system

Abstract

The system according to this embodiment aims to support learning by creating an environment in which students can effectively use personal computers. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an environment construction unit, an initial setup unit, and a source code provision unit. The reception unit receives prompts to understand what the student "wants to do". The analysis unit analyzes the information based on the prompts received by the reception unit. The environment construction unit constructs the environment based on the information analyzed by the analysis unit. The initial setup unit performs the initial setup of the environment constructed by the environment construction unit. The source code provision unit provides specific source code in the environment that has been initially set up by the initial setup unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to construct an environment and perform initial settings for students to effectively use a personal computer, and learning support is not sufficiently provided.

[0005] The system according to the embodiment aims to construct an environment in which students can effectively use a personal computer and support learning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an environment construction unit, an initial setup unit, and a source code provision unit. The reception unit receives prompts to understand what the student "wants to do." The analysis unit analyzes the information based on the prompts received by the reception unit. The environment construction unit constructs the environment based on the information analyzed by the analysis unit. The initial setup unit performs the initial setup of the environment constructed by the environment construction unit. The source code provision unit provides specific source code in the environment that has been initially set up by the initial setup unit. [Effects of the Invention]

[0007] The system according to this embodiment can create an environment in which students can effectively use personal computers and support their learning. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system for effectively utilizing personal computers provided by schools for after-school learning and other purposes. This learning support system addresses conventional problems such as not knowing how to use a computer, being unable to participate in ICT workshops, and the high cost and unavailability of desired software. It utilizes generative AI and autonomous agents to provide consistent information tailored to what students want to achieve. For example, the learning support system uses generative AI to understand what students "want to do" and, based on that, builds the necessary environment and performs initial setup. Next, the learning support system provides specific source code and advanced information such as game creation, music, and graphics. This allows students to progress in their learning through a virtual environment. Furthermore, the learning support system creates personalized agent teachers for each student and provides a subscription-based virtual environment, enabling them to use all necessary tools in one place. For example, the learning support system allows students to learn not only simple programming but also more advanced information. Additionally, the learning support system improves learning efficiency through an AI teacher that provides consistent services from environment construction to initial setup and provision of specific source code. Moreover, by utilizing a subscription-based virtual environment, the learning support system eliminates the need to purchase expensive software, reducing the financial burden. This allows the learning support system to effectively assist students' learning.

[0029] The learning support system according to this embodiment comprises a reception unit, an analysis unit, an environment construction unit, an initial setup unit, and a source code provision unit. The reception unit receives prompts to understand what the student "wants to do." For example, the reception unit can receive text or voice input from the student. The reception unit can also analyze the content of the prompts using AI to understand the student's intentions. The analysis unit analyzes information based on the prompts received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the content of the prompts and identify what the student "wants to do." The analysis unit can also use AI to analyze the content of the prompts and extract necessary information. The environment construction unit constructs the environment based on the information analyzed by the analysis unit. For example, the environment construction unit sets up the development environment and installs necessary tools. The environment construction unit can also automate the environment construction using AI. The initial setup unit performs the initial setup of the environment constructed by the environment construction unit. For example, the initial setup unit edits configuration files and creates user accounts. The initial setup unit can also automate the initial setup using AI. The source code provision unit provides specific source code in an environment configured by the initial setup unit. For example, the source code provision unit provides program templates or code that implements specific functions. The source code provision unit can also generate source code using AI. As a result, the learning support system according to this embodiment can understand what students "want to do," build the necessary environment and perform initial setup based on that, and provide specific source code to effectively support learning.

[0030] The reception desk receives prompts to understand what students "want to do." For example, it can receive text and audio input from students. Specifically, students can input programming assignments or project ideas in text or explain them verbally. The reception desk receives these inputs in real time and converts them into the appropriate format. Furthermore, the reception desk can use AI to analyze the content of the prompts and understand the students' intentions. For example, it can use natural language processing technology to analyze the text and audio content to identify what kind of program the student wants to create and what functions they want to implement. The AI ​​grammatically analyzes the input prompts, extracting keywords and important phrases to clarify the students' intentions. It can also perform more accurate analysis by referring to past prompts and similar projects. This allows the reception desk to accurately understand what students "want to do" and provide the foundational information needed to move to the next step. In addition, the reception desk supports multiple input methods, receiving student intentions in various ways, including not only text input but also voice input and image input. This allows students to input prompts in the way that best suits them, making the learning support system more convenient and effective to use.

[0031] The analysis unit analyzes information based on prompts received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the content of the prompt and identify what the student wants to do. Specifically, it uses AI to analyze the content of text and audio and extract important keywords and phrases. For example, if the prompt "I want to make a game" is entered, the analysis unit will extract the keywords "game" and "want to make" and identify the necessary information based on these. The analysis unit can also use AI to analyze the content of the prompt and extract necessary information. For example, it can identify the technical requirements and constraints included in the prompt and select an appropriate development environment and tools based on them. Furthermore, the analysis unit can perform more accurate analysis by referring to past prompts and similar projects. For example, if a similar prompt has been entered in the past, the analysis results can be used as a reference to identify the student's intention more quickly and accurately. This allows the analysis unit to accurately identify what the student wants to do and provide information to move on to the next step. In addition, the analysis unit can perform analysis in real time and provide results quickly. This allows students to move on to the next step without waiting time, making the use of the learning support system smoother.

[0032] The Environment Setup Department builds the environment based on the information analyzed by the Analysis Department. For example, the Environment Setup Department sets up the development environment and installs necessary tools. Specifically, based on what the student wants to do, as identified by the Analysis Department, it selects appropriate programming languages, frameworks, and libraries and automatically installs them. For example, if a student enters the prompt "I want to create a machine learning model with Python," the Environment Setup Department sets up a Python development environment and installs the necessary libraries (e.g., NumPy and TensorFlow). The Environment Setup Department can also automate environment setup using AI. The AI ​​learns from past environment setup data and selects the optimal setup procedure. This improves the efficiency of environment setup and minimizes errors. Furthermore, the Environment Setup Department can provide a cloud-based development environment. This allows students to develop from anywhere, regardless of their device. For example, by launching a virtual machine on the cloud and setting up the development environment there, students can access it from any internet-connected device. This allows the Environment Setup Department to quickly provide the optimal development environment tailored to what students want to do, effectively supporting their learning.

