generated content to the user, providing optimal learning content.

[0546] Example: The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[0547] Learning progress management function

[0548] The device records the student's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[0549] Example: A device records a student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[0550] Sentiment analysis function

[0551] The server uses an emotion analysis engine to analyze students' emotions in real time. The device inputs the students' facial expressions and voices into the emotion analysis engine and sends the analysis results to the server. The server dynamically adjusts learning content and curriculum based on the emotion analysis.

[0552] For example, if a student looks tired while studying, the emotion engine will recognize the emotion as "tired" and send it to the server. Based on this information, the server will provide content that makes the learning easier or provide encouraging words from the virtual teacher to invigorate the student.

[0553] Content provision using smart glasses

[0554] The server provides selected learning content to students through smart glasses, which have built-in cameras and displays that allow students to enjoy a visual learning experience.

[0555] For example, a student wears smart glasses in a self-driving car and visually receives learning content in real time. When a difficult topic is presented, a sentiment analysis engine analyzes the student's reaction and simplifies the content displayed.

[0556] Virtual Teacher Function in the Metaverse

[0557] The server creates a virtual learning environment within the metaverse, with a customized virtual teacher for each student, who interacts with the student in real time to provide tailored instruction and feedback.

[0558] Example: A student puts on VR glasses and joins a virtual class in the metaverse. The server controls a virtual teacher who answers questions and provides guidance.

[0559] Prompt Sentence Examples

[0560] Develop a system that analyzes user emotions and dynamically changes educational content in real time. Design it to provide relaxing content when users feel fatigued.

[0561] As described above, this invention utilizes generative AI models and big data to provide individually optimized learning experiences tailored to each student's academic ability and emotions. Specific hardware includes a server, terminals, smart glasses, and VR glasses, while software includes TensorFlow, OpenCV, and an emotion analysis engine. This makes it possible to realize an innovative learning system that goes beyond conventional educational methods.

[0562] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0563] Step 1:

[0564] The server loads student profile information and past academic performance data from the database. The input is the student's personal information and past test data, and the output is student profile information. Based on this information, initial data for individual optimization is prepared.

[0565] Step 2:

[0566] The terminal displays the academic ability check questions received from the server to the user (student). The input is the check questions sent from the server, and the output is a quiz-style question displayed to the user. The user answers these questions through the terminal.

[0567] Step 3:

[0568] The user (student) answers the academic ability check questions and sends the answer data to the server via their terminal. The input is the user's answer data, and the output is the data sent to the server. This updates the user's current academic ability information.

[0569] Step 4:

[0570] The server analyzes the received response data and evaluates the user's (student's) academic ability. The input is the user's response data, and the output is the academic ability evaluation result. A generative AI model is used for the analysis to evaluate the accuracy rate and difficulty of the questions.

[0571] Step 5:

[0572] The server automatically generates an appropriate curriculum based on the results of academic assessment. The input is the academic assessment results, and the output is an individually optimized curriculum. The generated curriculum takes into account each student's strengths and weaknesses.

[0573] Step 6:

[0574] The server generates learning content based on the generated curriculum and sends it to the terminal. The input is the curriculum information, and the output is the learning content, which includes exercises, instructional videos, etc.

[0575] Step 7:

[0576] The terminal displays the generated learning content to the user (student), who then proceeds with the learning. The input is the learning content sent from the server, and the output is the user's learning activity.

[0577] Step 8:

[0578] The device records the student's learning progress and achievement in real time and periodically transmits it to the server. The input is the user's learning data, and the output is the data sent to the server. The learning progress is recorded in detail and the next step is prepared.

[0579] Step 9:

[0580] The server analyzes the collected learning progress data and proposes necessary corrections and next learning steps. The input is learning progress data, and the output is proposals and revised curriculum. This optimizes individual learning progress.

[0581] Step 10:

[0582] The server uses an emotion analysis engine to analyze students' emotions in real time. The input is the student's facial expressions and voice data, and the output is the emotional evaluation results. This analysis makes it possible to adjust according to the student's emotional state.

[0583] Step 11:

[0584] The server dynamically adjusts the learning content based on the emotion analysis results. The input is the emotion analysis results, and the output is the adjusted learning content. For example, if the user feels fatigued, the content may be lighter or an encouraging message may be displayed.

[0585] Step 12:

[0586] The device visually presents learning content using smart glasses. The input is content received from the server, and the output is content visually presented to the user, thereby visually enhancing the learning experience.

[0587] Step 13:

[0588] The server provides learning support using virtual teachers within the metaverse. The input is the user's learning data, and the output is guidance and feedback from the virtual teacher. Students can participate in virtual classes and receive direct guidance from the virtual teacher.

[0589] The above are the specific processing steps for carrying out the invention. At each step, input data is processed and analyzed to obtain optimal output, thereby realizing individually optimized learning for each student.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] [Second embodiment]

[0594] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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."

[0606] This invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. The invention consists of the following main functions and steps.

[0607] Academic ability check function

[0608] First, the server loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user. The user (student) answers these questions and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic achievement.

[0609] (Example)

[0610] Students answer math problems on their devices, and the answers are sent to the server, which then determines whether the answers are correct and evaluates the student's level of math comprehension.

[0611] Personalize your learning content

[0612] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The device displays the generated content to the user, thereby providing optimal learning content for each student.

[0613] (Example)

[0614] The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[0615] Achievement and progress management

[0616] The device records the user's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[0617] (Example)

[0618] The device records the student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[0619] Anime-style content

[0620] The server generates learning content in the form of animation for students who are not good at reading texts, and the device displays this content to the user, allowing them to progress through their studies in a visually easy-to-understand format.

[0621] (Example)

[0622] The server generates animated content for multiplication for students who have difficulty understanding letters and sends it to the device, which displays it and allows students to learn by watching the animation.

[0623] The Metaverse and Virtual Teachers

[0624] The server generates a virtual learning environment in the metaverse and operates a virtual teacher for each user. The user (student) wears VR glasses, logs into the metaverse, and interacts with the virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[0625] (Example)

[0626] Students wear VR glasses and participate in a virtual class in the metaverse, while the server controls a virtual teacher who answers students' questions and provides instruction.

[0627] Paid services

[0628] The server manages the provision of paid customizable virtual tutor services and presents additional content and customization options to users as needed. Users select these options to receive additional services.

[0629] (Example)

[0630] The user selects a customization option for the virtual teacher and purchases additional content for a fee. The server generates this customization data and transmits it to the terminal.

[0631] The specific embodiment of the present invention has been described above. This system can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers.

[0632] The processing flow will be explained below.

[0633] Academic ability check function

[0634] Step 1:

[0635] The server loads student profile information and past academic performance data from a database.

[0636] Specific operation: Using an SQL query, retrieve student data corresponding to the user ID from the database.

[0637] Step 2:

[0638] The terminal displays the academic ability check questions received from the server to the user.

[0639] Specific operation: Renders problem data obtained from the server via an HTTP request on the screen using HTML and CSS.

[0640] Step 3:

[0641] The user (student) answers the academic ability check questions displayed on the terminal.

[0642] Specific actions: Enter answers using the form and click the submit button.

[0643] Step 4:

[0644] The terminal transmits the user's response data to the server.

[0645] Specific behavior: Uses JavaScript to convert form data into JSON format and sends it to the server via a POST request.

[0646] Step 5:

[0647] The server analyzes the received response data and evaluates the student's academic ability.

[0648] Specific operation: Calculates the accuracy rate and answer speed using scripts such as Python and Java, and generates an academic ability evaluation score.

[0649] Personalize your learning content

[0650] Step 1:

[0651] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[0652] Specific actions: Utilizing generative AI to formulate learning items and sequences and build a curriculum.

[0653] Step 2:

[0654] The server generates learning content based on the generated curriculum.

[0655] Specific operation: Generates multimedia content such as text, images, and videos and sends them to the device via API.

[0656] Step 3:

[0657] The terminal displays the study content sent from the server to the user.

[0658] What it does: Parses JSON data and renders it into UI components with HTML and CSS.

[0659] Achievement and progress management

[0660] Step 1:

[0661] The device records the user's learning progress and achievement in real time.

[0662] What it does: Uses JavaScript to track actions and saves the data to a real-time database.

[0663] Step 2:

[0664] The terminal transmits the recorded progress data to the server at regular intervals.

[0665] Specific operation: Using Ajax, progress data is sent to the server in batch processing.

[0666] Step 3:

[0667] The server analyzes the collected progress data and suggests next learning steps and necessary corrections.

[0668] What it does: It uses machine learning algorithms to analyze data and generate feedback scores and suggestions.

[0669] Step 4:

[0670] The terminal displays the feedback content received from the server to the user.

[0671] Specific behavior: Display the feedback data in the UI and notify the user using popups or notifications.

[0672] Anime-style content

[0673] Step 1:

[0674] The server generates animated learning content based on student profiles and academic data.

[0675] Specific operation: Use the animation generation engine to create animation content based on a scenario.

[0676] Step 2:

[0677] The server transmits the generated animation content to the terminal.

[0678] Specific operation: Upload the generated animation file to the content management system and send a link to the device.

[0679] Step 3:

[0680] The terminal displays the content in the form of an animation to the user.

[0681] Specific operation: Play the anime file using a video player.

[0682] The Metaverse and Virtual Teachers

[0683] Step 1:

[0684] The server generates a learning environment within the metaverse and customizes it for each user.

[0685] Specific actions: Perform 3D modeling and scene setup to prepare a personalized learning environment for each user.

[0686] Step 2:

[0687] Users (students) put on VR glasses and log in to the metaverse.

[0688] Specific operation: Enter authentication information and launch the VR application.

[0689] Step 3:

[0690] The server operates the virtual teacher and interacts with the user.

[0691] Specific behavior: Executes the avatar teacher script, provides instruction to students, and answers questions.

[0692] Step 4:

[0693] The device handles interactions within the metaverse and provides appropriate feedback to the user.

[0694] Specific Actions: Collect and analyze data in real time and take appropriate action within the 3D environment.

[0695] Paid services

[0696] Step 1:

[0697] The server presents the user with paid service options.

[0698] Specific operations: Generates information about fee plans and additional features for paid services and sends it to the device.

[0699] Step 2:

[0700] The user selects a paid service as needed and receives customization and educational materials provided by the server.

[0701] What it does: Complete the online subscription process and activate the selected services.

[0702] Step 3:

[0703] The terminal displays the selected paid service to the user.

[0704] Specific behavior: Displays access links and download options for paid content.

[0705] In this way, by explaining the specific operations at each processing step, the processing flow of the program for the autonomous learning service can be explained in detail.

[0706] Example 1

[0707] 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."

[0708] Traditional educational systems have struggled to provide personalized learning tailored to each student's learning progress and level of understanding. They also lacked appropriate learning support for students who struggled with writing, and real-time learning support in virtual environments was limited. Furthermore, the lack of real-time recording of learning progress and prompt, appropriate feedback led to problems that reduced learning effectiveness.

[0709] 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.

[0710] In this invention, the server includes a means for checking students' academic ability, a means for automatically generating an appropriate curriculum based on the check results, and a means for generating learning content based on the curriculum. This enables personalized learning based on each student's level of understanding. The server also includes a means for generating academic ability check questions using an AI model, a means for recording learning progress data in real time using a terminal, a means for providing learning content in animation format, and a means for accessing a virtual learning environment using a VR device. This enables appropriate learning support for students who are weak at writing and real-time learning support in a virtual environment, as well as real-time recording of learning progress and rapid feedback.

[0711] A "means for checking students' academic ability" is a system that measures students' academic ability by having students input answers and evaluating those answers.

[0712] The "means for automatically generating an appropriate curriculum based on the check results" is a system that analyzes the results of students' academic ability checks and generates the optimal learning plan for each individual student based on that.

[0713] The "means for generating curriculum-based learning content" is a system that creates specific learning materials and problem sets based on an automatically generated curriculum.

[0714] "Means to provide learning content in the most optimal form to students" refers to a system that displays and delivers learning content in accordance with each student's learning style and progress.

[0715] "Means for managing students' learning progress and achievement" refers to a system that records and analyzes students' learning progress and achievement in real time.

[0716] "Means for making necessary corrections according to learning progress" refers to a system that provides necessary adjustments and advice based on students' learning data.

[0717] "Learning support means using a virtual teacher in the metaverse" is a system in which a virtual teacher provides guidance and support to students in a virtual reality space.

[0718] "Means for generating academic ability check questions using an AI model" refers to a system that uses artificial intelligence to automatically generate questions to measure students' academic ability.

[0719] "Means for recording learning progress data in real time using a terminal" refers to a system that records learning progress in real time via the device used by the student.

[0720] "Means for providing learning content in animation format" refers to a system that provides learning content using animations that are visually easy for students to understand.

[0721] "Means for accessing a virtual learning environment using a VR device" refers to a system that uses a virtual reality device to access a virtual learning space and conduct learning there.

[0722] MODE FOR CARRYING OUT THE INVENTION

[0723] This invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. The following describes a specific implementation of this system.

[0724] Hardware and software used

[0725] 1. Server: Use the following cloud platforms:

[0726] Amazon Web Services (AWS)

[0727] Microsoft Azure

[0728] 2. Devices: Use the following devices and software:

[0729] Tablet devices, PCs

[0730] Chrome browser

[0731] 3. Database: The following database management systems are used:

[0732] MySQL

[0733] PostgreSQL

[0734] 4. AI model: The following generative AI model is used:

[0735] OpenAI GPT-4

[0736] 5. VR Devices: Use the following virtual reality devices:

[0737] Oculus Rift

[0738] HTC Vive

[0739] Program processing

[0740] Academic ability check function

[0741] The server loads the student's profile information and past academic performance data from the database. Specifically, it retrieves the student's name, age, past grades, etc. from the MySQL database. The server then uses the generative AI model to generate academic performance check questions and sends them to the device. For example, the following prompt sentence is input to the AI ​​model:

[0742] Generate five math problems appropriate for your students' ages.

[0743] The device displays academic ability check questions to the user. For example, a math problem is displayed on a tablet screen. The user (student) answers the question, for example, by entering "2 + 2 = 4." The device then sends the user's answer to the server. The server analyzes the answer data and evaluates the student's academic ability. The analysis method is to calculate the percentage of correct answers and quantify the level of understanding.

[0744] Personalize your learning content

[0745] The server automatically generates an appropriate curriculum based on the results of the academic ability check. This process uses a generative AI model to determine the "next thing to learn." The server then generates learning content based on the automatically generated curriculum and sends it to the device. The generated content can be in the form of a PDF file or video link. The device then displays the personalized learning content to the user.

[0746] Specifically, the generative AI model is fed with the following prompt sentence:

[0747] Generate practice content for math problems based on students' understanding.

[0748] Achievement and progress management

[0749] The device records the user's learning progress data in real time. For example, it records how long it took to solve each problem. The recorded data is sent to the server at the end of the learning session. The server analyzes the progress data and suggests the next learning step. For example, it may suggest, "Try a slightly more difficult problem."

[0750] A concrete example is the following prompt:

[0751] This week's study time was a total of 10 hours. Let's do our best next week!

[0752] Anime-style content

[0753] The server generates learning content in the form of animation for students who are not good at reading. The generated animation content includes video file formats and streaming links, and is sent to the terminal. The terminal displays this content to the user.

[0754] For example, use the following prompt:

[0755] Create an animation that visually explains the concept of multiplication.

[0756] The Metaverse and Virtual Teachers

[0757] The server generates a virtual learning environment in the metaverse, and users (students) log in wearing VR devices. The server operates the virtual teacher, and the device processes this interaction and provides appropriate feedback to the user. Learning support is provided in real time using the VR device.

[0758] Examples of prompt statements include:

[0759] Put on your VR glasses and move on to the next virtual experiment.

[0760] Paid services

[0761] The server presents paid customization options, and users (students and parents) select and purchase additional services. For example, payment can be made by credit card. The server generates customized additional content and sends it to the device.