[0033] The Initial Setup Unit performs the initial setup of the environment built by the Environment Setup Unit. For example, the Initial Setup Unit can edit configuration files and create user accounts. Specifically, it automatically generates the necessary configuration files for the development environment and sets appropriate parameters. For example, in a Python development environment, it generates configuration files (such as requirements.txt and Pipfile) for setting up the virtual environment and managing dependencies. The Initial Setup Unit can also automate the initial setup using AI. The AI ​​learns from past configuration data and selects the optimal settings. This improves the efficiency of the initial setup and minimizes errors. Furthermore, the Initial Setup Unit can create user accounts and set permissions. For example, when using a cloud-based development environment, it can create individual accounts for each student and set appropriate permissions to ensure security. This allows the Initial Setup Unit to quickly initialize the development environment built by the Environment Setup Unit, enabling students to start development immediately. Additionally, the Initial Setup Unit can save and reuse configuration settings. This streamlines the setup process when repeating similar projects or when multiple students use the same environment. This allows the initial setup unit to quickly provide the optimal initial settings tailored to what the student wants to do, effectively supporting their learning.

[0034] The source code provider provides specific source code within the environment configured by the initial setup unit. For example, the source code provider provides program templates and code that implements specific functions. Specifically, it automatically generates and provides program templates tailored to what students "want to do." For instance, if a student inputs the prompt "I want to create a machine learning model using Python," the source code provider generates and provides basic template code for a machine learning model. The source code provider can also generate source code using AI. The AI ​​learns from past code data and generates optimal code tailored to what the student "wants to do." This improves the quality of the source code and minimizes errors. Furthermore, the source code provider can automatically generate code comments and documentation. This makes it easier for students to understand the provided code, improving learning effectiveness. For example, it adds detailed comments to each part of the code, explaining what processes are being performed. It also provides links to relevant documentation and reference materials to help students learn more deeply. This allows the source code provider to quickly provide specific source code tailored to what students "want to do," effectively supporting their learning.

[0035] The Virtual Environment Provisioning Department provides virtual environments. The Virtual Environment Provisioning Department can build virtual environments using, for example, virtual machines or container technologies. The Virtual Environment Provisioning Department can, for example, provide students with all the software and tools they need using virtual machines. The Virtual Environment Provisioning Department can also provide lightweight virtual environments using container technologies. The Virtual Environment Provisioning Department can, for example, build virtual environments using container technologies such as Docker or Kubernetes and provide students with the tools and software they need. This allows the Virtual Environment Provisioning Department to make all the tools students need available to them in one place. Some or all of the above processes in the Virtual Environment Provisioning Department may be performed using, for example, AI, or not using AI. For example, the Virtual Environment Provisioning Department can input the construction of the virtual environment into a generating AI and have the generating AI perform the provision of the virtual environment.

[0036] The Agent Teacher Creation Unit creates agent teachers tailored to individual students. For example, the Agent Teacher Creation Unit can customize agent teachers based on a student's learning history and interests. For example, it can analyze a student's learning history to create the optimal agent teacher. Furthermore, the Agent Teacher Creation Unit can set the character and personality of the agent teacher based on the student's interests. For example, the Agent Teacher Creation Unit can generate the character and personality of the agent teacher using a generative AI. This enables the Agent Teacher Creation Unit to provide individualized learning support. Some or all of the above processes in the Agent Teacher Creation Unit may be performed using AI, or not. For example, the Agent Teacher Creation Unit can input a student's learning history into a generative AI and have the generative AI create the agent teacher.

[0037] The analysis unit uses a generative AI to understand what the student wants to do. For example, the analysis unit uses the generative AI to analyze the content of a prompt and identify the student's intention. For example, the analysis unit inputs a prompt into the generative AI, which analyzes the content of the prompt to understand what the student wants to do. The analysis unit can also use the generative AI to analyze the content of a prompt and extract necessary information. In this way, the analysis unit can understand what the student wants to do more accurately by using the generative AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of a prompt into the generative AI and have the generative AI perform the analysis of the prompt.

[0038] The environment construction unit uses generative AI to construct the necessary environment. For example, the environment construction unit uses generative AI to set up the development environment and install necessary tools. For example, the environment construction unit inputs environment construction instructions to the generative AI, and the generative AI automatically constructs the environment. The environment construction unit can also automate environment construction using generative AI. This allows the environment construction unit to quickly and accurately construct the necessary environment by using generative AI. Some or all of the above processes in the environment construction unit may be performed using AI, or not using AI. For example, the environment construction unit can input environment construction instructions to the generative AI and have the generative AI execute the environment construction.

[0039] The initial setup unit performs initial setup using a generating AI. For example, the initial setup unit uses the generating AI to edit configuration files and create user accounts. For example, the initial setup unit inputs initial setup instructions to the generating AI, and the generating AI automatically performs the initial setup. The initial setup unit can also automate the initial setup using the generating AI. This allows the initial setup unit to perform initial setup quickly and accurately by using the generating AI. Some or all of the above-described processes in the initial setup unit may be performed using AI, or they may not be performed using AI. For example, the initial setup unit can input initial setup instructions to the generating AI and have the generating AI execute the initial setup.

[0040] The source code provider provides specific source code using a generation AI. For example, the source code provider provides program templates or code that implements specific functions using the generation AI. For example, the source code provider inputs source code generation instructions to the generation AI, and the generation AI automatically generates the source code. The source code provider can also automate source code generation using the generation AI. This allows the source code provider to provide specific source code quickly and accurately by using the generation AI. Some or all of the above-described processes in the source code provider may be performed using AI, or not using AI. For example, the source code provider can input source code generation instructions to the generation AI and have the generation AI execute the source code generation.

[0041] The reception desk can analyze a student's past prompt history and select the optimal reception method. For example, the reception desk may prioritize suggesting prompt formats that the student has frequently used in the past. For example, the reception desk may suggest prompts suitable for a specific time period based on the student's past prompt history. For example, the reception desk may analyze a student's past prompt history and provide prompts according to their learning progress. In this way, the reception desk can select the optimal reception method by analyzing a student's past prompt history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input past prompt history into a generating AI and have the generating AI select the optimal reception method.

[0042] The reception unit can filter prompts based on the student's current learning status and areas of interest when prompts are received. For example, the reception unit prioritizes prompts related to the subject the student is currently studying. For example, the reception unit suggests highly relevant prompts based on the student's areas of interest. For example, the reception unit provides prompts of appropriate difficulty level according to the student's learning progress. In this way, the reception unit can provide more relevant prompts by filtering prompts based on the student's current learning status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the student's current learning status and areas of interest into a generating AI and have the generating AI perform prompt filtering.

[0043] The reception unit can prioritize highly relevant prompts when receiving prompts, taking into account the student's geographical location. For example, if a student is in a specific region, the reception unit will prioritize prompts related to that region. For example, if a student is traveling, the reception unit will provide prompts related to their travel destination. For example, if a student is at school, the reception unit will prioritize prompts related to the school curriculum. In this way, the reception unit can provide more relevant prompts by taking into account the student's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input geographical location information into a generating AI and have the generating AI determine the priority of prompts.