[0762] A concrete example is the following prompt:

[0763] Use our paid service to get specialized instruction on applied math problems.

[0764] As described above, this system enables personalized learning based on each student's level of understanding, contributing to improving the quality of education.

[0765] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0766] Step 1:

[0767] The server loads student profile information and past academic performance data. Specifically, it retrieves information such as student name, age, and past grades from a MySQL database. The input is student identification information such as ID, and the output is this data. Data processing involves extracting the necessary information using database queries and formatting it into a structured data format.

[0768] Step 2:

[0769] The server uses a generative AI model to generate academic ability check questions. The input is information such as the student's age and academic level, and the output is the generated academic ability check questions. To process the data, the following prompt is input to the AI ​​model: "Generate five arithmetic problems appropriate for the student's age."

[0770] Step 3:

[0771] The server sends the generated academic ability check questions to the terminal. The input is the generated question data, and the output is the sent question data. For data processing, the question data is serialized in JSON format or similar and sent to the terminal over the network.

[0772] Step 4:

[0773] The terminal displays the academic ability check questions received from the server to the user. The input is the received question data, and the output is the question displayed on the user interface. Specifically, the terminal deserializes the question data and displays it on the screen.

[0774] Step 5:

[0775] The user answers the academic ability check questions. The input is the displayed question, and the output is the user's answer data. In concrete terms, the user inputs the answer on the terminal.

[0776] Step 6:

[0777] The terminal sends the user's answer to the server. The input is the user's answer data, and the output is the answer data to be sent to the server. For data processing, the answer data is serialized in JSON format or similar and sent to the server via the network.

[0778] Step 7:

[0779] The server analyzes the response data and evaluates the student's academic ability. The input is the user's response data, and the output is the academic ability evaluation result. Data processing involves determining whether the answer is correct or incorrect, and quantifying the accuracy rate and level of understanding.

[0780] Step 8:

[0781] The server automatically generates an appropriate curriculum based on the results of the academic ability check. The input is the academic ability assessment results, and the output is the generated curriculum. Data processing uses an AI model to determine what should be learned next.

[0782] Step 9:

[0783] The server generates learning content based on the generated curriculum and sends it to the terminal. The input is the curriculum data, and the output is the transmitted learning content. Data processing involves converting the learning content into PDF files, video links, etc., and sending them over the network.

[0784] Step 10:

[0785] The terminal displays personalized learning content to the user. The input is the transmitted learning content, and the output is the content displayed on the user interface. Specifically, the terminal deserializes the content into a display format and displays it.

[0786] Step 11:

[0787] The device records the user's learning progress data in real time. The input is the user's learning behavior, and the output is the recorded progress data. Specific operations include recording the start and end times of learning and the time spent on each problem.

[0788] Step 12:

[0789] The device sends the recorded progress data to the server. The input is the progress data, and the output is the data to be sent to the server. The data is processed by serializing the progress data in JSON format or similar and sending it over the network.

[0790] Step 13:

[0791] The server analyzes the progress data and proposes the next learning step. The input is the progress data and the output is the proposal. Data processing involves analyzing the learning progress and recommending what to learn next.

[0792] Step 14:

[0793] The server sends the proposal to the terminal. The input is the proposal, and the output is the data sent to the terminal. The proposal is serialized and sent over the network as data processing.

[0794] Step 15:

[0795] The terminal displays the suggestion to the user. The input is the suggestion, and the output is the suggestion displayed on the user interface. Specifically, the suggestion is deserialized and displayed on the screen.

[0796] (Application example 1)

[0797] 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."

[0798] In today's educational environment, it is difficult to use a uniform teaching method to accommodate students with diverse learning styles and levels of understanding, which can lead to a decline in educational effectiveness and a decrease in motivation to learn. Furthermore, providing optimal learning content to each student is difficult to do manually, increasing the burden on teachers. Furthermore, with the spread of distance education, there is a demand for methods to provide learning support at the same level as face-to-face classes.

[0799] 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.

[0800] In this invention, the server includes means for checking students' academic abilities, means for automatically generating an appropriate curriculum based on the check results, means for generating learning content based on the curriculum, means for providing the learning content in an optimal form to the student via a smartphone application, means for managing learning progress and achievement, means for making necessary corrections according to the learning progress, means for learning support using a virtual teacher in the metaverse, and means for providing real-time interaction with the virtual teacher. This provides an optimized learning experience for each student, improves the quality of education, reduces the burden on teachers, and makes it possible to provide support equivalent to face-to-face classes in a distance learning environment.

[0801] "Student" refers to a person engaged in a learning activity.

[0802] "Academic ability" is an indicator of the degree of acquisition of knowledge and skills in a particular academic field.

[0803] "Checking" refers to the act of evaluating and confirming academic ability, progress, and other status.

[0804] "Curriculum" refers to the entire educational content organized to achieve specific educational objectives.

[0805] "Automatic generation" refers to the process in which the system autonomously generates the necessary data and information without the need for manual operation.

[0806] "Learning content" refers to a collection of instructional materials or information provided to achieve a specific learning goal.

[0807] "Providing" refers to the act of distributing or supplying learning materials, services, etc. to learners.

[0808] "Progress" is a state that indicates how much progress has been made in learning activities.

[0809] "Degree of achievement" refers to the level that indicates the degree to which a set learning goal has been achieved.

[0810] "Management" refers to monitoring and controlling the status of progress and achievement, and taking appropriate measures.

[0811] "Correction" refers to correcting or improving an error or deficiency.

[0812] The "metaverse" refers to a virtual space built on the Internet where users can interact through their own avatars.

[0813] A "virtual teacher" refers to an avatar or AI system that teaches students in a virtual space in place of a real teacher.

[0814] "Smartphone application" refers to a software program that runs on a smartphone.

[0815] "Real-time" refers to a state in which a particular operation or process is performed immediately without delay.

[0816] "Interaction" refers to the flow of mutual communication and operations between a user and a system or avatar.

[0817] This invention is an educational support system that utilizes generative AI and big data, and is realized through a smartphone application, a server, and a metaverse environment. Specific embodiments of the system are described below.

[0818] Hardware and Software Configuration

[0819] Server: A high-performance server is used to manage user data, analyze academic ability, and create an automated curriculum using generative AI models. This server is a system equipped with a database, AI models, and APIs.

[0820] Smartphone: Functions as an interface for learners (users). A dedicated application is installed on the smartphone, and users use it to check their academic ability, access learning content, and manage their progress.

[0821] Metaverse environment: A system for providing learning support in a virtual space, allowing real-time interaction with a virtual teacher. This environment is accessed via VR devices.

[0822] System Operation

[0823] 1. Academic ability check

[0824] The server generates questions for an academic ability check based on the user's profile information and past academic ability data. The smartphone application presents these questions to the user and sends the user's answers to the server. The server analyzes the answer data and evaluates the user's academic ability. The evaluation results are used in the next step.

[0825] 2. Providing personalized learning content

[0826] Based on the results of the academic ability check, the server uses a generative AI model to automatically generate an optimal curriculum for the user. Based on that curriculum, specific learning content is created and sent to a smartphone application. The user can then use this content to progress through their studies via the application.

[0827] 3. Tracking learning progress and achievement

[0828] The smartphone application records the user's learning progress and achievement in real time. This data is periodically sent to the server, which analyzes it and suggests necessary corrections and next learning steps. The user receives feedback and continues learning.

[0829] 4. Learning Support in the Metaverse

[0830] The server operates a virtual teacher in the metaverse environment and realizes real-time interaction with the user. The user can log in to the metaverse using a VR device and receive guidance and answers to questions from the virtual teacher.

[0831] 5. Provision of paid services

[0832] The server also provides users with customization options and additional content for a fee, allowing them to customize their virtual teacher. This data is sent to a smartphone application, allowing users to enhance their learning with additional, specialized content.

[0833] Examples and prompts

[0834] For example, there is a system in which a user with user ID "12345" takes an academic ability check on their smartphone, sends the results to a server, and receives learning content appropriate for the user. The following is a prompt for this specific example system:

[0835] Please write Python code to have the user with user ID "12345" take an academic ability check on their smartphone, send the results to the server, and receive learning content appropriate for the user.

[0836] By inputting this prompt into a generative AI model, it is expected that appropriate code will be generated and used as part of the system.

[0837] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0838] Step 1:

[0839] The server loads the user's profile information and past academic achievement data from a database.

[0840] Input: User ID, user profile information in database and past academic achievement data.

[0841] Data manipulation and calculations: Performing database queries to retrieve required information and load user records.

[0842] Output: User profile information and past academic achievement data.

[0843] Step 2:

[0844] Based on the loaded data, the server automatically generates questions for academic ability tests using a generative AI model.

[0845] Input: User profile information and past academic achievement data.

[0846] Data processing and calculation: Generative AI models generate questions appropriate to current academic ability.

[0847] Output: A set of questions for academic ability check.

[0848] Step 3:

[0849] The terminal (smartphone) displays the academic ability test questions received from the server to the user.

[0850] Input: A set of questions for the academic ability check sent from the server.

[0851] Data processing and calculation: Converting received data into a display format and applying it to the user interface.

[0852] Output: Academic ability test questions displayed in the user interface.

[0853] Step 4:

[0854] The user answers the academic ability check questions through the terminal and transmits the answers to the server.

[0855] Input: User response data.

[0856] Data processing and calculation: Collect response data and send it to the server.

[0857] Output: Response data.

[0858] Step 5:

[0859] The server analyzes the received response data and evaluates the user's academic ability.

[0860] Input: User response data.

[0861] Data processing and calculation: Analyze the user's academic ability by applying algorithms to determine whether the answers are correct and to evaluate academic ability.

[0862] Output: Academic assessment results.

[0863] Step 6:

[0864] Based on the academic assessment results, the server uses a generative AI model to automatically generate the optimal curriculum for the user.

[0865] Input: Academic assessment results.

[0866] Data processing and computation: Using generative AI models to create curriculum.

[0867] Output: Personalized curriculum.

[0868] Step 7:

[0869] The server creates specific learning content based on the generated curriculum and transmits it to the terminal.

[0870] Enter: personalized curriculum.

[0871] Data processing and calculation: Generate teaching materials and content corresponding to each learning step.

[0872] Output: Learning content.

[0873] Step 8:

[0874] The terminal displays the learning content received from the server to the user.

[0875] Input: Learning content sent from the server.

[0876] Data processing and calculation: Converting received data into a display format and applying it to the user interface.

[0877] Output: Learning content displayed in a user interface

[0878] Step 9:

[0879] The device records the user's learning progress and achievement in real time and periodically transmits the data to the server.

[0880] Input: User learning activity data.

[0881] Data processing and calculation: Analyzes learning progress and achievement level and sends the data to the server.

[0882] Output: Progress data sent to the server.

[0883] Step 10:

[0884] The server analyzes the collected progress data and suggests corrections to the learning content and the next learning step.

[0885] Input: Learning progress and achievement data.

[0886] Data processing and calculations: Perform calculations based on progress data to determine necessary corrections and next steps.

[0887] Output: Suggested revisions to what you learned and suggested next steps.

[0888] Step 11:

[0889] Users wear VR devices and participate in virtual classes in the metaverse, while the server controls a virtual teacher who interacts with users in real time.

[0890] Input: User login data, Virtual Learning Environment data.

[0891] Data processing and computation: Controlling interactions in the virtual space and responding to user questions.

[0892] Output: A learning experience with a virtual teacher within the metaverse.

[0893] Step 12:

[0894] The server offers customizable virtual teachers and additional content options for a fee.

[0895] Input: User request data, customization options data.

[0896] Data Processing and Computing: Manage data related to paid services and generate customized virtual teachers and content.

[0897] Output: Customized virtual teacher data and additional content.

[0898] 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.

[0899] The present invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the quality of education can be further improved. An embodiment of the present invention is described in detail below.

[0900] Academic ability check function

[0901] First, the server loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user. The user (student) answers these questions and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic achievement.

[0902] (Example)

[0903] Students answer math problems on their devices, and the answers are sent to the server, which then determines whether the answers are correct and evaluates the student's level of math comprehension.

[0904] Personalize your learning content

[0905] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The device displays the generated content to the user, thereby providing optimal learning content for each student.

[0906] (Example)

[0907] The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[0908] Achievement and progress management

[0909] The device records the user's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[0910] (Example)

[0911] The device records the student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[0912] Anime-style content

[0913] The server generates learning content in the form of animation for students who are not good at reading texts, and the device displays this content to the user, allowing them to progress through their studies in a visually easy-to-understand format.

[0914] (Example)

[0915] The server generates animated content for multiplication for students who have difficulty understanding letters and sends it to the device, which displays it and allows students to learn by watching the animation.

[0916] The Metaverse and Virtual Teachers

[0917] The server generates a virtual learning environment in the metaverse and customizes it for each user. Users (students) wear VR glasses, log in to the metaverse, and interact with a virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[0918] (Example)

[0919] Students wear VR glasses and participate in a virtual class in the metaverse, while the server controls a virtual teacher who answers students' questions and provides instruction.

[0920] Emotion engine integration

[0921] The server uses an emotion engine to analyze the user's emotions in real time. The device inputs the user's facial expressions and voice into the emotion engine and sends the analysis results to the server. The server dynamically adjusts the learning content and the behavior of the virtual teacher based on the emotion analysis.

[0922] (Example)

[0923] If a user shows a tired expression while studying, the emotion engine recognizes the emotion as "fatigue" and sends it to the server. Based on this information, the server can provide content that is easier to study or change the behavior of the virtual teacher to offer encouraging words.

[0924] Paid services

[0925] The server manages the provision of paid customizable virtual tutor services and presents additional content and customization options to users as needed. Users select these options to receive additional services.

[0926] (Example)

[0927] The user selects a customization option for the virtual teacher and purchases additional content for a fee. The server generates this customization data and transmits it to the terminal.

[0928] A specific embodiment of the present invention has been described above. This system can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers. Furthermore, the integration of an emotion engine makes it possible to provide a more personalized learning experience.

[0929] The processing flow will be explained below.

[0930] Academic ability check function

[0931] Step 1:

[0932] The server loads student profile information and past academic performance data from a database.

[0933] Specific operation: Using an SQL query, retrieve student data corresponding to the user ID from the database.

[0934] Step 2:

[0935] The terminal displays the academic ability check questions received from the server to the user.

[0936] Specific operation: Renders problem data obtained from the server via an HTTP request on the screen using HTML and CSS.

[0937] Step 3:

[0938] The user (student) answers the academic ability check questions displayed on the terminal.

[0939] Specific actions: Enter answers using the form and click the submit button.

[0940] Step 4:

[0941] The terminal transmits the user's response data to the server.

[0942] Specific behavior: Uses JavaScript to convert form data into JSON format and sends it to the server via a POST request.

[0943] Step 5:

[0944] The server analyzes the received response data and evaluates the student's academic ability.

[0945] Specific operation: Calculates the accuracy rate and answer speed using scripts such as Python and Java, and generates an academic ability evaluation score.

[0946] Personalize your learning content

[0947] Step 1:

[0948] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[0949] Specific actions: Utilizing generative AI to formulate learning items and sequences and build a curriculum.

[0950] Step 2:

[0951] The server generates learning content based on the generated curriculum.

[0952] Specific operation: Generates multimedia content such as text, images, and videos and sends them to the device via API.

[0953] Step 3:

[0954] The terminal displays the study content sent from the server to the user.

[0955] What it does: Parses JSON data and renders it into UI components with HTML and CSS.

[0956] Achievement and progress management

[0957] Step 1:

[0958] The device records the user's learning progress and achievement in real time.

[0959] What it does: Uses JavaScript to track actions and saves the data to a real-time database.

[0960] Step 2:

[0961] The terminal transmits the recorded progress data to the server at regular intervals.

[0962] Specific operation: Using Ajax, progress data is sent to the server in batch processing.