[0044] The reception unit can analyze a student's social media activity when a prompt is received and receive relevant prompts. For example, the reception unit can provide prompts related to topics the student has shown interest in on social media. For example, the reception unit can suggest prompts based on the content of accounts the student follows on social media. For example, the reception unit can analyze a student's social media activity history and provide highly relevant prompts. In this way, the reception unit can provide more relevant prompts by analyzing the student's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input social media activity data into a generating AI and have the generating AI select prompts.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the prompts during the analysis. For example, the analysis unit provides detailed analysis results for high-importance prompts. For example, the analysis unit provides concise analysis results for low-importance prompts. For example, the analysis unit adjusts the display method of the analysis results according to the importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the prompts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input prompt importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the prompt during analysis. For example, the analysis unit applies a code analysis algorithm to prompts related to programming. For example, the analysis unit applies a music analysis algorithm to prompts related to music. For example, the analysis unit applies an image analysis algorithm to prompts related to graphics. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of the prompt. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input prompt category data into a generating AI and have the generating AI select an analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the submission timing of prompts during the analysis. For example, the analysis unit may prioritize the analysis of prompts submitted earlier. For example, the analysis unit may postpone the analysis of prompts submitted later. For example, the analysis unit may adjust the priority of analysis according to the submission timing. This allows the analysis unit to perform more efficient analysis by determining the priority of analysis based on the submission timing of prompts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input submission timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the prompts during analysis. For example, the analysis unit prioritizes the analysis of highly relevant prompts. For example, the analysis unit postpones the analysis of less relevant prompts. For example, the analysis unit adjusts the order of analysis according to relevance. In this way, the analysis unit can provide more relevant analysis results by adjusting the order of analysis based on the relevance of the prompts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0049] The environment setup unit can analyze a student's past learning history to select the optimal environment setup method during environment setup. For example, the environment setup unit can build the optimal environment based on the environment settings the student has used in the past. For example, the environment setup unit can select the optimal tools and software from the student's past learning history. For example, the environment setup unit can analyze the student's past learning history and propose the optimal environment setup method. In this way, the environment setup unit can select the optimal environment setup method by analyzing the student's past learning history. Some or all of the above processes in the environment setup unit may be performed using AI, for example, or without AI. For example, the environment setup unit can input past learning history data into a generating AI and have the generating AI perform the selection of the environment setup method.

[0050] The environment setup unit can customize the means of environment setup based on the student's current learning status during environment setup. For example, the environment setup unit provides tools and software related to the subject the student is currently studying. For example, the environment setup unit suggests appropriate environment settings according to the student's learning progress. For example, the environment setup unit analyzes the student's current learning status and provides the optimal environment setup method. In this way, the environment setup unit can provide a more appropriate environment by customizing the means of environment setup based on the student's current learning status. Some or all of the above processes in the environment setup unit may be performed using AI, for example, or without AI. For example, the environment setup unit can input current learning status data into a generating AI and have the generating AI perform the customization of the environment setup means.

[0051] The environment setup unit can select the optimal environment setup method by considering the student's geographical location information during environment setup. For example, if a student is in a specific region, the environment setup unit provides tools and software related to that region. For example, if a student is traveling, the environment setup unit provides environment settings related to the travel destination. For example, if a student is at school, the environment setup unit provides environment settings related to the school curriculum. In this way, the environment setup unit can provide a more appropriate environment by considering the student's geographical location information. Some or all of the above processing in the environment setup unit may be performed using AI, for example, or without AI. For example, the environment setup unit can input geographical location data into a generating AI and have the generating AI select the environment setup method.

[0052] The environment building unit can analyze students' social media activities and propose methods for building the environment during the building process. For example, the environment building unit can provide environment settings related to topics that students have shown interest in on social media. For example, the environment building unit can propose environment settings based on the content of accounts that students follow on social media. For example, the environment building unit can analyze students' social media activity history and provide highly relevant environment settings. In this way, the environment building unit can provide a more appropriate environment by analyzing students' social media activities. Some or all of the above processes in the environment building unit may be performed using AI, for example, or without AI. For example, the environment building unit can input social media activity data into a generating AI and have the generating AI execute suggestions for environment building methods.

[0053] The initial setup unit can analyze a student's past setup history to select the optimal initial setup method during the initial setup process. For example, the initial setup unit provides the optimal initial setup based on settings the student has used in the past. For example, the initial setup unit selects the optimal tools and software from the student's past setup history. For example, the initial setup unit analyzes the student's past setup history and proposes the optimal initial setup method. In this way, the initial setup unit can select the optimal initial setup method by analyzing the student's past setup history. Some or all of the above processes in the initial setup unit may be performed using AI, for example, or without AI. For example, the initial setup unit can input past setup history data into a generating AI and have the generating AI perform the selection of the initial setup method.

[0054] The initial setup unit can customize the initial setup method based on the student's current learning status during the initial setup process. For example, the initial setup unit provides tools and software related to the subject the student is currently studying. For example, the initial setup unit suggests appropriate initial settings according to the student's learning progress. For example, the initial setup unit analyzes the student's current learning status and provides the optimal initial setup method. In this way, the initial setup unit can provide more appropriate initial settings by customizing the initial setup method based on the student's current learning status. Some or all of the above-described processes in the initial setup unit may be performed using AI, for example, or without AI. For example, the initial setup unit can input current learning status data into a generating AI and have the generating AI perform the customization of the initial setup method.

[0055] The initial setup unit can select the optimal initial setup method during the initial setup process, taking into account the student's geographical location information. For example, if the student is in a specific region, the initial setup unit provides tools and software related to that region. For example, if the student is traveling, the initial setup unit provides initial settings related to the travel destination. For example, if the student is at school, the initial setup unit provides initial settings related to the school curriculum. In this way, the initial setup unit can provide more appropriate initial settings by taking into account the student's geographical location information. Some or all of the above processing in the initial setup unit may be performed using AI, for example, or without AI. For example, the initial setup unit can input geographical location data into a generating AI and have the generating AI perform the selection of the initial setup method.

[0056] The initial setup unit can analyze a student's social media activity during the initial setup process and propose initial setup methods. For example, the initial setup unit can provide initial setups related to topics the student has shown interest in on social media. For example, the initial setup unit can propose initial setups based on the content of accounts the student follows on social media. For example, the initial setup unit can analyze a student's social media activity history and provide highly relevant initial setups. In this way, the initial setup unit can provide more appropriate initial setups by analyzing the student's social media activity. Some or all of the above-described processes in the initial setup unit may be performed using AI, for example, or without AI. For example, the initial setup unit can input social media activity data into a generating AI and have the generating AI execute the proposal of initial setup methods.

[0057] The source code provider can analyze a student's past learning history to select the optimal source code provision method when providing source code. For example, the source code provider can provide the optimal source code based on what the student has learned in the past. For example, the source code provider can select the optimal code example from the student's past learning history. For example, the source code provider can analyze the student's past learning history and propose the optimal source code provision method. In this way, the source code provider can select the optimal source code provision method by analyzing the student's past learning history. Some or all of the above processes in the source code provider may be performed using AI, for example, or without using AI. For example, the source code provider can input past learning history data into a generating AI and have the generating AI perform the selection of the source code provision method.