[0963] Step 3:

[0964] The server analyzes the collected progress data and suggests next learning steps and necessary corrections.

[0965] What it does: It uses machine learning algorithms to analyze data and generate feedback scores and suggestions.

[0966] Step 4:

[0967] The terminal displays the feedback content received from the server to the user.

[0968] Specific behavior: Display the feedback data in the UI and notify the user using popups or notifications.

[0969] Anime-style content

[0970] Step 1:

[0971] The server generates animated learning content based on student profiles and academic data.

[0972] Specific operation: Use the animation generation engine to create animation content based on a scenario.

[0973] Step 2:

[0974] The server transmits the generated animation content to the terminal.

[0975] Specific operation: Upload the generated animation file to the content management system and send a link to the device.

[0976] Step 3:

[0977] The terminal displays the content in the form of an animation to the user.

[0978] Specific operation: Play the anime file using a video player.

[0979] The Metaverse and Virtual Teachers

[0980] Step 1:

[0981] The server generates a learning environment within the metaverse and customizes it for each user.

[0982] Specific actions: Perform 3D modeling and scene setup to prepare a personalized learning environment for each user.

[0983] Step 2:

[0984] Users (students) put on VR glasses and log in to the metaverse.

[0985] Specific operation: Enter authentication information and launch the VR application.

[0986] Step 3:

[0987] The server operates the virtual teacher and interacts with the user.

[0988] Specific behavior: Executes the avatar teacher script, provides instruction to students, and answers questions.

[0989] Step 4:

[0990] The device handles interactions within the metaverse and provides appropriate feedback to the user.

[0991] Specific Actions: Collect and analyze data in real time and take appropriate action within the 3D environment.

[0992] Emotion engine integration

[0993] Step 1:

[0994] The server uses an emotion engine to analyze the user's emotions in real time.

[0995] Specific operation: Using an emotion analysis algorithm, emotional information is extracted from the user's facial expressions and voice.

[0996] Step 2:

[0997] The device inputs the user's facial expressions and voice into an emotion engine and sends the analysis results to the server.

[0998] Specific operation: Uses camera and microphone devices to collect the user's facial expressions and voice data and send it to an analysis engine.

[0999] Step 3:

[1000] The server dynamically adjusts the learning content and the behavior of the virtual teacher based on sentiment analysis.

[1001] Specific actions: Based on the emotional data, a script is executed to adjust the difficulty of the learning content and the behavior of the virtual teacher appropriately.

[1002] Paid services

[1003] Step 1:

[1004] The server presents the user with paid service options.

[1005] Specific operations: Generates information about fee plans and additional features for paid services and sends it to the device.

[1006] Step 2:

[1007] The user selects a paid service as needed and receives customization and educational materials provided by the server.

[1008] What it does: Complete the online subscription process and activate the selected services.

[1009] Step 3:

[1010] The terminal displays the selected paid service to the user.

[1011] Specific behavior: Displays access links and download options for paid content.

[1012] This clarifies the operation of a system that integrates an autonomous learning service and an emotion engine by providing a detailed explanation of the specific operations and flow at each processing step.

[1013] Example 2

[1014] 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."

[1015] The current education system makes it difficult to provide a personalized learning environment that reflects each student's individual academic ability and progress. Furthermore, there is no system that dynamically adjusts learning content based on a student's emotions and level of understanding. As a result, the quality of education declines and problems arise, such as a decrease in motivation to learn. Furthermore, effective learning support is not provided for students who are weak at writing or who are falling behind in specific areas. Furthermore, learning support using virtual reality environments is insufficient, and there is no customizable system that can meet specific learning needs.

[1016] 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.

[1017] In this invention, the server includes means for checking students' academic abilities, means for automatically generating an appropriate curriculum based on the check results, means for generating learning content based on the curriculum, means for providing the learning content in an optimal form to the student, means for managing the student's learning progress and achievement level, means for making necessary corrections according to the learning progress, means for supporting learning using a virtual teacher in a virtual reality environment, and means for analyzing the student's emotions and dynamically adjusting the learning content and the behavior of the virtual teacher based on the analysis results. This makes it possible to provide an optimized learning experience for each student, increase their motivation to learn, and improve the quality of education.

[1018] A "means for checking academic ability" is a device or program that has the function of generating questions to evaluate students' academic ability and collecting and analyzing students' answers to those questions.

[1019] A "means for automatically generating a curriculum" is a device or program that has the function of automatically creating an optimal learning plan that meets the individual learning needs of each student based on the results of an academic ability check.

[1020] A "means for generating learning content" is a device or program that has the function of creating and providing specific learning materials and exercises based on the generated curriculum.

[1021] A "means for providing learning content" is a device or program that has the function of effectively displaying or delivering the generated learning content to students.

[1022] A "means for managing learning progress and achievement" is a device or program that has the function of recording and analyzing the progress of students' learning activities and the degree of goal achievement.

[1023] The "means for making necessary modifications according to learning progress" refers to a device or program that has the function of dynamically modifying and adjusting learning plans and teaching materials suitable for students based on learning progress data.

[1024] A "learning support means using a virtual teacher in a virtual reality environment" is a device or program that has the function of allowing a virtual teacher to provide real-time guidance and support to students in a virtual environment created using virtual reality technology.

[1025] "Means for analyzing students' emotions and dynamically adjusting learning content and the behavior of the virtual teacher based on the analysis results" refers to a device or program that uses an emotion analysis engine to evaluate students' emotional states in real time and automatically change the learning content and the behavior of the virtual teacher based on the evaluation results.

[1026] This invention is an autonomous learning system that utilizes generative AI models and big data to support learning in educational settings. This system checks students' academic ability in educational settings such as elementary and junior high schools and provides individually optimized learning content to improve students' motivation and learning efficiency. In addition, by combining it with a sentiment analysis engine, it is possible to provide a more individually optimized learning experience.

[1027] First, the server loads the student's profile information and past academic achievement data from a database. This data is managed using an SQL database server (e.g., MySQL, PostgreSQL), and the retrieved data is temporarily stored in memory. The server then sends a prompt to the generative AI model to generate academic achievement test questions based on the loaded data. An example of a prompt used in this case is, "Please generate math questions based on the student's past grades." For example, OpenAI's GPT-4 is used as the generative AI model.

[1028] Next, the server sends the generated academic ability check questions to the terminal. The terminal displays the received academic ability check questions to the user (student). At this time, the terminal uses HTML and JavaScript to generate a screen for displaying the questions. The user (student) answers the academic ability check questions displayed on the terminal and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic ability.

[1029] Once the academic ability assessment is complete, the server automatically generates an appropriate curriculum based on the results. This curriculum is tailored to each student's individual learning needs, and the server creates the curriculum by sending a prompt to the generative AI model: "Please generate content that will reinforce the student's weaknesses." The server then creates learning content based on the generated curriculum and sends it to the device. The device then displays the generated learning content to the user (student). This process makes it possible to provide learning content that is optimized for each individual student.

[1030] The system also has the function of managing students' learning progress and achievement. The device records the user's (student's) learning progress and achievement in real time and periodically sends the data to the server. The server analyzes the collected progress data and suggests the next learning step or necessary corrections. The server also generates feedback and provides it to the user via the device to support the student's learning.

[1031] Furthermore, the server has the ability to generate animated learning content for students who are not good at reading. The server sends a prompt to the generative AI model, such as "Please generate an explanation of multiplication in animated format," and sends the generated animated content to the device. The device displays this content, allowing students to progress through their studies in a visually easy-to-understand format.

[1032] In addition, the server has a learning support function using a virtual teacher in a virtual reality environment. Users (students) wear VR glasses and log in to the metaverse, where they interact with the virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[1033] By integrating an emotion analysis engine, the server can analyze the user's emotions in real time and dynamically adjust the learning content and the behavior of the virtual teacher based on the results. For example, if a user looks tired while studying, the emotion analysis engine will recognize that emotion as "tired" and send it to the server. Based on this information, the server can provide content that is less difficult to learn or change the behavior of the virtual teacher to offer encouraging words.

[1034] Furthermore, the server manages the provision of virtual tutor services that can be customized for a fee, and presents additional content and customization options to the user as needed. The user can select these options to receive additional services. For example, the user can select a virtual tutor customization option for a fee and purchase dedicated additional content. The server generates this customization data and transmits it to the terminal, thereby providing a learning environment tailored to the user's needs.

[1035] As described above, by combining a generative AI model and a sentiment analysis engine, this system can provide a learning experience tailored to each student and improve the quality of education, thereby eliminating educational disparities and reducing the burden on teachers.

[1036] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1037] Program processing steps

[1038] Step 1: Loading the Data

[1039] The server loads student profile information and past academic performance data from a database.

[1040] Input: Student ID or user credentials.

[1041] Processing: The server uses SQL queries to retrieve student profile information and past academic performance data from the database and temporarily store them in memory.

[1042] Output: Loaded student profile information and academic performance data.

[1043] Specific operation: Parses data obtained from an SQL database server (e.g., MySQL, PostgreSQL) and loads it into memory.

[1044] Step 2: Generate ability test questions

[1045] The server generates academic ability check questions based on the loaded data.

[1046] Input: Loaded student profile information and academic data.

[1047] Processing: The server sends a prompt (e.g., "Generate math problems based on the student's past performance") to the generative AI model and formats the generated problems.

[1048] Output: The generated ability check questions.

[1049] Specific operation: Format the problem obtained from a generative AI model (e.g., GPT-4) in text format so that it can be used in the next step.

[1050] Step 3: Distribution of academic ability test questions

[1051] The server transmits the generated academic ability check questions to the terminal.

[1052] Input: Generated achievement check questions.

[1053] Processing: The server uses a REST API to send the generated questions to the terminal in JSON format.

[1054] Output: The academic ability test questions sent to the device.

[1055] Specific operation: Send data to the terminal using an HTTP request.

[1056] Step 4: Viewing the Academic Ability Test Questions

[1057] The terminal displays the academic ability check questions received from the server to the user (student).

[1058] Input: Academic ability check questions sent from the server.

[1059] Processing: The terminal uses HTML and JavaScript to generate a screen that displays the academic ability check questions to the user.

[1060] Output: The assessment question that is displayed to the user.

[1061] Specific operation: A web page is generated to display the academic ability check questions, and an interface is provided to allow the user to answer them.

[1062] Step 5: Receiving and sending responses

[1063] The user (student) answers the academic ability check questions and sends the answers to the server via the terminal.

[1064] Input: The answers to the assessment questions entered by the user.

[1065] Processing: When the user enters an answer into the terminal and presses the send button, the terminal sends the answer data in JSON format to the server.

[1066] Output: The response data sent to the server.

[1067] Specific operation: When the user enters an answer and clicks the submit button, the device sends the data to the server in JSON format.

[1068] Step 6: Analyze the answers

[1069] The server analyzes the received response data and evaluates the student's academic ability.

[1070] Input: User response data.

[1071] Processing: The server compares the received answer data with the correct answer data, generates an evaluation score, and stores it in a database.

[1072] Output: Student achievement assessment results.

[1073] Specific operation: The server compares the answers with the correct answer database, calculates the number of correct and incorrect answers, and generates an academic ability evaluation score.

[1074] Step 7: Auto-generate curriculum

[1075] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[1076] Input: Student academic assessment results.

[1077] Processing: Based on the academic assessment results, the server sends a prompt to the generative AI model saying, "Please generate content that will reinforce the student's weaknesses," and generates a curriculum.

[1078] Output: The generated curriculum.

[1079] Specific actions: Format the content obtained from the generative AI model and organize it into a curriculum.

[1080] Step 8: Generate and deliver learning content

[1081] The server creates learning content based on the generated curriculum and transmits it to the terminal.

[1082] Input: The generated curriculum.

[1083] Processing: The server sends the prompt "Please generate learning content based on the curriculum" to the generative AI model and sends the resulting content to the terminal.

[1084] Output: The learning content sent to the device.

[1085] Specific operation: The generated learning content is formatted appropriately and sent to the terminal via an HTTP request.

[1086] Step 9: View learning content

[1087] The terminal displays the generated learning content to the user (student).

[1088] Input: The learning content sent from the server.

[1089] Processing: The device uses HTML and JavaScript to generate a screen that displays the learning content.

[1090] Output: The learning content that is displayed to the user.

[1091] Specific operations: Generate a web page that displays learning content and allows users to learn using that content.

[1092] These are the specific processing steps of this system. At each step, processing is performed based on the input data, and the optimal result is output. This system can improve the quality of education and provide a learning experience that is tailored to each individual student.

[1093] (Application example 2)

[1094] 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."

[1095] While conventional autonomous learning systems support personalized learning based on each student's academic ability and progress, they are unable to take into account the student's emotions and condition, thereby failing to maximize learning effectiveness. Furthermore, while interactive learning methods are needed in actual educational settings to maintain student motivation and concentration, these methods still have limitations. Similar challenges are emerging when providing education and entertainment within autonomous vehicles.

[1096] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1097] In this invention, the server includes a means for checking students' academic ability, a means for automatically generating an appropriate curriculum based on the check results, and a means for generating learning content based on the curriculum. This enables individually optimized learning based on each student's academic ability and progress. Furthermore, the server includes a means for analyzing students' emotions and dynamically adjusting learning content based on the analysis results, and a means for visually providing learning content using smart glasses, enabling a learning experience that takes into account the student's condition and environment. Furthermore, learning support is possible using a virtual teacher within the metaverse, and interactive communication with students can maintain and improve their motivation and concentration.

[1098] "Means for checking academic ability" refers to devices or systems that ask questions to assess students' current academic ability and level of understanding, and then compile and analyze the results.

[1099] "Means for automatically generating a curriculum" refers to a device or system that automatically designs and creates optimal learning content and order based on the results of individual student academic ability checks and progress.

[1100] "Means for generating learning content" refers to devices or systems that create specific teaching materials, practice questions, explanatory materials, etc. based on the curriculum.

[1101] "Means for providing learning content" refers to devices or systems that display and provide the generated learning content to students in an appropriate manner.

[1102] "Means for managing learning progress and achievement" refers to devices or systems that record and analyze students' learning history, progress, and level of understanding, and provide appropriate feedback.

[1103] "Means for making necessary corrections" refers to devices or systems that adjust or correct the curriculum or learning content when there are problems with students' learning progress or level of understanding.

[1104] A "learning support means using a virtual teacher" is a device or system that uses a virtual teacher character in the metaverse to provide instruction to students and answer their questions.

[1105] "Means for analyzing emotions" refers to devices or systems that analyze students' facial expressions, voice, etc., and evaluate their current emotional state.

[1106] "Means for dynamically adjusting learning content" refers to devices or systems that change or adjust the learning content and difficulty level in real time based on the analyzed emotional results.

[1107] A "means for visually providing learning content using smart glasses" is a device or system that visually presents learning content via smart glasses and enhances students' learning experience.

[1108] "Means for providing learning content in animation format" refers to devices or systems that use animation to visually explain and present learning content so that it is easy for students to understand.

[1109] The "means for providing a customizable virtual teacher" refers to a device or system that provides a service that allows users to adjust and customize the appearance and behavior of a virtual teacher according to their requests.

[1110] This invention is an autonomous learning system that checks the academic ability of students, automatically generates an appropriate curriculum, and provides learning content. Each element will be described in detail below.

[1111] Academic ability check function

[1112] The server first loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user, and the user (student) answers these questions. The answer data is sent to the server, which analyzes the received answer data and evaluates the student's academic achievement.

[1113] Example: A student answers math problems on a device, and the answer data is sent to a server, which determines whether the answer is correct or incorrect and evaluates the student's level of understanding.

[1114] Automatic curriculum generation function

[1115] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The terminal displays the generated content to the user, providing optimal learning content.