[0058] The source code provider can customize the means of providing source code based on the student's current learning status when providing source code. For example, the source code provider provides source code related to the subject the student is currently studying. For example, the source code provider suggests appropriate source code according to the student's learning progress. For example, the source code provider analyzes the student's current learning status and provides the optimal method of providing source code. As a result, the source code provider can provide more appropriate source code by customizing the means of providing source code based on the student's current learning status. Some or all of the above processing in the source code provider may be performed using AI, for example, or without AI. For example, the source code provider can input current learning status data into a generating AI and have the generating AI perform the customization of the source code provision means.

[0059] The source code provider can select the optimal source code provision method by considering the student's geographical location information when providing source code. For example, if a student is in a specific region, the source code provider will provide source code related to that region. For example, if a student is traveling, the source code provider will provide source code related to their travel destination. For example, if a student is at school, the source code provider will provide source code related to the school curriculum. In this way, the source code provider can provide more appropriate source code by considering the student's geographical location information. Some or all of the above processing in the source code provider may be performed using AI, for example, or without AI. For example, the source code provider can input geographical location data into a generating AI and have the generating AI perform the selection of the source code provision method.

[0060] The source code provider can analyze students' social media activity and propose methods for providing source code when providing it. For example, the source code provider can provide source code related to topics that students have shown interest in on social media. For example, the source code provider can propose source code based on the content of accounts that students follow on social media. For example, the source code provider can analyze students' social media activity history and provide highly relevant source code. In this way, the source code provider can provide more appropriate source code by analyzing students' social media activity. Some or all of the above processing in the source code provider may be performed using AI, for example, or without AI. For example, the source code provider can input social media activity data into a generating AI and have the generating AI execute a proposal for a source code provision method.

[0061] The virtual environment provisioning unit can analyze a student's past learning history and select the optimal virtual environment provisioning method when providing a virtual environment. For example, the virtual environment provisioning unit provides the optimal virtual environment based on the virtual environment settings the student has used in the past. For example, the virtual environment provisioning unit selects the optimal tools and software from the student's past learning history. For example, the virtual environment provisioning unit analyzes the student's past learning history and proposes the optimal virtual environment provisioning method. In this way, the virtual environment provisioning unit can select the optimal virtual environment provisioning method by analyzing the student's past learning history. Some or all of the above processes in the virtual environment provisioning unit may be performed using AI, for example, or without AI. For example, the virtual environment provisioning unit can input past learning history data into a generating AI and have the generating AI perform the selection of the virtual environment provisioning method.

[0062] The virtual environment provider unit can select the optimal virtual environment provision method by considering the student's geographical location information when providing a virtual environment. For example, if a student is in a specific region, the virtual environment provider unit provides tools and software related to that region. For example, if a student is traveling, the virtual environment provider unit provides virtual environment settings related to the travel destination. For example, if a student is at school, the virtual environment provider unit provides virtual environment settings related to the school curriculum. In this way, the virtual environment provider unit can provide a more appropriate virtual environment by considering the student's geographical location information. Some or all of the above processing in the virtual environment provider unit may be performed using AI, for example, or without AI. For example, the virtual environment provider unit can input geographical location data into a generating AI and have the generating AI perform the selection of the virtual environment provision method.

[0063] The Agent Teacher Creation Unit can analyze a student's past learning history to select the optimal Agent Teacher creation method when creating an Agent Teacher. For example, the Agent Teacher Creation Unit can create the optimal Agent Teacher based on the settings of Agent Teachers the student has used in the past. For example, the Agent Teacher Creation Unit can select the optimal Agent Teacher character from the student's past learning history. For example, the Agent Teacher Creation Unit can analyze the student's past learning history and propose the optimal Agent Teacher creation method. In this way, the Agent Teacher Creation Unit can select the optimal Agent Teacher creation method by analyzing the student's past learning history. Some or all of the above processes in the Agent Teacher Creation Unit may be performed using AI, for example, or without AI. For example, the Agent Teacher Creation Unit can input past learning history data into a generating AI and have the generating AI perform the selection of the Agent Teacher creation method.

[0064] The Agent Teacher Creation Unit can select the optimal Agent Teacher Creation Method by considering the student's geographical location information when creating an Agent Teacher. For example, if the student is in a specific region, the Agent Teacher Creation Unit will create an Agent Teacher related to that region. For example, if the student is traveling, the Agent Teacher Creation Unit will create an Agent Teacher related to the travel destination. For example, if the student is at school, the Agent Teacher Creation Unit will create an Agent Teacher related to the school curriculum. In this way, the Agent Teacher Creation Unit can provide a more appropriate Agent Teacher by considering the student's geographical location information. Some or all of the above processing in the Agent Teacher Creation Unit may be performed using AI, for example, or without AI. For example, the Agent Teacher Creation Unit can input geographical location data into a generating AI and have the generating AI select the Agent Teacher Creation Method.

[0065] The Agent Teacher Creation Unit can analyze a student's social media activity and suggest an Agent Teacher character when creating an Agent Teacher. For example, the Agent Teacher Creation Unit can create an Agent Teacher based on a character the student has shown interest in on social media. For example, the Agent Teacher Creation Unit can suggest an Agent Teacher based on the content of accounts the student follows on social media. For example, the Agent Teacher Creation Unit can analyze a student's social media activity history and provide a highly relevant Agent Teacher. In this way, the Agent Teacher Creation Unit can provide a more appropriate Agent Teacher by analyzing the student's social media activity. Some or all of the above processes in the Agent Teacher Creation Unit may be performed using AI, for example, or not using AI. For example, the Agent Teacher Creation Unit can input social media activity data into a generating AI and have the generating AI suggest an Agent Teacher character.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The learning support system can also include a learning style analysis unit that analyzes students' learning styles. This unit analyzes students' past learning history and behavioral patterns to identify their learning style. For example, students who prefer visual learning can be provided with materials that heavily utilize visual aids. Students who prefer auditory learning can be provided with audio guides or podcast-style materials. Furthermore, students who prefer hands-on learning can be provided with interactive simulations and experiments. This allows the learning support system to provide the optimal learning method tailored to each student's learning style.

[0068] The learning support system can also include a progress monitoring unit that monitors students' learning progress in real time. The progress monitoring unit understands how far students are progressing in their studies and provides feedback as needed. For example, if a student is falling behind on a particular assignment, the progress monitoring unit can provide additional support or resources. Furthermore, if a student achieves a goal, the progress monitoring unit can provide rewards and praise to enhance their sense of accomplishment. In addition, the progress monitoring unit can analyze students' learning data and suggest the next tasks and goals they should tackle. This allows the learning support system to effectively support students' learning progress.

[0069] The learning support system can also be equipped with a motivation enhancement section to further improve students' learning motivation. This section provides various methods to increase students' desire to learn. For example, it can offer learning content incorporating game elements, allowing students to learn while having fun. Furthermore, the motivation enhancement section can introduce badges or point systems to recognize students' efforts, allowing them to feel a sense of accomplishment. Additionally, the motivation enhancement section can provide group learning functions to promote competition and cooperation among students. In this way, the learning support system can increase students' learning motivation and improve learning effectiveness.

[0070] The learning support system can also be equipped with an environment optimization unit to further optimize the student's learning environment. This unit has various functions to provide an environment where students can concentrate on their studies. For example, it can suggest the optimal learning environment based on the student's study time and location. It can also provide a noise cancellation function to reduce noise that occurs during student study. Furthermore, it can monitor the student's study posture and lighting conditions and provide appropriate advice. In this way, the learning support system can help students progress in an optimal learning environment.

[0071] The learning support system can also include an evaluation unit to assess students' learning outcomes. This evaluation unit comprehensively evaluates students' learning outcomes and provides feedback. For example, it can analyze students' test results and assignment submission status to conduct a comprehensive evaluation. It can also evaluate students' learning processes and identify areas for improvement. Furthermore, the evaluation unit can incorporate student self-assessment and peer assessment to provide a more multifaceted evaluation. This allows the learning support system to accurately assess students' learning outcomes and provide appropriate feedback.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception desk receives prompts to understand what students want to do. For example, it can receive text or audio input from students. The reception desk can also use AI to analyze the content of the prompts and understand the students' intentions. Step 2: The analysis unit analyzes the information based on the prompt received by the reception unit. For example, it may use natural language processing technology to analyze the content of the prompt and identify what the student wants to do. It can also use AI to analyze the content of the prompt and extract the necessary information. Step 3: The environment construction unit constructs the environment based on the information analyzed by the analysis unit. For example, it sets up the development environment and installs the necessary tools. It is also possible to automate the environment construction using AI. Step 4: The initial setup section performs the initial setup of the environment built by the environment construction section. For example, it may edit configuration files or create user accounts. Initial setup can also be automated using AI. Step 5: The source code provision unit provides specific source code in the environment configured by the initial setup unit. For example, it provides program templates or code to implement specific functions. It can also generate source code using AI.

[0074] (Example of form 2) The learning support system according to an embodiment of the present invention is a system for effectively utilizing personal computers provided by schools for after-school learning and other purposes. This learning support system addresses conventional problems such as not knowing how to use a computer, being unable to participate in ICT workshops, and the high cost and unavailability of desired software. It utilizes generative AI and autonomous agents to provide consistent information tailored to what students want to achieve. For example, the learning support system uses generative AI to understand what students "want to do" and, based on that, builds the necessary environment and performs initial setup. Next, the learning support system provides specific source code and advanced information such as game creation, music, and graphics. This allows students to progress in their learning through a virtual environment. Furthermore, the learning support system creates personalized agent teachers for each student and provides a subscription-based virtual environment, enabling them to use all necessary tools in one place. For example, the learning support system allows students to learn not only simple programming but also more advanced information. Additionally, the learning support system improves learning efficiency through an AI teacher that provides consistent services from environment construction to initial setup and provision of specific source code. Moreover, by utilizing a subscription-based virtual environment, the learning support system eliminates the need to purchase expensive software, reducing the financial burden. This allows the learning support system to effectively assist students' learning.

[0075] The learning support system according to this embodiment comprises a reception unit, an analysis unit, an environment construction unit, an initial setup unit, and a source code provision unit. The reception unit receives prompts to understand what the student "wants to do." For example, the reception unit can receive text or voice input from the student. The reception unit can also analyze the content of the prompts using AI to understand the student's intentions. The analysis unit analyzes information based on the prompts received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the content of the prompts and identify what the student "wants to do." The analysis unit can also use AI to analyze the content of the prompts and extract necessary information. The environment construction unit constructs the environment based on the information analyzed by the analysis unit. For example, the environment construction unit sets up the development environment and installs necessary tools. The environment construction unit can also automate the environment construction using AI. The initial setup unit performs the initial setup of the environment constructed by the environment construction unit. For example, the initial setup unit edits configuration files and creates user accounts. The initial setup unit can also automate the initial setup using AI. The source code provision unit provides specific source code in an environment configured by the initial setup unit. For example, the source code provision unit provides program templates or code that implements specific functions. The source code provision unit can also generate source code using AI. As a result, the learning support system according to this embodiment can understand what students "want to do," build the necessary environment and perform initial setup based on that, and provide specific source code to effectively support learning.

[0076] The reception desk receives prompts to understand what students "want to do." For example, it can receive text and audio input from students. Specifically, students can input programming assignments or project ideas in text or explain them verbally. The reception desk receives these inputs in real time and converts them into the appropriate format. Furthermore, the reception desk can use AI to analyze the content of the prompts and understand the students' intentions. For example, it can use natural language processing technology to analyze the text and audio content to identify what kind of program the student wants to create and what functions they want to implement. The AI ​​grammatically analyzes the input prompts, extracting keywords and important phrases to clarify the students' intentions. It can also perform more accurate analysis by referring to past prompts and similar projects. This allows the reception desk to accurately understand what students "want to do" and provide the foundational information needed to move to the next step. In addition, the reception desk supports multiple input methods, receiving student intentions in various ways, including not only text input but also voice input and image input. This allows students to input prompts in the way that best suits them, making the learning support system more convenient and effective to use.

[0077] The analysis unit analyzes information based on prompts received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the content of the prompt and identify what the student wants to do. Specifically, it uses AI to analyze the content of text and audio and extract important keywords and phrases. For example, if the prompt "I want to make a game" is entered, the analysis unit will extract the keywords "game" and "want to make" and identify the necessary information based on these. The analysis unit can also use AI to analyze the content of the prompt and extract necessary information. For example, it can identify the technical requirements and constraints included in the prompt and select an appropriate development environment and tools based on them. Furthermore, the analysis unit can perform more accurate analysis by referring to past prompts and similar projects. For example, if a similar prompt has been entered in the past, the analysis results can be used as a reference to identify the student's intention more quickly and accurately. This allows the analysis unit to accurately identify what the student wants to do and provide information to move on to the next step. In addition, the analysis unit can perform analysis in real time and provide results quickly. This allows students to move on to the next step without waiting time, making the use of the learning support system smoother.