[1116] Example: The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[1117] Learning progress management function

[1118] The device records the student's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[1119] Example: A device records a student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[1120] Sentiment analysis function

[1121] The server uses an emotion analysis engine to analyze students' emotions in real time. The device inputs the students' facial expressions and voices into the emotion analysis engine and sends the analysis results to the server. The server dynamically adjusts learning content and curriculum based on the emotion analysis.

[1122] For example, if a student looks tired while studying, the emotion engine will recognize the emotion as "tired" and send it to the server. Based on this information, the server will provide content that makes the learning easier or provide encouraging words from the virtual teacher to invigorate the student.

[1123] Content provision using smart glasses

[1124] The server provides selected learning content to students through smart glasses, which have built-in cameras and displays that allow students to enjoy a visual learning experience.

[1125] For example, a student wears smart glasses in a self-driving car and visually receives learning content in real time. When a difficult topic is presented, a sentiment analysis engine analyzes the student's reaction and simplifies the content displayed.

[1126] Virtual Teacher Function in the Metaverse

[1127] The server creates a virtual learning environment within the metaverse, with a customized virtual teacher for each student, who interacts with the student in real time to provide tailored instruction and feedback.

[1128] Example: A student puts on VR glasses and joins a virtual class in the metaverse. The server controls a virtual teacher who answers questions and provides guidance.

[1129] Prompt Sentence Examples

[1130] Develop a system that analyzes user emotions and dynamically changes educational content in real time. Design it to provide relaxing content when users feel fatigued.

[1131] As described above, this invention utilizes generative AI models and big data to provide individually optimized learning experiences tailored to each student's academic ability and emotions. Specific hardware includes a server, terminals, smart glasses, and VR glasses, while software includes TensorFlow, OpenCV, and an emotion analysis engine. This makes it possible to realize an innovative learning system that goes beyond conventional educational methods.

[1132] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1133] Step 1:

[1134] The server loads student profile information and past academic performance data from the database. The input is the student's personal information and past test data, and the output is student profile information. Based on this information, initial data for individual optimization is prepared.

[1135] Step 2:

[1136] The terminal displays the academic ability check questions received from the server to the user (student). The input is the check questions sent from the server, and the output is a quiz-style question displayed to the user. The user answers these questions through the terminal.

[1137] Step 3:

[1138] The user (student) answers the academic ability check questions and sends the answer data to the server via their terminal. The input is the user's answer data, and the output is the data sent to the server. This updates the user's current academic ability information.

[1139] Step 4:

[1140] The server analyzes the received response data and evaluates the user's (student's) academic ability. The input is the user's response data, and the output is the academic ability evaluation result. A generative AI model is used for the analysis to evaluate the accuracy rate and difficulty of the questions.

[1141] Step 5:

[1142] The server automatically generates an appropriate curriculum based on the results of academic assessment. The input is the academic assessment results, and the output is an individually optimized curriculum. The generated curriculum takes into account each student's strengths and weaknesses.

[1143] Step 6:

[1144] The server generates learning content based on the generated curriculum and sends it to the terminal. The input is the curriculum information, and the output is the learning content, which includes exercises, instructional videos, etc.

[1145] Step 7:

[1146] The terminal displays the generated learning content to the user (student), who then proceeds with the learning. The input is the learning content sent from the server, and the output is the user's learning activity.

[1147] Step 8:

[1148] The device records the student's learning progress and achievement in real time and periodically transmits it to the server. The input is the user's learning data, and the output is the data sent to the server. The learning progress is recorded in detail and the next step is prepared.

[1149] Step 9:

[1150] The server analyzes the collected learning progress data and proposes necessary corrections and next learning steps. The input is learning progress data, and the output is proposals and revised curriculum. This optimizes individual learning progress.

[1151] Step 10:

[1152] The server uses an emotion analysis engine to analyze students' emotions in real time. The input is the student's facial expressions and voice data, and the output is the emotional evaluation results. This analysis makes it possible to adjust according to the student's emotional state.

[1153] Step 11:

[1154] The server dynamically adjusts the learning content based on the emotion analysis results. The input is the emotion analysis results, and the output is the adjusted learning content. For example, if the user feels fatigued, the content may be lighter or an encouraging message may be displayed.

[1155] Step 12:

[1156] The device visually presents learning content using smart glasses. The input is content received from the server, and the output is content visually presented to the user, thereby visually enhancing the learning experience.

[1157] Step 13:

[1158] The server provides learning support using virtual teachers within the metaverse. The input is the user's learning data, and the output is guidance and feedback from the virtual teacher. Students can participate in virtual classes and receive direct guidance from the virtual teacher.

[1159] The above are the specific processing steps for carrying out the invention. At each step, input data is processed and analyzed to obtain optimal output, thereby realizing individually optimized learning for each student.

[1160] 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.

[1161] 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.

[1162] 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.

[1163] [Third embodiment]

[1164] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1165] 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.

[1166] 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).

[1167] 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.

[1168] 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.

[1169] 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).

[1170] 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.

[1171] 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.

[1172] 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.

[1173] 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.

[1174] 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.

[1175] 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."

[1176] This invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. The invention consists of the following main functions and steps.

[1177] Academic ability check function

[1178] First, the server loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user. The user (student) answers these questions and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic achievement.

[1179] (Example)

[1180] Students answer math problems on their devices, and the answers are sent to the server, which then determines whether the answers are correct and evaluates the student's level of math comprehension.

[1181] Personalize your learning content

[1182] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The device displays the generated content to the user, thereby providing optimal learning content for each student.

[1183] (Example)

[1184] The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[1185] Achievement and progress management

[1186] The device records the user's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[1187] (Example)

[1188] The device records the student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[1189] Anime-style content

[1190] The server generates learning content in the form of animation for students who are not good at reading texts, and the device displays this content to the user, allowing them to progress through their studies in a visually easy-to-understand format.

[1191] (Example)

[1192] The server generates animated content for multiplication for students who have difficulty understanding letters and sends it to the device, which displays it and allows students to learn by watching the animation.

[1193] The Metaverse and Virtual Teachers

[1194] The server generates a virtual learning environment in the metaverse and operates a virtual teacher for each user. The user (student) wears VR glasses, logs into the metaverse, and interacts with the virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[1195] (Example)

[1196] Students wear VR glasses and participate in a virtual class in the metaverse, while the server controls a virtual teacher who answers students' questions and provides instruction.

[1197] Paid services

[1198] The server manages the provision of paid customizable virtual tutor services and presents additional content and customization options to users as needed. Users select these options to receive additional services.

[1199] (Example)

[1200] The user selects a customization option for the virtual teacher and purchases additional content for a fee. The server generates this customization data and transmits it to the terminal.

[1201] The specific embodiment of the present invention has been described above. This system can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers.

[1202] The processing flow will be explained below.

[1203] Academic ability check function

[1204] Step 1:

[1205] The server loads student profile information and past academic performance data from a database.

[1206] Specific operation: Using an SQL query, retrieve student data corresponding to the user ID from the database.

[1207] Step 2:

[1208] The terminal displays the academic ability check questions received from the server to the user.

[1209] Specific operation: Renders problem data obtained from the server via an HTTP request on the screen using HTML and CSS.

[1210] Step 3:

[1211] The user (student) answers the academic ability check questions displayed on the terminal.

[1212] Specific actions: Enter answers using the form and click the submit button.

[1213] Step 4:

[1214] The terminal transmits the user's response data to the server.

[1215] Specific behavior: Uses JavaScript to convert form data into JSON format and sends it to the server via a POST request.

[1216] Step 5:

[1217] The server analyzes the received response data and evaluates the student's academic ability.

[1218] Specific operation: Calculates the accuracy rate and answer speed using scripts such as Python and Java, and generates an academic ability evaluation score.

[1219] Personalize your learning content

[1220] Step 1:

[1221] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[1222] Specific actions: Utilizing generative AI to formulate learning items and sequences and build a curriculum.

[1223] Step 2:

[1224] The server generates learning content based on the generated curriculum.

[1225] Specific operation: Generates multimedia content such as text, images, and videos and sends them to the device via API.

[1226] Step 3:

[1227] The terminal displays the study content sent from the server to the user.

[1228] What it does: Parses JSON data and renders it into UI components with HTML and CSS.

[1229] Achievement and progress management

[1230] Step 1:

[1231] The device records the user's learning progress and achievement in real time.

[1232] What it does: Uses JavaScript to track actions and saves the data to a real-time database.

[1233] Step 2:

[1234] The terminal transmits the recorded progress data to the server at regular intervals.

[1235] Specific operation: Using Ajax, progress data is sent to the server in batch processing.

[1236] Step 3:

[1237] The server analyzes the collected progress data and suggests next learning steps and necessary corrections.

[1238] What it does: It uses machine learning algorithms to analyze data and generate feedback scores and suggestions.

[1239] Step 4:

[1240] The terminal displays the feedback content received from the server to the user.

[1241] Specific behavior: Display the feedback data in the UI and notify the user using popups or notifications.

[1242] Anime-style content

[1243] Step 1:

[1244] The server generates animated learning content based on student profiles and academic data.

[1245] Specific operation: Use the animation generation engine to create animation content based on a scenario.

[1246] Step 2:

[1247] The server transmits the generated animation content to the terminal.

[1248] Specific operation: Upload the generated animation file to the content management system and send a link to the device.

[1249] Step 3:

[1250] The terminal displays the content in the form of an animation to the user.

[1251] Specific operation: Play the anime file using a video player.

[1252] The Metaverse and Virtual Teachers

[1253] Step 1:

[1254] The server generates a learning environment within the metaverse and customizes it for each user.

[1255] Specific actions: Perform 3D modeling and scene setup to prepare a personalized learning environment for each user.

[1256] Step 2:

[1257] Users (students) put on VR glasses and log in to the metaverse.

[1258] Specific operation: Enter authentication information and launch the VR application.

[1259] Step 3:

[1260] The server operates the virtual teacher and interacts with the user.

[1261] Specific behavior: Executes the avatar teacher script, provides instruction to students, and answers questions.

[1262] Step 4:

[1263] The device handles interactions within the metaverse and provides appropriate feedback to the user.

[1264] Specific Actions: Collect and analyze data in real time and take appropriate action within the 3D environment.

[1265] Paid services

[1266] Step 1:

[1267] The server presents the user with paid service options.

[1268] Specific operations: Generates information about fee plans and additional features for paid services and sends it to the device.

[1269] Step 2:

[1270] The user selects a paid service as needed and receives customization and educational materials provided by the server.

[1271] What it does: Complete the online subscription process and activate the selected services.

[1272] Step 3:

[1273] The terminal displays the selected paid service to the user.

[1274] Specific behavior: Displays access links and download options for paid content.

[1275] In this way, by explaining the specific operations at each processing step, the processing flow of the program for the autonomous learning service can be explained in detail.

[1276] Example 1

[1277] 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."

[1278] Traditional educational systems have struggled to provide personalized learning tailored to each student's learning progress and level of understanding. They also lacked appropriate learning support for students who struggled with writing, and real-time learning support in virtual environments was limited. Furthermore, the lack of real-time recording of learning progress and prompt, appropriate feedback led to problems that reduced learning effectiveness.

[1279] 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.

[1280] In this invention, the server includes a means for checking students' academic ability, a means for automatically generating an appropriate curriculum based on the check results, and a means for generating learning content based on the curriculum. This enables personalized learning based on each student's level of understanding. The server also includes a means for generating academic ability check questions using an AI model, a means for recording learning progress data in real time using a terminal, a means for providing learning content in animation format, and a means for accessing a virtual learning environment using a VR device. This enables appropriate learning support for students who are weak at writing and real-time learning support in a virtual environment, as well as real-time recording of learning progress and rapid feedback.

[1281] A "means for checking students' academic ability" is a system that measures students' academic ability by having students input answers and evaluating those answers.

[1282] The "means for automatically generating an appropriate curriculum based on the check results" is a system that analyzes the results of students' academic ability checks and generates the optimal learning plan for each individual student based on that.

[1283] The "means for generating curriculum-based learning content" is a system that creates specific learning materials and problem sets based on an automatically generated curriculum.

[1284] "Means to provide learning content in the most optimal form to students" refers to a system that displays and delivers learning content in accordance with each student's learning style and progress.

[1285] "Means for managing students' learning progress and achievement" refers to a system that records and analyzes students' learning progress and achievement in real time.

[1286] "Means for making necessary corrections according to learning progress" refers to a system that provides necessary adjustments and advice based on students' learning data.

[1287] "Learning support means using a virtual teacher in the metaverse" is a system in which a virtual teacher provides guidance and support to students in a virtual reality space.

[1288] "Means for generating academic ability check questions using an AI model" refers to a system that uses artificial intelligence to automatically generate questions to measure students' academic ability.

[1289] "Means for recording learning progress data in real time using a terminal" refers to a system that records learning progress in real time via the device used by the student.

[1290] "Means for providing learning content in animation format" refers to a system that provides learning content using animations that are visually easy for students to understand.

[1291] "Means for accessing a virtual learning environment using a VR device" refers to a system that uses a virtual reality device to access a virtual learning space and conduct learning there.

[1292] MODE FOR CARRYING OUT THE INVENTION

[1293] This invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. The following describes a specific implementation of this system.

[1294] Hardware and software used

[1295] 1. Server: Use the following cloud platforms:

[1296] Amazon Web Services (AWS)

[1297] Microsoft Azure

[1298] 2. Devices: Use the following devices and software:

[1299] Tablet devices, PCs

[1300] Chrome browser

[1301] 3. Database: The following database management systems are used:

[1302] MySQL

[1303] PostgreSQL

[1304] 4. AI model: The following generative AI model is used:

[1305] OpenAI GPT-4

[1306] 5. VR Devices: Use the following virtual reality devices:

[1307] Oculus Rift

[1308] HTC Vive

[1309] Program processing

[1310] Academic ability check function

[1311] The server loads the student's profile information and past academic performance data from the database. Specifically, it retrieves the student's name, age, past grades, etc. from the MySQL database. The server then uses the generative AI model to generate academic performance check questions and sends them to the device. For example, the following prompt sentence is input to the AI ​​model:

[1312] Generate five math problems appropriate for your students' ages.

[1313] The device displays academic ability check questions to the user. For example, a math problem is displayed on a tablet screen. The user (student) answers the question, for example, by entering "2 + 2 = 4." The device then sends the user's answer to the server. The server analyzes the answer data and evaluates the student's academic ability. The analysis method is to calculate the percentage of correct answers and quantify the level of understanding.

[1314] Personalize your learning content

[1315] The server automatically generates an appropriate curriculum based on the results of the academic ability check. This process uses a generative AI model to determine the "next thing to learn." The server then generates learning content based on the automatically generated curriculum and sends it to the device. The generated content can be in the form of a PDF file or video link. The device then displays the personalized learning content to the user.

[1316] Specifically, the generative AI model is fed with the following prompt sentence:

[1317] Generate practice content for math problems based on students' understanding.

[1318] Achievement and progress management

[1319] The device records the user's learning progress data in real time. For example, it records how long it took to solve each problem. The recorded data is sent to the server at the end of the learning session. The server analyzes the progress data and suggests the next learning step. For example, it may suggest, "Try a slightly more difficult problem."

[1320] A concrete example is the following prompt:

[1321] This week's study time was a total of 10 hours. Let's do our best next week!

[1322] Anime-style content

[1323] The server generates learning content in the form of animation for students who are not good at reading. The generated animation content includes video file formats and streaming links, and is sent to the terminal. The terminal displays this content to the user.

[1324] For example, use the following prompt:

[1325] Create an animation that visually explains the concept of multiplication.

[1326] The Metaverse and Virtual Teachers

[1327] The server generates a virtual learning environment in the metaverse, and users (students) log in wearing VR devices. The server operates the virtual teacher, and the device processes this interaction and provides appropriate feedback to the user. Learning support is provided in real time using the VR device.

[1328] Examples of prompt statements include:

[1329] Put on your VR glasses and move on to the next virtual experiment.