[0078] The Environment Setup Department builds the environment based on the information analyzed by the Analysis Department. For example, the Environment Setup Department sets up the development environment and installs necessary tools. Specifically, based on what the student wants to do, as identified by the Analysis Department, it selects appropriate programming languages, frameworks, and libraries and automatically installs them. For example, if a student enters the prompt "I want to create a machine learning model with Python," the Environment Setup Department sets up a Python development environment and installs the necessary libraries (e.g., NumPy and TensorFlow). The Environment Setup Department can also automate environment setup using AI. The AI ​​learns from past environment setup data and selects the optimal setup procedure. This improves the efficiency of environment setup and minimizes errors. Furthermore, the Environment Setup Department can provide a cloud-based development environment. This allows students to develop from anywhere, regardless of their device. For example, by launching a virtual machine on the cloud and setting up the development environment there, students can access it from any internet-connected device. This allows the Environment Setup Department to quickly provide the optimal development environment tailored to what students want to do, effectively supporting their learning.

[0079] The Initial Setup Unit performs the initial setup of the environment built by the Environment Setup Unit. For example, the Initial Setup Unit can edit configuration files and create user accounts. Specifically, it automatically generates the necessary configuration files for the development environment and sets appropriate parameters. For example, in a Python development environment, it generates configuration files (such as requirements.txt and Pipfile) for setting up the virtual environment and managing dependencies. The Initial Setup Unit can also automate the initial setup using AI. The AI ​​learns from past configuration data and selects the optimal settings. This improves the efficiency of the initial setup and minimizes errors. Furthermore, the Initial Setup Unit can create user accounts and set permissions. For example, when using a cloud-based development environment, it can create individual accounts for each student and set appropriate permissions to ensure security. This allows the Initial Setup Unit to quickly initialize the development environment built by the Environment Setup Unit, enabling students to start development immediately. Additionally, the Initial Setup Unit can save and reuse configuration settings. This streamlines the setup process when repeating similar projects or when multiple students use the same environment. This allows the initial setup unit to quickly provide the optimal initial settings tailored to what the student wants to do, effectively supporting their learning.

[0080] The source code provider provides specific source code within the environment configured by the initial setup unit. For example, the source code provider provides program templates and code that implements specific functions. Specifically, it automatically generates and provides program templates tailored to what students "want to do." For instance, if a student inputs the prompt "I want to create a machine learning model using Python," the source code provider generates and provides basic template code for a machine learning model. The source code provider can also generate source code using AI. The AI ​​learns from past code data and generates optimal code tailored to what the student "wants to do." This improves the quality of the source code and minimizes errors. Furthermore, the source code provider can automatically generate code comments and documentation. This makes it easier for students to understand the provided code, improving learning effectiveness. For example, it adds detailed comments to each part of the code, explaining what processes are being performed. It also provides links to relevant documentation and reference materials to help students learn more deeply. This allows the source code provider to quickly provide specific source code tailored to what students "want to do," effectively supporting their learning.

[0081] The Virtual Environment Provisioning Department provides virtual environments. The Virtual Environment Provisioning Department can build virtual environments using, for example, virtual machines or container technologies. The Virtual Environment Provisioning Department can, for example, provide students with all the software and tools they need using virtual machines. The Virtual Environment Provisioning Department can also provide lightweight virtual environments using container technologies. The Virtual Environment Provisioning Department can, for example, build virtual environments using container technologies such as Docker or Kubernetes and provide students with the tools and software they need. This allows the Virtual Environment Provisioning Department to make all the tools students need available to them in one place. Some or all of the above processes in the Virtual Environment Provisioning Department may be performed using, for example, AI, or not using AI. For example, the Virtual Environment Provisioning Department can input the construction of the virtual environment into a generating AI and have the generating AI perform the provision of the virtual environment.

[0082] The Agent Teacher Creation Unit creates agent teachers tailored to individual students. For example, the Agent Teacher Creation Unit can customize agent teachers based on a student's learning history and interests. For example, it can analyze a student's learning history to create the optimal agent teacher. Furthermore, the Agent Teacher Creation Unit can set the character and personality of the agent teacher based on the student's interests. For example, the Agent Teacher Creation Unit can generate the character and personality of the agent teacher using a generative AI. This enables the Agent Teacher Creation Unit to provide individualized learning support. Some or all of the above processes in the Agent Teacher Creation Unit may be performed using AI, or not. For example, the Agent Teacher Creation Unit can input a student's learning history into a generative AI and have the generative AI create the agent teacher.

[0083] The analysis unit uses a generative AI to understand what the student wants to do. For example, the analysis unit uses the generative AI to analyze the content of a prompt and identify the student's intention. For example, the analysis unit inputs a prompt into the generative AI, which analyzes the content of the prompt to understand what the student wants to do. The analysis unit can also use the generative AI to analyze the content of a prompt and extract necessary information. In this way, the analysis unit can understand what the student wants to do more accurately by using the generative AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of a prompt into the generative AI and have the generative AI perform the analysis of the prompt.

[0084] The environment construction unit uses generative AI to construct the necessary environment. For example, the environment construction unit uses generative AI to set up the development environment and install necessary tools. For example, the environment construction unit inputs environment construction instructions to the generative AI, and the generative AI automatically constructs the environment. The environment construction unit can also automate environment construction using generative AI. This allows the environment construction unit to quickly and accurately construct the necessary environment by using generative AI. Some or all of the above processes in the environment construction unit may be performed using AI, or not using AI. For example, the environment construction unit can input environment construction instructions to the generative AI and have the generative AI execute the environment construction.

[0085] The initial setup unit performs initial setup using a generating AI. For example, the initial setup unit uses the generating AI to edit configuration files and create user accounts. For example, the initial setup unit inputs initial setup instructions to the generating AI, and the generating AI automatically performs the initial setup. The initial setup unit can also automate the initial setup using the generating AI. This allows the initial setup unit to perform initial setup quickly and accurately by using the generating AI. Some or all of the above-described processes in the initial setup unit may be performed using AI, or they may not be performed using AI. For example, the initial setup unit can input initial setup instructions to the generating AI and have the generating AI execute the initial setup.

[0086] The source code provider provides specific source code using a generation AI. For example, the source code provider provides program templates or code that implements specific functions using the generation AI. For example, the source code provider inputs source code generation instructions to the generation AI, and the generation AI automatically generates the source code. The source code provider can also automate source code generation using the generation AI. This allows the source code provider to provide specific source code quickly and accurately by using the generation AI. Some or all of the above-described processes in the source code provider may be performed using AI, or not using AI. For example, the source code provider can input source code generation instructions to the generation AI and have the generation AI execute the source code generation.

[0087] The reception unit can estimate a student's emotions and adjust the timing of prompt responses based on the estimated emotions. For example, if a student is stressed, the reception unit may delay prompt responses to allow time to relax. For example, if a student is focused, the reception unit may respond quickly to prompts to avoid interrupting the learning flow. For example, if a student is tired, the reception unit may temporarily stop prompt responses to encourage a break. This allows the reception unit to respond to prompts at a more appropriate time by adjusting the timing according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit may input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The reception desk can analyze a student's past prompt history and select the optimal reception method. For example, the reception desk may prioritize suggesting prompt formats that the student has frequently used in the past. For example, the reception desk may suggest prompts suitable for a specific time period based on the student's past prompt history. For example, the reception desk may analyze a student's past prompt history and provide prompts according to their learning progress. In this way, the reception desk can select the optimal reception method by analyzing a student's past prompt history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input past prompt history into a generating AI and have the generating AI select the optimal reception method.