[1330] Paid services

[1331] The server presents paid customization options, and users (students and parents) select and purchase additional services. For example, payment can be made by credit card. The server generates customized additional content and sends it to the device.

[1332] A concrete example is the following prompt:

[1333] Use our paid service to get specialized instruction on applied math problems.

[1334] As described above, this system enables personalized learning based on each student's level of understanding, contributing to improving the quality of education.

[1335] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1336] Step 1:

[1337] The server loads student profile information and past academic performance data. Specifically, it retrieves information such as student name, age, and past grades from a MySQL database. The input is student identification information such as ID, and the output is this data. Data processing involves extracting the necessary information using database queries and formatting it into a structured data format.

[1338] Step 2:

[1339] The server uses a generative AI model to generate academic ability check questions. The input is information such as the student's age and academic level, and the output is the generated academic ability check questions. To process the data, the following prompt is input to the AI ​​model: "Generate five arithmetic problems appropriate for the student's age."

[1340] Step 3:

[1341] The server sends the generated academic ability check questions to the terminal. The input is the generated question data, and the output is the sent question data. For data processing, the question data is serialized in JSON format or similar and sent to the terminal over the network.

[1342] Step 4:

[1343] The terminal displays the academic ability check questions received from the server to the user. The input is the received question data, and the output is the question displayed on the user interface. Specifically, the terminal deserializes the question data and displays it on the screen.

[1344] Step 5:

[1345] The user answers the academic ability check questions. The input is the displayed question, and the output is the user's answer data. In concrete terms, the user inputs the answer on the terminal.

[1346] Step 6:

[1347] The terminal sends the user's answer to the server. The input is the user's answer data, and the output is the answer data to be sent to the server. For data processing, the answer data is serialized in JSON format or similar and sent to the server via the network.

[1348] Step 7:

[1349] The server analyzes the response data and evaluates the student's academic ability. The input is the user's response data, and the output is the academic ability evaluation result. Data processing involves determining whether the answer is correct or incorrect, and quantifying the accuracy rate and level of understanding.

[1350] Step 8:

[1351] The server automatically generates an appropriate curriculum based on the results of the academic ability check. The input is the academic ability assessment results, and the output is the generated curriculum. Data processing uses an AI model to determine what should be learned next.

[1352] Step 9:

[1353] The server generates learning content based on the generated curriculum and sends it to the terminal. The input is the curriculum data, and the output is the transmitted learning content. Data processing involves converting the learning content into PDF files, video links, etc., and sending them over the network.

[1354] Step 10:

[1355] The terminal displays personalized learning content to the user. The input is the transmitted learning content, and the output is the content displayed on the user interface. Specifically, the terminal deserializes the content into a display format and displays it.

[1356] Step 11:

[1357] The device records the user's learning progress data in real time. The input is the user's learning behavior, and the output is the recorded progress data. Specific operations include recording the start and end times of learning and the time spent on each problem.

[1358] Step 12:

[1359] The device sends the recorded progress data to the server. The input is the progress data, and the output is the data to be sent to the server. The data is processed by serializing the progress data in JSON format or similar and sending it over the network.

[1360] Step 13:

[1361] The server analyzes the progress data and proposes the next learning step. The input is the progress data and the output is the proposal. Data processing involves analyzing the learning progress and recommending what to learn next.

[1362] Step 14:

[1363] The server sends the proposal to the terminal. The input is the proposal, and the output is the data sent to the terminal. The proposal is serialized and sent over the network as data processing.

[1364] Step 15:

[1365] The terminal displays the suggestion to the user. The input is the suggestion, and the output is the suggestion displayed on the user interface. Specifically, the suggestion is deserialized and displayed on the screen.

[1366] (Application example 1)

[1367] 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."

[1368] In today's educational environment, it is difficult to use a uniform teaching method to accommodate students with diverse learning styles and levels of understanding, which can lead to a decline in educational effectiveness and a decrease in motivation to learn. Furthermore, providing optimal learning content to each student is difficult to do manually, increasing the burden on teachers. Furthermore, with the spread of distance education, there is a demand for methods to provide learning support at the same level as face-to-face classes.

[1369] 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.

[1370] In this invention, the server includes means for checking students' academic abilities, means for automatically generating an appropriate curriculum based on the check results, means for generating learning content based on the curriculum, means for providing the learning content in an optimal form to the student via a smartphone application, means for managing learning progress and achievement, means for making necessary corrections according to the learning progress, means for learning support using a virtual teacher in the metaverse, and means for providing real-time interaction with the virtual teacher. This provides an optimized learning experience for each student, improves the quality of education, reduces the burden on teachers, and makes it possible to provide support equivalent to face-to-face classes in a distance learning environment.

[1371] "Student" refers to a person engaged in a learning activity.

[1372] "Academic ability" is an indicator of the degree of acquisition of knowledge and skills in a particular academic field.

[1373] "Checking" refers to the act of evaluating and confirming academic ability, progress, and other status.

[1374] "Curriculum" refers to the entire educational content organized to achieve specific educational objectives.

[1375] "Automatic generation" refers to the process in which the system autonomously generates the necessary data and information without the need for manual operation.

[1376] "Learning content" refers to a collection of instructional materials or information provided to achieve a specific learning goal.

[1377] "Providing" refers to the act of distributing or supplying learning materials, services, etc. to learners.

[1378] "Progress" is a state that indicates how much progress has been made in learning activities.

[1379] "Degree of achievement" refers to the level that indicates the degree to which a set learning goal has been achieved.

[1380] "Management" refers to monitoring and controlling the status of progress and achievement, and taking appropriate measures.

[1381] "Correction" refers to correcting or improving an error or deficiency.

[1382] The "metaverse" refers to a virtual space built on the Internet where users can interact through their own avatars.

[1383] A "virtual teacher" refers to an avatar or AI system that teaches students in a virtual space in place of a real teacher.

[1384] "Smartphone application" refers to a software program that runs on a smartphone.

[1385] "Real-time" refers to a state in which a particular operation or process is performed immediately without delay.

[1386] "Interaction" refers to the flow of mutual communication and operations between a user and a system or avatar.

[1387] This invention is an educational support system that utilizes generative AI and big data, and is realized through a smartphone application, a server, and a metaverse environment. Specific embodiments of the system are described below.

[1388] Hardware and Software Configuration

[1389] Server: A high-performance server is used to manage user data, analyze academic ability, and create an automated curriculum using generative AI models. This server is a system equipped with a database, AI models, and APIs.

[1390] Smartphone: Functions as an interface for learners (users). A dedicated application is installed on the smartphone, and users use it to check their academic ability, access learning content, and manage their progress.

[1391] Metaverse environment: A system for providing learning support in a virtual space, allowing real-time interaction with a virtual teacher. This environment is accessed via VR devices.

[1392] System Operation

[1393] 1. Academic ability check

[1394] The server generates questions for an academic ability check based on the user's profile information and past academic ability data. The smartphone application presents these questions to the user and sends the user's answers to the server. The server analyzes the answer data and evaluates the user's academic ability. The evaluation results are used in the next step.

[1395] 2. Providing personalized learning content

[1396] Based on the results of the academic ability check, the server uses a generative AI model to automatically generate an optimal curriculum for the user. Based on that curriculum, specific learning content is created and sent to a smartphone application. The user can then use this content to progress through their studies via the application.

[1397] 3. Tracking learning progress and achievement

[1398] The smartphone application records the user's learning progress and achievement in real time. This data is periodically sent to the server, which analyzes it and suggests necessary corrections and next learning steps. The user receives feedback and continues learning.

[1399] 4. Learning Support in the Metaverse

[1400] The server operates a virtual teacher in the metaverse environment and realizes real-time interaction with the user. The user can log in to the metaverse using a VR device and receive guidance and answers to questions from the virtual teacher.

[1401] 5. Provision of paid services

[1402] The server also provides users with customization options and additional content for a fee, allowing them to customize their virtual teacher. This data is sent to a smartphone application, allowing users to enhance their learning with additional, specialized content.

[1403] Examples and prompts

[1404] For example, there is a system in which a user with user ID "12345" takes an academic ability check on their smartphone, sends the results to a server, and receives learning content appropriate for the user. The following is a prompt for this specific example system:

[1405] Please write Python code to have the user with user ID "12345" take an academic ability check on their smartphone, send the results to the server, and receive learning content appropriate for the user.

[1406] By inputting this prompt into a generative AI model, it is expected that appropriate code will be generated and used as part of the system.

[1407] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1408] Step 1:

[1409] The server loads the user's profile information and past academic achievement data from a database.

[1410] Input: User ID, user profile information in database and past academic achievement data.

[1411] Data manipulation and calculations: Performing database queries to retrieve required information and load user records.

[1412] Output: User profile information and past academic achievement data.

[1413] Step 2:

[1414] Based on the loaded data, the server automatically generates questions for academic ability tests using a generative AI model.

[1415] Input: User profile information and past academic achievement data.

[1416] Data processing and calculation: Generative AI models generate questions appropriate to current academic ability.

[1417] Output: A set of questions for academic ability check.

[1418] Step 3:

[1419] The terminal (smartphone) displays the academic ability test questions received from the server to the user.

[1420] Input: A set of questions for the academic ability check sent from the server.

[1421] Data processing and calculation: Converting received data into a display format and applying it to the user interface.

[1422] Output: Academic ability test questions displayed in the user interface.

[1423] Step 4:

[1424] The user answers the academic ability check questions through the terminal and transmits the answers to the server.

[1425] Input: User response data.

[1426] Data processing and calculation: Collect response data and send it to the server.

[1427] Output: Response data.

[1428] Step 5:

[1429] The server analyzes the received response data and evaluates the user's academic ability.

[1430] Input: User response data.

[1431] Data processing and calculation: Analyze the user's academic ability by applying algorithms to determine whether the answers are correct and to evaluate academic ability.

[1432] Output: Academic assessment results.

[1433] Step 6:

[1434] Based on the academic assessment results, the server uses a generative AI model to automatically generate the optimal curriculum for the user.

[1435] Input: Academic assessment results.

[1436] Data processing and computation: Using generative AI models to create curriculum.

[1437] Output: Personalized curriculum.

[1438] Step 7:

[1439] The server creates specific learning content based on the generated curriculum and transmits it to the terminal.

[1440] Enter: personalized curriculum.

[1441] Data processing and calculation: Generate teaching materials and content corresponding to each learning step.

[1442] Output: Learning content.

[1443] Step 8:

[1444] The terminal displays the learning content received from the server to the user.

[1445] Input: Learning content sent from the server.

[1446] Data processing and calculation: Converting received data into a display format and applying it to the user interface.

[1447] Output: Learning content displayed in a user interface

[1448] Step 9:

[1449] The device records the user's learning progress and achievement in real time and periodically transmits the data to the server.

[1450] Input: User learning activity data.

[1451] Data processing and calculation: Analyzes learning progress and achievement level and sends the data to the server.

[1452] Output: Progress data sent to the server.

[1453] Step 10:

[1454] The server analyzes the collected progress data and suggests corrections to the learning content and the next learning step.

[1455] Input: Learning progress and achievement data.

[1456] Data processing and calculations: Perform calculations based on progress data to determine necessary corrections and next steps.

[1457] Output: Suggested revisions to what you learned and suggested next steps.

[1458] Step 11:

[1459] Users wear VR devices and participate in virtual classes in the metaverse, while the server controls a virtual teacher who interacts with users in real time.

[1460] Input: User login data, Virtual Learning Environment data.

[1461] Data processing and computation: Controlling interactions in the virtual space and responding to user questions.

[1462] Output: A learning experience with a virtual teacher within the metaverse.

[1463] Step 12:

[1464] The server offers customizable virtual teachers and additional content options for a fee.

[1465] Input: User request data, customization options data.

[1466] Data Processing and Computing: Manage data related to paid services and generate customized virtual teachers and content.

[1467] Output: Customized virtual teacher data and additional content.

[1468] 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.

[1469] The present invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the quality of education can be further improved. An embodiment of the present invention is described in detail below.

[1470] Academic ability check function

[1471] First, the server loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user. The user (student) answers these questions and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic achievement.

[1472] (Example)

[1473] Students answer math problems on their devices, and the answers are sent to the server, which then determines whether the answers are correct and evaluates the student's level of math comprehension.

[1474] Personalize your learning content

[1475] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The device displays the generated content to the user, thereby providing optimal learning content for each student.

[1476] (Example)

[1477] The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[1478] Achievement and progress management

[1479] The device records the user's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[1480] (Example)

[1481] The device records the student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[1482] Anime-style content

[1483] The server generates learning content in the form of animation for students who are not good at reading texts, and the device displays this content to the user, allowing them to progress through their studies in a visually easy-to-understand format.

[1484] (Example)

[1485] The server generates animated content for multiplication for students who have difficulty understanding letters and sends it to the device, which displays it and allows students to learn by watching the animation.

[1486] The Metaverse and Virtual Teachers

[1487] The server generates a virtual learning environment in the metaverse and customizes it for each user. Users (students) wear VR glasses, log in to the metaverse, and interact with a virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[1488] (Example)

[1489] Students wear VR glasses and participate in a virtual class in the metaverse, while the server controls a virtual teacher who answers students' questions and provides instruction.

[1490] Emotion engine integration

[1491] The server uses an emotion engine to analyze the user's emotions in real time. The device inputs the user's facial expressions and voice into the emotion engine and sends the analysis results to the server. The server dynamically adjusts the learning content and the behavior of the virtual teacher based on the emotion analysis.

[1492] (Example)

[1493] If a user shows a tired expression while studying, the emotion engine recognizes the emotion as "fatigue" and sends it to the server. Based on this information, the server can provide content that is easier to study or change the behavior of the virtual teacher to offer encouraging words.

[1494] Paid services

[1495] The server manages the provision of paid customizable virtual tutor services and presents additional content and customization options to users as needed. Users select these options to receive additional services.

[1496] (Example)

[1497] The user selects a customization option for the virtual teacher and purchases additional content for a fee. The server generates this customization data and transmits it to the terminal.

[1498] A specific embodiment of the present invention has been described above. This system can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers. Furthermore, the integration of an emotion engine makes it possible to provide a more personalized learning experience.

[1499] The processing flow will be explained below.

[1500] Academic ability check function

[1501] Step 1:

[1502] The server loads student profile information and past academic performance data from a database.

[1503] Specific operation: Using an SQL query, retrieve student data corresponding to the user ID from the database.

[1504] Step 2:

[1505] The terminal displays the academic ability check questions received from the server to the user.

[1506] Specific operation: Renders problem data obtained from the server via an HTTP request on the screen using HTML and CSS.

[1507] Step 3:

[1508] The user (student) answers the academic ability check questions displayed on the terminal.

[1509] Specific actions: Enter answers using the form and click the submit button.

[1510] Step 4:

[1511] The terminal transmits the user's response data to the server.

[1512] Specific behavior: Uses JavaScript to convert form data into JSON format and sends it to the server via a POST request.

[1513] Step 5:

[1514] The server analyzes the received response data and evaluates the student's academic ability.

[1515] Specific operation: Calculates the accuracy rate and answer speed using scripts such as Python and Java, and generates an academic ability evaluation score.

[1516] Personalize your learning content

[1517] Step 1:

[1518] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[1519] Specific actions: Utilizing generative AI to formulate learning items and sequences and build a curriculum.

[1520] Step 2:

[1521] The server generates learning content based on the generated curriculum.

[1522] Specific operation: Generates multimedia content such as text, images, and videos and sends them to the device via API.

[1523] Step 3:

[1524] The terminal displays the study content sent from the server to the user.

[1525] What it does: Parses JSON data and renders it into UI components with HTML and CSS.

[1526] Achievement and progress management

[1527] Step 1:

[1528] The device records the user's learning progress and achievement in real time.

[1529] What it does: Uses JavaScript to track actions and saves the data to a real-time database.