[0089] The reception unit can filter prompts based on the student's current learning status and areas of interest when prompts are received. For example, the reception unit prioritizes prompts related to the subject the student is currently studying. For example, the reception unit suggests highly relevant prompts based on the student's areas of interest. For example, the reception unit provides prompts of appropriate difficulty level according to the student's learning progress. In this way, the reception unit can provide more relevant prompts by filtering prompts based on the student's current learning status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the student's current learning status and areas of interest into a generating AI and have the generating AI perform prompt filtering.

[0090] The reception desk can estimate a student's emotions and determine the priority of prompts to be received based on the estimated emotions. For example, if a student is excited, the reception desk will prioritize providing more difficult prompts. For example, if a student is relaxed, the reception desk will prioritize providing prompts that can be handled in a relaxed state. For example, if a student is tired, the reception desk will prioritize providing easier prompts. In this way, the reception desk can provide more appropriate prompts by prioritizing prompts according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input student emotion data into a generative AI and have the generative AI determine the priority of prompts.

[0091] The reception unit can prioritize highly relevant prompts when receiving prompts, taking into account the student's geographical location. For example, if a student is in a specific region, the reception unit will prioritize prompts related to that region. For example, if a student is traveling, the reception unit will provide prompts related to their travel destination. For example, if a student is at school, the reception unit will prioritize prompts related to the school curriculum. In this way, the reception unit can provide more relevant prompts by taking into account the student's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input geographical location information into a generating AI and have the generating AI determine the priority of prompts.

[0092] The reception unit can analyze a student's social media activity when a prompt is received and receive relevant prompts. For example, the reception unit can provide prompts related to topics the student has shown interest in on social media. For example, the reception unit can suggest prompts based on the content of accounts the student follows on social media. For example, the reception unit can analyze a student's social media activity history and provide highly relevant prompts. In this way, the reception unit can provide more relevant prompts by analyzing the student's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input social media activity data into a generating AI and have the generating AI select prompts.

[0093] The analysis unit can estimate a student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a student is nervous, the analysis unit provides a simple and easy-to-understand analysis result. For example, if a student is relaxed, the analysis unit provides a detailed analysis result. For example, if a student is excited, the analysis unit provides a visually stimulating analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the prompts during the analysis. For example, the analysis unit provides detailed analysis results for high-importance prompts. For example, the analysis unit provides concise analysis results for low-importance prompts. For example, the analysis unit adjusts the display method of the analysis results according to the importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the prompts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input prompt importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0095] The analysis unit can apply different analysis algorithms depending on the category of the prompt during analysis. For example, the analysis unit applies a code analysis algorithm to prompts related to programming. For example, the analysis unit applies a music analysis algorithm to prompts related to music. For example, the analysis unit applies an image analysis algorithm to prompts related to graphics. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of the prompt. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input prompt category data into a generating AI and have the generating AI select an analysis algorithm.

[0096] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the student is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the student is relaxed, the analysis unit provides a detailed analysis result. For example, if the student is excited, the analysis unit provides a visually stimulating analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0097] The analysis unit can determine the priority of analysis based on the submission timing of prompts during the analysis. For example, the analysis unit may prioritize the analysis of prompts submitted earlier. For example, the analysis unit may postpone the analysis of prompts submitted later. For example, the analysis unit may adjust the priority of analysis according to the submission timing. This allows the analysis unit to perform more efficient analysis by determining the priority of analysis based on the submission timing of prompts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input submission timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0098] The analysis unit can adjust the order of analysis based on the relevance of the prompts during analysis. For example, the analysis unit prioritizes the analysis of highly relevant prompts. For example, the analysis unit postpones the analysis of less relevant prompts. For example, the analysis unit adjusts the order of analysis according to relevance. In this way, the analysis unit can provide more relevant analysis results by adjusting the order of analysis based on the relevance of the prompts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0099] The environment building unit can estimate a student's emotions and adjust the environment building method based on the estimated emotions. For example, if a student is nervous, the environment building unit can build a simple and easy-to-use environment. For example, if a student is relaxed, the environment building unit can build an environment with detailed settings. For example, if a student is excited, the environment building unit can build a visually stimulating environment. In this way, the environment building unit can provide a more appropriate environment by adjusting the environment building method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the environment building unit may be performed using AI, for example, or not using AI. For example, the environment building unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the environment building method.

[0100] The environment setup unit can analyze a student's past learning history to select the optimal environment setup method during environment setup. For example, the environment setup unit can build the optimal environment based on the environment settings the student has used in the past. For example, the environment setup unit can select the optimal tools and software from the student's past learning history. For example, the environment setup unit can analyze the student's past learning history and propose the optimal environment setup method. In this way, the environment setup unit can select the optimal environment setup method by analyzing the student's past learning history. Some or all of the above processes in the environment setup unit may be performed using AI, for example, or without AI. For example, the environment setup unit can input past learning history data into a generating AI and have the generating AI perform the selection of the environment setup method.

[0101] The environment setup unit can customize the means of environment setup based on the student's current learning status during environment setup. For example, the environment setup unit provides tools and software related to the subject the student is currently studying. For example, the environment setup unit suggests appropriate environment settings according to the student's learning progress. For example, the environment setup unit analyzes the student's current learning status and provides the optimal environment setup method. In this way, the environment setup unit can provide a more appropriate environment by customizing the means of environment setup based on the student's current learning status. Some or all of the above processes in the environment setup unit may be performed using AI, for example, or without AI. For example, the environment setup unit can input current learning status data into a generating AI and have the generating AI perform the customization of the environment setup means.

[0102] The environment building unit can estimate a student's emotions and determine the priority of environment building based on the estimated emotions. For example, if a student is excited, the environment building unit will prioritize building a visually stimulating environment. For example, if a student is relaxed, the environment building unit will prioritize building an environment that can be used in a relaxed state. For example, if a student is tired, the environment building unit will prioritize building a simple and easy-to-use environment. In this way, the environment building unit can provide a more appropriate environment by determining the priority of environment building according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the environment building unit may be performed using AI or not using AI. For example, the environment building unit can input student emotion data into a generative AI and have the generative AI perform the determination of environment building priorities.

[0103] The environment setup unit can select the optimal environment setup method by considering the student's geographical location information during environment setup. For example, if a student is in a specific region, the environment setup unit provides tools and software related to that region. For example, if a student is traveling, the environment setup unit provides environment settings related to the travel destination. For example, if a student is at school, the environment setup unit provides environment settings related to the school curriculum. In this way, the environment setup unit can provide a more appropriate environment by considering the student's geographical location information. Some or all of the above processing in the environment setup unit may be performed using AI, for example, or without AI. For example, the environment setup unit can input geographical location data into a generating AI and have the generating AI select the environment setup method.