[1530] Step 2:

[1531] The terminal transmits the recorded progress data to the server at regular intervals.

[1532] Specific operation: Using Ajax, progress data is sent to the server in batch processing.

[1533] Step 3:

[1534] The server analyzes the collected progress data and suggests next learning steps and necessary corrections.

[1535] What it does: It uses machine learning algorithms to analyze data and generate feedback scores and suggestions.

[1536] Step 4:

[1537] The terminal displays the feedback content received from the server to the user.

[1538] Specific behavior: Display the feedback data in the UI and notify the user using popups or notifications.

[1539] Anime-style content

[1540] Step 1:

[1541] The server generates animated learning content based on student profiles and academic data.

[1542] Specific operation: Use the animation generation engine to create animation content based on a scenario.

[1543] Step 2:

[1544] The server transmits the generated animation content to the terminal.

[1545] Specific operation: Upload the generated animation file to the content management system and send a link to the device.

[1546] Step 3:

[1547] The terminal displays the content in the form of an animation to the user.

[1548] Specific operation: Play the anime file using a video player.

[1549] The Metaverse and Virtual Teachers

[1550] Step 1:

[1551] The server generates a learning environment within the metaverse and customizes it for each user.

[1552] Specific actions: Perform 3D modeling and scene setup to prepare a personalized learning environment for each user.

[1553] Step 2:

[1554] Users (students) put on VR glasses and log in to the metaverse.

[1555] Specific operation: Enter authentication information and launch the VR application.

[1556] Step 3:

[1557] The server operates the virtual teacher and interacts with the user.

[1558] Specific behavior: Executes the avatar teacher script, provides instruction to students, and answers questions.

[1559] Step 4:

[1560] The device handles interactions within the metaverse and provides appropriate feedback to the user.

[1561] Specific Actions: Collect and analyze data in real time and take appropriate action within the 3D environment.

[1562] Emotion engine integration

[1563] Step 1:

[1564] The server uses an emotion engine to analyze the user's emotions in real time.

[1565] Specific operation: Using an emotion analysis algorithm, emotional information is extracted from the user's facial expressions and voice.

[1566] Step 2:

[1567] The device inputs the user's facial expressions and voice into an emotion engine and sends the analysis results to the server.

[1568] Specific operation: Uses camera and microphone devices to collect the user's facial expressions and voice data and send it to an analysis engine.

[1569] Step 3:

[1570] The server dynamically adjusts the learning content and the behavior of the virtual teacher based on sentiment analysis.

[1571] Specific actions: Based on the emotional data, a script is executed to adjust the difficulty of the learning content and the behavior of the virtual teacher appropriately.

[1572] Paid services

[1573] Step 1:

[1574] The server presents the user with paid service options.

[1575] Specific operations: Generates information about fee plans and additional features for paid services and sends it to the device.

[1576] Step 2:

[1577] The user selects a paid service as needed and receives customization and educational materials provided by the server.

[1578] What it does: Complete the online subscription process and activate the selected services.

[1579] Step 3:

[1580] The terminal displays the selected paid service to the user.

[1581] Specific behavior: Displays access links and download options for paid content.

[1582] This clarifies the operation of a system that integrates an autonomous learning service and an emotion engine by providing a detailed explanation of the specific operations and flow at each processing step.

[1583] Example 2

[1584] 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."

[1585] The current education system makes it difficult to provide a personalized learning environment that reflects each student's individual academic ability and progress. Furthermore, there is no system that dynamically adjusts learning content based on a student's emotions and level of understanding. As a result, the quality of education declines and problems arise, such as a decrease in motivation to learn. Furthermore, effective learning support is not provided for students who are weak at writing or who are falling behind in specific areas. Furthermore, learning support using virtual reality environments is insufficient, and there is no customizable system that can meet specific learning needs.

[1586] 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.

[1587] In this invention, the server includes means for checking students' academic abilities, means for automatically generating an appropriate curriculum based on the check results, means for generating learning content based on the curriculum, means for providing the learning content in an optimal form to the student, means for managing the student's learning progress and achievement level, means for making necessary corrections according to the learning progress, means for supporting learning using a virtual teacher in a virtual reality environment, and means for analyzing the student's emotions and dynamically adjusting the learning content and the behavior of the virtual teacher based on the analysis results. This makes it possible to provide an optimized learning experience for each student, increase their motivation to learn, and improve the quality of education.

[1588] A "means for checking academic ability" is a device or program that has the function of generating questions to evaluate students' academic ability and collecting and analyzing students' answers to those questions.

[1589] A "means for automatically generating a curriculum" is a device or program that has the function of automatically creating an optimal learning plan that meets the individual learning needs of each student based on the results of an academic ability check.

[1590] A "means for generating learning content" is a device or program that has the function of creating and providing specific learning materials and exercises based on the generated curriculum.

[1591] A "means for providing learning content" is a device or program that has the function of effectively displaying or delivering the generated learning content to students.

[1592] A "means for managing learning progress and achievement" is a device or program that has the function of recording and analyzing the progress of students' learning activities and the degree of goal achievement.

[1593] The "means for making necessary modifications according to learning progress" refers to a device or program that has the function of dynamically modifying and adjusting learning plans and teaching materials suitable for students based on learning progress data.

[1594] A "learning support means using a virtual teacher in a virtual reality environment" is a device or program that has the function of allowing a virtual teacher to provide real-time guidance and support to students in a virtual environment created using virtual reality technology.

[1595] "Means for analyzing students' emotions and dynamically adjusting learning content and the behavior of the virtual teacher based on the analysis results" refers to a device or program that uses an emotion analysis engine to evaluate students' emotional states in real time and automatically change the learning content and the behavior of the virtual teacher based on the evaluation results.

[1596] This invention is an autonomous learning system that utilizes generative AI models and big data to support learning in educational settings. This system checks students' academic ability in educational settings such as elementary and junior high schools and provides individually optimized learning content to improve students' motivation and learning efficiency. In addition, by combining it with a sentiment analysis engine, it is possible to provide a more individually optimized learning experience.

[1597] First, the server loads the student's profile information and past academic achievement data from a database. This data is managed using an SQL database server (e.g., MySQL, PostgreSQL), and the retrieved data is temporarily stored in memory. The server then sends a prompt to the generative AI model to generate academic achievement test questions based on the loaded data. An example of a prompt used in this case is, "Please generate math questions based on the student's past grades." For example, OpenAI's GPT-4 is used as the generative AI model.

[1598] Next, the server sends the generated academic ability check questions to the terminal. The terminal displays the received academic ability check questions to the user (student). At this time, the terminal uses HTML and JavaScript to generate a screen for displaying the questions. The user (student) answers the academic ability check questions displayed on the terminal and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic ability.

[1599] Once the academic ability assessment is complete, the server automatically generates an appropriate curriculum based on the results. This curriculum is tailored to each student's individual learning needs, and the server creates the curriculum by sending a prompt to the generative AI model: "Please generate content that will reinforce the student's weaknesses." The server then creates learning content based on the generated curriculum and sends it to the device. The device then displays the generated learning content to the user (student). This process makes it possible to provide learning content that is optimized for each individual student.

[1600] The system also has the function of managing students' learning progress and achievement. The device records the user's (student's) learning progress and achievement in real time and periodically sends the data to the server. The server analyzes the collected progress data and suggests the next learning step or necessary corrections. The server also generates feedback and provides it to the user via the device to support the student's learning.

[1601] Furthermore, the server has the ability to generate animated learning content for students who are not good at reading. The server sends a prompt to the generative AI model, such as "Please generate an explanation of multiplication in animated format," and sends the generated animated content to the device. The device displays this content, allowing students to progress through their studies in a visually easy-to-understand format.

[1602] In addition, the server has a learning support function using a virtual teacher in a virtual reality environment. Users (students) wear VR glasses and log in to the metaverse, where they interact with the virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[1603] By integrating an emotion analysis engine, the server can analyze the user's emotions in real time and dynamically adjust the learning content and the behavior of the virtual teacher based on the results. For example, if a user looks tired while studying, the emotion analysis engine will recognize that emotion as "tired" and send it to the server. Based on this information, the server can provide content that is less difficult to learn or change the behavior of the virtual teacher to offer encouraging words.

[1604] Furthermore, the server manages the provision of virtual tutor services that can be customized for a fee, and presents additional content and customization options to the user as needed. The user can select these options to receive additional services. For example, the user can select a virtual tutor customization option for a fee and purchase dedicated additional content. The server generates this customization data and transmits it to the terminal, thereby providing a learning environment tailored to the user's needs.

[1605] As described above, by combining a generative AI model and a sentiment analysis engine, this system can provide a learning experience tailored to each student and improve the quality of education, thereby eliminating educational disparities and reducing the burden on teachers.

[1606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1607] Program processing steps

[1608] Step 1: Loading the Data

[1609] The server loads student profile information and past academic performance data from a database.

[1610] Input: Student ID or user credentials.

[1611] Processing: The server uses SQL queries to retrieve student profile information and past academic performance data from the database and temporarily store them in memory.

[1612] Output: Loaded student profile information and academic performance data.

[1613] Specific operation: Parses data obtained from an SQL database server (e.g., MySQL, PostgreSQL) and loads it into memory.

[1614] Step 2: Generate ability test questions

[1615] The server generates academic ability check questions based on the loaded data.

[1616] Input: Loaded student profile information and academic data.

[1617] Processing: The server sends a prompt (e.g., "Generate math problems based on the student's past performance") to the generative AI model and formats the generated problems.

[1618] Output: The generated ability check questions.

[1619] Specific operation: Format the problem obtained from a generative AI model (e.g., GPT-4) in text format so that it can be used in the next step.

[1620] Step 3: Distribution of academic ability test questions

[1621] The server transmits the generated academic ability check questions to the terminal.

[1622] Input: Generated achievement check questions.

[1623] Processing: The server uses a REST API to send the generated questions to the terminal in JSON format.

[1624] Output: The academic ability test questions sent to the device.

[1625] Specific operation: Send data to the terminal using an HTTP request.

[1626] Step 4: Viewing the Academic Ability Test Questions

[1627] The terminal displays the academic ability check questions received from the server to the user (student).

[1628] Input: Academic ability check questions sent from the server.

[1629] Processing: The terminal uses HTML and JavaScript to generate a screen that displays the academic ability check questions to the user.

[1630] Output: The assessment question that is displayed to the user.

[1631] Specific operation: A web page is generated to display the academic ability check questions, and an interface is provided to allow the user to answer them.

[1632] Step 5: Receiving and sending responses

[1633] The user (student) answers the academic ability check questions and sends the answers to the server via the terminal.

[1634] Input: The answers to the assessment questions entered by the user.

[1635] Processing: When the user enters an answer into the terminal and presses the send button, the terminal sends the answer data in JSON format to the server.

[1636] Output: The response data sent to the server.

[1637] Specific operation: When the user enters an answer and clicks the submit button, the device sends the data to the server in JSON format.

[1638] Step 6: Analyze the answers

[1639] The server analyzes the received response data and evaluates the student's academic ability.

[1640] Input: User response data.

[1641] Processing: The server compares the received answer data with the correct answer data, generates an evaluation score, and stores it in a database.

[1642] Output: Student achievement assessment results.

[1643] Specific operation: The server compares the answers with the correct answer database, calculates the number of correct and incorrect answers, and generates an academic ability evaluation score.

[1644] Step 7: Auto-generate curriculum

[1645] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[1646] Input: Student academic assessment results.

[1647] Processing: Based on the academic assessment results, the server sends a prompt to the generative AI model saying, "Please generate content that will reinforce the student's weaknesses," and generates a curriculum.

[1648] Output: The generated curriculum.

[1649] Specific actions: Format the content obtained from the generative AI model and organize it into a curriculum.

[1650] Step 8: Generate and deliver learning content

[1651] The server creates learning content based on the generated curriculum and transmits it to the terminal.

[1652] Input: The generated curriculum.

[1653] Processing: The server sends the prompt "Please generate learning content based on the curriculum" to the generative AI model and sends the resulting content to the terminal.

[1654] Output: The learning content sent to the device.

[1655] Specific operation: The generated learning content is formatted appropriately and sent to the terminal via an HTTP request.

[1656] Step 9: View learning content

[1657] The terminal displays the generated learning content to the user (student).

[1658] Input: The learning content sent from the server.

[1659] Processing: The device uses HTML and JavaScript to generate a screen that displays the learning content.

[1660] Output: The learning content that is displayed to the user.

[1661] Specific operations: Generate a web page that displays learning content and allows users to learn using that content.

[1662] These are the specific processing steps of this system. At each step, processing is performed based on the input data, and the optimal result is output. This system can improve the quality of education and provide a learning experience that is tailored to each individual student.

[1663] (Application example 2)

[1664] 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."

[1665] While conventional autonomous learning systems support personalized learning based on each student's academic ability and progress, they are unable to take into account the student's emotions and condition, thereby failing to maximize learning effectiveness. Furthermore, while interactive learning methods are needed in actual educational settings to maintain student motivation and concentration, these methods still have limitations. Similar challenges are emerging when providing education and entertainment within autonomous vehicles.

[1666] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1667] In this invention, the server includes a means for checking students' academic ability, a means for automatically generating an appropriate curriculum based on the check results, and a means for generating learning content based on the curriculum. This enables individually optimized learning based on each student's academic ability and progress. Furthermore, the server includes a means for analyzing students' emotions and dynamically adjusting learning content based on the analysis results, and a means for visually providing learning content using smart glasses, enabling a learning experience that takes into account the student's condition and environment. Furthermore, learning support is possible using a virtual teacher within the metaverse, and interactive communication with students can maintain and improve their motivation and concentration.

[1668] "Means for checking academic ability" refers to devices or systems that ask questions to assess students' current academic ability and level of understanding, and then compile and analyze the results.

[1669] "Means for automatically generating a curriculum" refers to a device or system that automatically designs and creates optimal learning content and order based on the results of individual student academic ability checks and progress.

[1670] "Means for generating learning content" refers to devices or systems that create specific teaching materials, practice questions, explanatory materials, etc. based on the curriculum.

[1671] "Means for providing learning content" refers to devices or systems that display and provide the generated learning content to students in an appropriate manner.

[1672] "Means for managing learning progress and achievement" refers to devices or systems that record and analyze students' learning history, progress, and level of understanding, and provide appropriate feedback.

[1673] "Means for making necessary corrections" refers to devices or systems that adjust or correct the curriculum or learning content when there are problems with students' learning progress or level of understanding.

[1674] A "learning support means using a virtual teacher" is a device or system that uses a virtual teacher character in the metaverse to provide instruction to students and answer their questions.

[1675] "Means for analyzing emotions" refers to devices or systems that analyze students' facial expressions, voice, etc., and evaluate their current emotional state.

[1676] "Means for dynamically adjusting learning content" refers to devices or systems that change or adjust the learning content and difficulty level in real time based on the analyzed emotional results.

[1677] A "means for visually providing learning content using smart glasses" is a device or system that visually presents learning content via smart glasses and enhances students' learning experience.

[1678] "Means for providing learning content in animation format" refers to devices or systems that use animation to visually explain and present learning content so that it is easy for students to understand.

[1679] The "means for providing a customizable virtual teacher" refers to a device or system that provides a service that allows users to adjust and customize the appearance and behavior of a virtual teacher according to their requests.

[1680] This invention is an autonomous learning system that checks the academic ability of students, automatically generates an appropriate curriculum, and provides learning content. Each element will be described in detail below.

[1681] Academic ability check function

[1682] The server first loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user, and the user (student) answers these questions. The answer data is sent to the server, which analyzes the received answer data and evaluates the student's academic achievement.

[1683] Example: A student answers math problems on a device, and the answer data is sent to a server, which determines whether the answer is correct or incorrect and evaluates the student's level of understanding.

[1684] Automatic curriculum generation function

[1685] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The terminal displays the generated content to the user, providing optimal learning content.

[1686] Example: The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[1687] Learning progress management function

[1688] The device records the student's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[1689] Example: A device records a student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[1690] Sentiment analysis function

[1691] The server uses an emotion analysis engine to analyze students' emotions in real time. The device inputs the students' facial expressions and voices into the emotion analysis engine and sends the analysis results to the server. The server dynamically adjusts learning content and curriculum based on the emotion analysis.

[1692] For example, if a student looks tired while studying, the emotion engine will recognize the emotion as "tired" and send it to the server. Based on this information, the server will provide content that makes the learning easier or provide encouraging words from the virtual teacher to invigorate the student.

[1693] Content provision using smart glasses

[1694] The server provides selected learning content to students through smart glasses, which have built-in cameras and displays that allow students to enjoy a visual learning experience.

[1695] For example, a student wears smart glasses in a self-driving car and visually receives learning content in real time. When a difficult topic is presented, a sentiment analysis engine analyzes the student's reaction and simplifies the content displayed.

[1696] Virtual Teacher Function in the Metaverse

[1697] The server creates a virtual learning environment within the metaverse, with a customized virtual teacher for each student, who interacts with the student in real time to provide tailored instruction and feedback.

[1698] Example: A student puts on VR glasses and joins a virtual class in the metaverse. The server controls a virtual teacher who answers questions and provides guidance.

[1699] Prompt Sentence Examples

[1700] Develop a system that analyzes user emotions and dynamically changes educational content in real time. Design it to provide relaxing content when users feel fatigued.

[1701] As described above, this invention utilizes generative AI models and big data to provide individually optimized learning experiences tailored to each student's academic ability and emotions. Specific hardware includes a server, terminals, smart glasses, and VR glasses, while software includes TensorFlow, OpenCV, and an emotion analysis engine. This makes it possible to realize an innovative learning system that goes beyond conventional educational methods.

[1702] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1703] Step 1:

[1704] The server loads student profile information and past academic performance data from the database. The input is the student's personal information and past test data, and the output is student profile information. Based on this information, initial data for individual optimization is prepared.

[1705] Step 2:

[1706] The terminal displays the academic ability check questions received from the server to the user (student). The input is the check questions sent from the server, and the output is a quiz-style question displayed to the user. The user answers these questions through the terminal.

[1707] Step 3:

[1708] The user (student) answers the academic ability check questions and sends the answer data to the server via their terminal. The input is the user's answer data, and the output is the data sent to the server. This updates the user's current academic ability information.

[1709] Step 4:

[1710] The server analyzes the received response data and evaluates the user's (student's) academic ability. The input is the user's response data, and the output is the academic ability evaluation result. A generative AI model is used for the analysis to evaluate the accuracy rate and difficulty of the questions.

[1711] Step 5:

[1712] The server automatically generates an appropriate curriculum based on the results of academic assessment. The input is the academic assessment results, and the output is an individually optimized curriculum. The generated curriculum takes into account each student's strengths and weaknesses.

[1713] Step 6:

[1714] The server generates learning content based on the generated curriculum and sends it to the terminal. The input is the curriculum information, and the output is the learning content, which includes exercises, instructional videos, etc.

[1715] Step 7:

[1716] The terminal displays the generated learning content to the user (student), who then proceeds with the learning. The input is the learning content sent from the server, and the output is the user's learning activity.

[1717] Step 8:

[1718] The device records the student's learning progress and achievement in real time and periodically transmits it to the server. The input is the user's learning data, and the output is the data sent to the server. The learning progress is recorded in detail and the next step is prepared.

[1719] Step 9:

[1720] The server analyzes the collected learning progress data and proposes necessary corrections and next learning steps. The input is learning progress data, and the output is proposals and revised curriculum. This optimizes individual learning progress.

[1721] Step 10:

[1722] The server uses an emotion analysis engine to analyze students' emotions in real time. The input is the student's facial expressions and voice data, and the output is the emotional evaluation results. This analysis makes it possible to adjust according to the student's emotional state.

[1723] Step 11:

[1724] The server dynamically adjusts the learning content based on the emotion analysis results. The input is the emotion analysis results, and the output is the adjusted learning content. For example, if the user feels fatigued, the content may be lighter or an encouraging message may be displayed.

[1725] Step 12:

[1726] The device visually presents learning content using smart glasses. The input is content received from the server, and the output is content visually presented to the user, thereby visually enhancing the learning experience.

[1727] Step 13:

[1728] The server provides learning support using virtual teachers within the metaverse. The input is the user's learning data, and the output is guidance and feedback from the virtual teacher. Students can participate in virtual classes and receive direct guidance from the virtual teacher.

[1729] The above are the specific processing steps for carrying out the invention. At each step, input data is processed and analyzed to obtain optimal output, thereby realizing individually optimized learning for each student.

[1730] 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.

[1731] 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.

[1732] 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.

[1733] [Fourth embodiment]

[1734] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1735] 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.

[1736] 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).

[1737] 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.

[1738] 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.

[1739] 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).

[1740] 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.

[1741] 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.

[1742] 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.

[1743] 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.

[1744] 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.

[1745] 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.

[1746] 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."

[1747] This invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. The invention consists of the following main functions and steps.

[1748] Academic ability check function

[1749] First, the server loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user. The user (student) answers these questions and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic achievement.

[1750] (Example)

[1751] Students answer math problems on their devices, and the answers are sent to the server, which then determines whether the answers are correct and evaluates the student's level of math comprehension.

[1752] Personalize your learning content

[1753] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The device displays the generated content to the user, thereby providing optimal learning content for each student.

[1754] (Example)

[1755] The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[1756] Achievement and progress management

[1757] The device records the user's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[1758] (Example)

[1759] The device records the student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[1760] Anime-style content

[1761] The server generates learning content in the form of animation for students who are not good at reading texts, and the device displays this content to the user, allowing them to progress through their studies in a visually easy-to-understand format.

[1762] (Example)

[1763] The server generates animated content for multiplication for students who have difficulty understanding letters and sends it to the device, which displays it and allows students to learn by watching the animation.

[1764] The Metaverse and Virtual Teachers

[1765] The server generates a virtual learning environment in the metaverse and operates a virtual teacher for each user. The user (student) wears VR glasses, logs into the metaverse, and interacts with the virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[1766] (Example)

[1767] Students wear VR glasses and participate in a virtual class in the metaverse, while the server controls a virtual teacher who answers students' questions and provides instruction.

[1768] Paid services

[1769] The server manages the provision of paid customizable virtual tutor services and presents additional content and customization options to users as needed. Users select these options to receive additional services.

[1770] (Example)

[1771] The user selects a customization option for the virtual teacher and purchases additional content for a fee. The server generates this customization data and transmits it to the terminal.

[1772] The specific embodiment of the present invention has been described above. This system can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers.

[1773] The processing flow will be explained below.

[1774] Academic ability check function

[1775] Step 1:

[1776] The server loads student profile information and past academic performance data from a database.

[1777] Specific operation: Using an SQL query, retrieve student data corresponding to the user ID from the database.

[1778] Step 2:

[1779] The terminal displays the academic ability check questions received from the server to the user.

[1780] Specific operation: Renders problem data obtained from the server via an HTTP request on the screen using HTML and CSS.

[1781] Step 3:

[1782] The user (student) answers the academic ability check questions displayed on the terminal.

[1783] Specific actions: Enter answers using the form and click the submit button.

[1784] Step 4:

[1785] The terminal transmits the user's response data to the server.

[1786] Specific behavior: Uses JavaScript to convert form data into JSON format and sends it to the server via a POST request.

[1787] Step 5:

[1788] The server analyzes the received response data and evaluates the student's academic ability.

[1789] Specific operation: Calculates the accuracy rate and answer speed using scripts such as Python and Java, and generates an academic ability evaluation score.

[1790] Personalize your learning content

[1791] Step 1:

[1792] The server automatically generates an appropriate curriculum based on the results of the academic ability check.

[1793] Specific actions: Utilizing generative AI to formulate learning items and sequences and build a curriculum.

[1794] Step 2:

[1795] The server generates learning content based on the generated curriculum.

[1796] Specific operation: Generates multimedia content such as text, images, and videos and sends them to the device via API.

[1797] Step 3:

[1798] The terminal displays the study content sent from the server to the user.

[1799] What it does: Parses JSON data and renders it into UI components with HTML and CSS.

[1800] Achievement and progress management

[1801] Step 1:

[1802] The device records the user's learning progress and achievement in real time.

[1803] What it does: Uses JavaScript to track actions and saves the data to a real-time database.

[1804] Step 2:

[1805] The terminal transmits the recorded progress data to the server at regular intervals.

[1806] Specific operation: Using Ajax, progress data is sent to the server in batch processing.

[1807] Step 3:

[1808] The server analyzes the collected progress data and suggests next learning steps and necessary corrections.

[1809] What it does: It uses machine learning algorithms to analyze data and generate feedback scores and suggestions.

[1810] Step 4:

[1811] The terminal displays the feedback content received from the server to the user.

[1812] Specific behavior: Display the feedback data in the UI and notify the user using popups or notifications.

[1813] Anime-style content

[1814] Step 1:

[1815] The server generates animated learning content based on student profiles and academic data.

[1816] Specific operation: Use the animation generation engine to create animation content based on a scenario.

[1817] Step 2:

[1818] The server transmits the generated animation content to the terminal.

[1819] Specific operation: Upload the generated animation file to the content management system and send a link to the device.

[1820] Step 3:

[1821] The terminal displays the content in the form of an animation to the user.

[1822] Specific operation: Play the anime file using a video player.

[1823] The Metaverse and Virtual Teachers

[1824] Step 1:

[1825] The server generates a learning environment within the metaverse and customizes it for each user.

[1826] Specific actions: Perform 3D modeling and scene setup to prepare a personalized learning environment for each user.

[1827] Step 2:

[1828] Users (students) put on VR glasses and log in to the metaverse.

[1829] Specific operation: Enter authentication information and launch the VR application.

[1830] Step 3:

[1831] The server operates the virtual teacher and interacts with the user.

[1832] Specific behavior: Executes the avatar teacher script, provides instruction to students, and answers questions.

[1833] Step 4:

[1834] The device handles interactions within the metaverse and provides appropriate feedback to the user.

[1835] Specific Actions: Collect and analyze data in real time and take appropriate action within the 3D environment.

[1836] Paid services

[1837] Step 1:

[1838] The server presents the user with paid service options.

[1839] Specific operations: Generates information about fee plans and additional features for paid services and sends it to the device.

[1840] Step 2:

[1841] The user selects a paid service as needed and receives customization and educational materials provided by the server.

[1842] What it does: Complete the online subscription process and activate the selected services.

[1843] Step 3:

[1844] The terminal displays the selected paid service to the user.

[1845] Specific behavior: Displays access links and download options for paid content.

[1846] In this way, by explaining the specific operations at each processing step, the processing flow of the program for the autonomous learning service can be explained in detail.

[1847] Example 1

[1848] 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."

[1849] Traditional educational systems have struggled to provide personalized learning tailored to each student's learning progress and level of understanding. They also lacked appropriate learning support for students who struggled with writing, and real-time learning support in virtual environments was limited. Furthermore, the lack of real-time recording of learning progress and prompt, appropriate feedback led to problems that reduced learning effectiveness.

[1850] 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.

[1851] In this invention, the server includes a means for checking students' academic ability, a means for automatically generating an appropriate curriculum based on the check results, and a means for generating learning content based on the curriculum. This enables personalized learning based on each student's level of understanding. The server also includes a means for generating academic ability check questions using an AI model, a means for recording learning progress data in real time using a terminal, a means for providing learning content in animation format, and a means for accessing a virtual learning environment using a VR device. This enables appropriate learning support for students who are weak at writing and real-time learning support in a virtual environment, as well as real-time recording of learning progress and rapid feedback.

[1852] A "means for checking students' academic ability" is a system that measures students' academic ability by having students input answers and evaluating those answers.

[1853] The "means for automatically generating an appropriate curriculum based on the check results" is a system that analyzes the results of students' academic ability checks and generates the optimal learning plan for each individual student based on that.

[1854] The "means for generating curriculum-based learning content" is a system that creates specific learning materials and problem sets based on an automatically generated curriculum.

[1855] "Means to provide learning content in the most optimal form to students" refers to a system that displays and delivers learning content in accordance with each student's learning style and progress.

[1856] "Means for managing students' learning progress and achievement" refers to a system that records and analyzes students' learning progress and achievement in real time.

[1857] "Means for making necessary corrections according to learning progress" refers to a system that provides necessary adjustments and advice based on students' learning data.

[1858] "Learning support means using a virtual teacher in the metaverse" is a system in which a virtual teacher provides guidance and support to students in a virtual reality space.

[1859] "Means for generating academic ability check questions using an AI model" refers to a system that uses artificial intelligence to automatically generate questions to measure students' academic ability.

[1860] "Means for recording learning progress data in real time using a terminal" refers to a system that records learning progress in real time via the device used by the student.

[1861] "Means for providing learning content in animation format" refers to a system that provides learning content using animations that are visually easy for students to understand.

[1862] "Means for accessing a virtual learning environment using a VR device" refers to a system that uses a virtual reality device to access a virtual learning space and conduct learning there.

[1863] MODE FOR CARRYING OUT THE INVENTION

[1864] This invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. The following describes a specific implementation of this system.

[1865] Hardware and software used

[1866] 1. Server: Use the following cloud platforms:

[1867] Amazon Web Services (AWS)

[1868] Microsoft Azure

[1869] 2. Devices: Use the following devices and software:

[1870] Tablet devices, PCs

[1871] Chrome browser

[1872] 3. Database: The following database management systems are used:

[1873] MySQL

[1874] PostgreSQL

[1875] 4. AI model: The following generative AI model is used:

[1876] OpenAI GPT-4

[1877] 5. VR Devices: Use the following virtual reality devices:

[1878] Oculus Rift

[1879] HTC Vive

[1880] Program processing

[1881] Academic ability check function

[1882] The server loads the student's profile information and past academic performance data from the database. Specifically, it retrieves the student's name, age, past grades, etc. from the MySQL database. The server then uses the generative AI model to generate academic performance check questions and sends them to the device. For example, the following prompt sentence is input to the AI ​​model:

[1883] Generate five math problems appropriate for your students' ages.

[1884] The device displays academic ability check questions to the user. For example, a math problem is displayed on a tablet screen. The user (student) answers the question, for example, by entering "2 + 2 = 4." The device then sends the user's answer to the server. The server analyzes the answer data and evaluates the student's academic ability. The analysis method is to calculate the percentage of correct answers and quantify the level of understanding.

[1885] Personalize your learning content

[1886] The server automatically generates an appropriate curriculum based on the results of the academic ability check. This process uses a generative AI model to determine the "next thing to learn." The server then generates learning content based on the automatically generated curriculum and sends it to the device. The generated content can be in the form of a PDF file or video link. The device then displays the personalized learning content to the user.

[1887] Specifically, the generative AI model is fed with the following prompt sentence:

[1888] Generate practice content for math problems based on students' understanding.

[1889] Achievement and progress management

[1890] The device records the user's learning progress data in real time. For example, it records how long it took to solve each problem. The recorded data is sent to the server at the end of the learning session. The server analyzes the progress data and suggests the next learning step. For example, it may suggest, "Try a slightly more difficult problem."

[1891] A concrete example is the following prompt:

[1892] This week's study time was a total of 10 hours. Let's do our best next week!

[1893] Anime-style content

[1894] The server generates learning content in the form of animation for students who are not good at reading. The generated animation content includes video file formats and streaming links, and is sent to the terminal. The terminal displays this content to the user.

[1895] For example, use the following prompt:

[1896] Create an animation that visually explains the concept of multiplication.

[1897] The Metaverse and Virtual Teachers

[1898] The server generates a virtual learning environment in the metaverse, and users (students) log in wearing VR devices. The server operates the virtual teacher, and the device processes this interaction and provides appropriate feedback to the user. Learning support is provided in real time using the VR device.

[1899] Examples of prompt statements include:

[1900] Put on your VR glasses and move on to the next virtual experiment.

[1901] Paid services

[1902] The server presents paid customization options, and users (students and parents) select and purchase additional services. For example, payment can be made by credit card. The server generates customized additional content and sends it to the device.

[1903] A concrete example is the following prompt:

[1904] Use our paid service to get specialized instruction on applied math problems.

[1905] As described above, this system enables personalized learning based on each student's level of understanding, contributing to improving the quality of education.

[1906] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1907] Step 1:

[1908] The server loads student profile information and past academic performance data. Specifically, it retrieves information such as student name, age, and past grades from a MySQL database. The input is student identification information such as ID, and the output is this data. Data processing involves extracting the necessary information using database queries and formatting it into a structured data format.

[1909] Step 2:

[1910] The server uses a generative AI model to generate academic ability check questions. The input is information such as the student's age and academic level, and the output is the generated academic ability check questions. To process the data, the following prompt is input to the AI ​​model: "Generate five arithmetic problems appropriate for the student's age."

[1911] Step 3:

[1912] The server sends the generated academic ability check questions to the terminal. The input is the generated question data, and the output is the sent question data. For data processing, the question data is serialized in JSON format or similar and sent to the terminal over the network.

[1913] Step 4:

[1914] The terminal displays the academic ability check questions received from the server to the user. The input is the received question data, and the output is the question displayed on the user interface. Specifically, the terminal deserializes the question data and displays it on the screen.

[1915] Step 5:

[1916] The user answers the academic ability check questions. The input is the displayed question, and the output is the user's answer data. In concrete terms, the user inputs the answer on the terminal.

[1917] Step 6:

[1918] The terminal sends the user's answer to the server. The input is the user's answer data, and the output is the answer data to be sent to the server. For data processing, the answer data is serialized in JSON format or similar and sent to the server via the network.

[1919] Step 7:

[1920] The server analyzes the response data and evaluates the student's academic ability. The input is the user's response data, and the output is the academic ability evaluation result. Data processing involves determining whether the answer is correct or incorrect, and quantifying the accuracy rate and level of understanding.

[1921] Step 8:

[1922] The server automatically generates an appropriate curriculum based on the results of the academic ability check. The input is the academic ability assessment results, and the output is the generated curriculum. Data processing uses an AI model to determine what should be learned next.

[1923] Step 9:

[1924] The server generates learning content based on the generated curriculum and sends it to the terminal. The input is the curriculum data, and the output is the transmitted learning content. Data processing involves converting the learning content into PDF files, video links, etc., and sending them over the network.

[1925] Step 10:

[1926] The terminal displays personalized learning content to the user. The input is the transmitted learning content, and the output is the content displayed on the user interface. Specifically, the terminal deserializes the content into a display format and displays it.

[1927] Step 11:

[1928] The device records the user's learning progress data in real time. The input is the user's learning behavior, and the output is the recorded progress data. Specific operations include recording the start and end times of learning and the time spent on each problem.

[1929] Step 12:

[1930] The device sends the recorded progress data to the server. The input is the progress data, and the output is the data to be sent to the server. The data is processed by serializing the progress data in JSON format or similar and sending it over the network.

[1931] Step 13:

[1932] The server analyzes the progress data and proposes the next learning step. The input is the progress data and the output is the proposal. Data processing involves analyzing the learning progress and recommending what to learn next.

[1933] Step 14:

[1934] The server sends the proposal to the terminal. The input is the proposal, and the output is the data sent to the terminal. The proposal is serialized and sent over the network as data processing.

[1935] Step 15:

[1936] The terminal displays the suggestion to the user. The input is the suggestion, and the output is the suggestion displayed on the user interface. Specifically, the suggestion is deserialized and displayed on the screen.

[1937] (Application example 1)

[1938] 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."

[1939] In today's educational environment, it is difficult to use a uniform teaching method to accommodate students with diverse learning styles and levels of understanding, which can lead to a decline in educational effectiveness and a decrease in motivation to learn. Furthermore, providing optimal learning content to each student is difficult to do manually, increasing the burden on teachers. Furthermore, with the spread of distance education, there is a demand for methods to provide learning support at the same level as face-to-face classes.

[1940] 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.

[1941] In this invention, the server includes means for checking students' academic abilities, means for automatically generating an appropriate curriculum based on the check results, means for generating learning content based on the curriculum, means for providing the learning content in an optimal form to the student via a smartphone application, means for managing learning progress and achievement, means for making necessary corrections according to the learning progress, means for learning support using a virtual teacher in the metaverse, and means for providing real-time interaction with the virtual teacher. This provides an optimized learning experience for each student, improves the quality of education, reduces the burden on teachers, and makes it possible to provide support equivalent to face-to-face classes in a distance learning environment.

[1942] "Student" refers to a person engaged in a learning activity.

[1943] "Academic ability" is an indicator of the degree of acquisition of knowledge and skills in a particular academic field.

[1944] "Checking" refers to the act of evaluating and confirming academic ability, progress, and other status.

[1945] "Curriculum" refers to the entire educational content organized to achieve specific educational objectives.

[1946] "Automatic generation" refers to the process in which the system autonomously generates the necessary data and information without the need for manual operation.

[1947] "Learning content" refers to a collection of instructional materials or information provided to achieve a specific learning goal.

[1948] "Providing" refers to the act of distributing or supplying learning materials, services, etc. to learners.

[1949] "Progress" is a state that indicates how much progress has been made in learning activities.

[1950] "Degree of achievement" refers to the level that indicates the degree to which a set learning goal has been achieved.

[1951] "Management" refers to monitoring and controlling the status of progress and achievement, and taking appropriate measures.

[1952] "Correction" refers to correcting or improving an error or deficiency.

[1953] The "metaverse" refers to a virtual space built on the Internet where users can interact through their own avatars.

[1954] A "virtual teacher" refers to an avatar or AI system that teaches students in a virtual space in place of a real teacher.

[1955] "Smartphone application" refers to a software program that runs on a smartphone.

[1956] "Real-time" refers to a state in which a particular operation or process is performed immediately without delay.

[1957] "Interaction" refers to the flow of mutual communication and operations between a user and a system or avatar.

[1958] This invention is an educational support system that utilizes generative AI and big data, and is realized through a smartphone application, a server, and a metaverse environment. Specific embodiments of the system are described below.

[1959] Hardware and Software Configuration

[1960] Server: A high-performance server is used to manage user data, analyze academic ability, and create an automated curriculum using generative AI models. This server is a system equipped with a database, AI models, and APIs.

[1961] Smartphone: Functions as an interface for learners (users). A dedicated application is installed on the smartphone, and users use it to check their academic ability, access learning content, and manage their progress.

[1962] Metaverse environment: A system for providing learning support in a virtual space, allowing real-time interaction with a virtual teacher. This environment is accessed via VR devices.

[1963] System Operation

[1964] 1. Academic ability check

[1965] The server generates questions for an academic ability check based on the user's profile information and past academic ability data. The smartphone application presents these questions to the user and sends the user's answers to the server. The server analyzes the answer data and evaluates the user's academic ability. The evaluation results are used in the next step.

[1966] 2. Providing personalized learning content

[1967] Based on the results of the academic ability check, the server uses a generative AI model to automatically generate an optimal curriculum for the user. Based on that curriculum, specific learning content is created and sent to a smartphone application. The user can then use this content to progress through their studies via the application.

[1968] 3. Tracking learning progress and achievement

[1969] The smartphone application records the user's learning progress and achievement in real time. This data is periodically sent to the server, which analyzes it and suggests necessary corrections and next learning steps. The user receives feedback and continues learning.

[1970] 4. Learning Support in the Metaverse

[1971] The server operates a virtual teacher in the metaverse environment and realizes real-time interaction with the user. The user can log in to the metaverse using a VR device and receive guidance and answers to questions from the virtual teacher.

[1972] 5. Provision of paid services

[1973] The server also provides users with customization options and additional content for a fee, allowing them to customize their virtual teacher. This data is sent to a smartphone application, allowing users to enhance their learning with additional, specialized content.

[1974] Examples and prompts

[1975] For example, there is a system in which a user with user ID "12345" takes an academic ability check on their smartphone, sends the results to a server, and receives learning content appropriate for the user. The following is a prompt for this specific example system:

[1976] Please write Python code to have the user with user ID "12345" take an academic ability check on their smartphone, send the results to the server, and receive learning content appropriate for the user.

[1977] By inputting this prompt into a generative AI model, it is expected that appropriate code will be generated and used as part of the system.

[1978] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1979] Step 1:

[1980] The server loads the user's profile information and past academic achievement data from a database.

[1981] Input: User ID, user profile information in database and past academic achievement data.

[1982] Data manipulation and calculations: Performing database queries to retrieve required information and load user records.

[1983] Output: User profile information and past academic achievement data.

[1984] Step 2:

[1985] Based on the loaded data, the server automatically generates questions for academic ability tests using a generative AI model.

[1986] Input: User profile information and past academic achievement data.

[1987] Data processing and calculation: Generative AI models generate questions appropriate to current academic ability.

[1988] Output: A set of questions for academic ability check.

[1989] Step 3:

[1990] The terminal (smartphone) displays the academic ability test questions received from the server to the user.

[1991] Input: A set of questions for the academic ability check sent from the server.

[1992] Data processing and calculation: Converting received data into a display format and applying it to the user interface.

[1993] Output: Academic ability test questions displayed in the user interface.

[1994] Step 4:

[1995] The user answers the academic ability check questions through the terminal and transmits the answers to the server.

[1996] Input: User response data.

[1997] Data processing and calculation: Collect response data and send it to the server.

[1998] Output: Response data.

[1999] Step 5:

[2000] The server analyzes the received response data and evaluates the user's academic ability.

[2001] Input: User response data.

[2002] Data processing and calculation: Analyze the user's academic ability by applying algorithms to determine whether the answers are correct and to evaluate academic ability.

[2003] Output: Academic assessment results.

[2004] Step 6:

[2005] Based on the academic assessment results, the server uses a generative AI model to automatically generate the optimal curriculum for the user.

[2006] Input: Academic assessment results.

[2007] Data processing and computation: Using generative AI models to create curriculum.

[2008] Output: Personalized curriculum.

[2009] Step 7:

[2010] The server creates specific learning content based on the generated curriculum and transmits it to the terminal.

[2011] Enter: personalized curriculum.

[2012] Data processing and calculation: Generate teaching materials and content corresponding to each learning step.

[2013] Output: Learning content.

[2014] Step 8:

[2015] The terminal displays the learning content received from the server to the user.

[2016] Input: Learning content sent from the server.

[2017] Data processing and calculation: Converting received data into a display format and applying it to the user interface.

[2018] Output: Learning content displayed in a user interface

[2019] Step 9:

[2020] The device records the user's learning progress and achievement in real time and periodically transmits the data to the server.

[2021] Input: User learning activity data.

[2022] Data processing and calculation: Analyzes learning progress and achievement level and sends the data to the server.

[2023] Output: Progress data sent to the server.

[2024] Step 10:

[2025] The server analyzes the collected progress data and suggests corrections to the learning content and the next learning step.

[2026] Input: Learning progress and achievement data.

[2027] Data processing and calculations: Perform calculations based on progress data to determine necessary corrections and next steps.

[2028] Output: Suggested revisions to what you learned and suggested next steps.

[2029] Step 11:

[2030] Users wear VR devices and participate in virtual classes in the metaverse, while the server controls a virtual teacher who interacts with users in real time.

[2031] Input: User login data, Virtual Learning Environment data.

[2032] Data processing and computation: Controlling interactions in the virtual space and responding to user questions.

[2033] Output: A learning experience with a virtual teacher within the metaverse.

[2034] Step 12:

[2035] The server offers customizable virtual teachers and additional content options for a fee.

[2036] Input: User request data, customization options data.

[2037] Data Processing and Computing: Manage data related to paid services and generate customized virtual teachers and content.

[2038] Output: Customized virtual teacher data and additional content.

[2039] 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.

[2040] The present invention is an autonomous learning system that utilizes generative AI and big data. This system is designed to solve various problems in educational settings such as elementary and junior high schools. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the quality of education can be further improved. An embodiment of the present invention is described in detail below.

[2041] Academic ability check function

[2042] First, the server loads the student's profile information and past academic achievement data from the database. The terminal displays the academic achievement check questions received from the server to the user. The user (student) answers these questions and sends the answers to the server via the terminal. The server analyzes the received answer data and evaluates the student's academic achievement.

[2043] (Example)

[2044] Students answer math problems on their devices, and the answers are sent to the server, which then determines whether the answers are correct and evaluates the student's level of math comprehension.

[2045] Personalize your learning content

[2046] The server automatically generates an appropriate curriculum based on the results of the academic ability check, and generates learning content based on that curriculum. The device displays the generated content to the user, thereby providing optimal learning content for each student.

[2047] (Example)

[2048] The server generates and sends practice content for weak points, such as calculation problems, based on the student's level of understanding. The device displays this content, and the student uses it to advance their studies.

[2049] Achievement and progress management

[2050] The device records the user's learning progress and achievement in real time. The server analyzes the collected progress data, suggests next learning steps and necessary corrections, and generates and provides feedback to the user.

[2051] (Example)

[2052] The device records the student's learning progress and periodically sends the data to a server, which analyzes the data and suggests further learning content.

[2053] Anime-style content

[2054] The server generates learning content in the form of animation for students who are not good at reading texts, and the device displays this content to the user, allowing them to progress through their studies in a visually easy-to-understand format.

[2055] (Example)

[2056] The server generates animated content for multiplication for students who have difficulty understanding letters and sends it to the device, which displays it and allows students to learn by watching the animation.

[2057] The Metaverse and Virtual Teachers

[2058] The server generates a virtual learning environment in the metaverse and customizes it for each user. Users (students) wear VR glasses, log in to the metaverse, and interact with a virtual teacher in real time. The device processes this interaction and provides appropriate feedback to the user.

[2059] (Example)

[2060] Students wear VR glasses and participate in a virtual class in the metaverse, while the server controls a virtual teacher who answers students' questions and provides instruction.

[2061] Emotion engine integration

[2062] The server uses an emotion engine to analyze the user's emotions in real time. The device inputs the user's facial expressions and voice into the emotion engine and sends the analysis results to the server. The server dynamically adjusts the learning content and the behavior of the virtual teacher based on the emotion analysis.

[2063] (Example)

[2064] If a user shows a tired expression while studying, the emotion engine recognizes the emotion as "fatigue" and sends it to the server. Based on this information, the server can provide content that is easier to study or change the behavior of the virtual teacher to offer encouraging words.

[2065] Paid services

[2066] The server manages the provision of paid customizable virtual tutor services and presents additional content and customization options to users as needed. Users select these options to receive additional services.

[2067] (Example)

[2068] The user selects a customization option for the virtual teacher and purchases additional content for a fee. The server generates this customization data and transmits it to the terminal.

[2069] A specific embodiment of the present invention has been described above. This system can improve the quality of education, eliminate educational disparities, and reduce the burden on teachers. Furthermore, the integration of an emotion engine makes it possible to provide a more personalized learning experience.

[2070] The processing flow will be explained below.

[2071] Academic ability check function

[2072] Step 1:

[2073] The server loads student profile information and past academic performance data from a datab...

Claims

1. A way to check students' academic ability, A method for automatically generating an appropriate curriculum based on the check results, and a means for generating curriculum-based learning content; A means to deliver learning content to students in the most optimal way, and a means of managing students' learning progress and achievement; A means to make necessary corrections as learning progresses; A learning support method using a virtual teacher in the metaverse; A system including:

2. The system of claim 1 , further comprising means for providing animated learning content to students who have difficulty with writing.

3. The system of claim 1 further comprising a means for providing a customizable virtual teacher for a fee.

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