[0104] The environment building unit can analyze students' social media activities and propose methods for building the environment during the building process. For example, the environment building unit can provide environment settings related to topics that students have shown interest in on social media. For example, the environment building unit can propose environment settings based on the content of accounts that students follow on social media. For example, the environment building unit can analyze students' social media activity history and provide highly relevant environment settings. In this way, the environment building unit can provide a more appropriate environment by analyzing students' social media activities. Some or all of the above processes in the environment building unit may be performed using AI, for example, or without AI. For example, the environment building unit can input social media activity data into a generating AI and have the generating AI execute suggestions for environment building methods.

[0105] The initial setup unit can estimate the student's emotions and adjust the initial setup method based on the estimated emotions. For example, if the student is nervous, the initial setup unit provides a simple and easy-to-use initial setup. For example, if the student is relaxed, the initial setup unit provides an initial setup that allows for detailed settings. For example, if the student is excited, the initial setup unit provides a visually stimulating initial setup. In this way, the initial setup unit can provide a more appropriate initial setup by adjusting the initial setup method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the initial setup unit may be performed using AI, for example, or not using AI. For example, the initial setup unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the initial setup method.

[0106] The initial setup unit can analyze a student's past setup history to select the optimal initial setup method during the initial setup process. For example, the initial setup unit provides the optimal initial setup based on settings the student has used in the past. For example, the initial setup unit selects the optimal tools and software from the student's past setup history. For example, the initial setup unit analyzes the student's past setup history and proposes the optimal initial setup method. In this way, the initial setup unit can select the optimal initial setup method by analyzing the student's past setup history. Some or all of the above processes in the initial setup unit may be performed using AI, for example, or without AI. For example, the initial setup unit can input past setup history data into a generating AI and have the generating AI perform the selection of the initial setup method.

[0107] The initial setup unit can customize the initial setup method based on the student's current learning status during the initial setup process. For example, the initial setup unit provides tools and software related to the subject the student is currently studying. For example, the initial setup unit suggests appropriate initial settings according to the student's learning progress. For example, the initial setup unit analyzes the student's current learning status and provides the optimal initial setup method. In this way, the initial setup unit can provide more appropriate initial settings by customizing the initial setup method based on the student's current learning status. Some or all of the above-described processes in the initial setup unit may be performed using AI, for example, or without AI. For example, the initial setup unit can input current learning status data into a generating AI and have the generating AI perform the customization of the initial setup method.

[0108] The initial setup unit can estimate a student's emotions and determine the priority of initial settings based on the estimated emotions. For example, if a student is excited, the initial setup unit will prioritize providing visually stimulating initial settings. If a student is relaxed, the initial setup unit will prioritize providing initial settings that can be used in a relaxed state. If a student is tired, the initial setup unit will prioritize providing simple and easy-to-use initial settings. In this way, the initial setup unit can provide more appropriate initial settings by determining the priority of initial settings according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the initial setup unit may be performed using AI or not using AI. For example, the initial setup unit can input student emotion data into a generative AI and have the generative AI determine the priority of initial settings.

[0109] The initial setup unit can select the optimal initial setup method during the initial setup process, taking into account the student's geographical location information. For example, if the student is in a specific region, the initial setup unit provides tools and software related to that region. For example, if the student is traveling, the initial setup unit provides initial settings related to the travel destination. For example, if the student is at school, the initial setup unit provides initial settings related to the school curriculum. In this way, the initial setup unit can provide more appropriate initial settings by taking into account the student's geographical location information. Some or all of the above processing in the initial setup unit may be performed using AI, for example, or without AI. For example, the initial setup unit can input geographical location data into a generating AI and have the generating AI perform the selection of the initial setup method.

[0110] The initial setup unit can analyze a student's social media activity during the initial setup process and propose initial setup methods. For example, the initial setup unit can provide initial setups related to topics the student has shown interest in on social media. For example, the initial setup unit can propose initial setups based on the content of accounts the student follows on social media. For example, the initial setup unit can analyze a student's social media activity history and provide highly relevant initial setups. In this way, the initial setup unit can provide more appropriate initial setups by analyzing the student's social media activity. Some or all of the above-described processes in the initial setup unit may be performed using AI, for example, or without AI. For example, the initial setup unit can input social media activity data into a generating AI and have the generating AI execute the proposal of initial setup methods.

[0111] The source code provider can estimate a student's emotions and adjust how the source code is provided based on the estimated emotions. For example, if a student is nervous, the source code provider provides simple and easy-to-read source code. If a student is relaxed, the source code provider provides source code with detailed comments. If a student is excited, the source code provider provides visually stimulating source code. In this way, the source code provider can provide more appropriate source code by adjusting how the source code is provided according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the source code provider may be performed using AI or not using AI. For example, the source code provider can input student emotion data into a generative AI and have the generative AI adjust how the source code is provided.

[0112] The source code provider can analyze a student's past learning history to select the optimal source code provision method when providing source code. For example, the source code provider can provide the optimal source code based on what the student has learned in the past. For example, the source code provider can select the optimal code example from the student's past learning history. For example, the source code provider can analyze the student's past learning history and propose the optimal source code provision method. In this way, the source code provider can select the optimal source code provision method by analyzing the student's past learning history. Some or all of the above processes in the source code provider may be performed using AI, for example, or without using AI. For example, the source code provider can input past learning history data into a generating AI and have the generating AI perform the selection of the source code provision method.

[0113] The source code provider can customize the means of providing source code based on the student's current learning status when providing source code. For example, the source code provider provides source code related to the subject the student is currently studying. For example, the source code provider suggests appropriate source code according to the student's learning progress. For example, the source code provider analyzes the student's current learning status and provides the optimal method of providing source code. As a result, the source code provider can provide more appropriate source code by customizing the means of providing source code based on the student's current learning status. Some or all of the above processing in the source code provider may be performed using AI, for example, or without AI. For example, the source code provider can input current learning status data into a generating AI and have the generating AI perform the customization of the source code provision means.

[0114] The source code provider can estimate a student's emotions and determine the priority of source code provision based on the estimated emotions. For example, if a student is excited, the source code provider will prioritize providing visually stimulating source code. If a student is relaxed, the source code provider will prioritize providing source code that allows for learning in a relaxed state. If a student is tired, the source code provider will prioritize providing simple and easy-to-understand source code. In this way, the source code provider can provide more appropriate source code by determining the priority of source code provision according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the source code provider may be performed using AI or not using AI. For example, the source code provider can input student emotion data into the generative AI and have the generative AI perform the determination of the priority of source code provision.

[0115] The source code provider can select the optimal source code provision method by considering the student's geographical location information when providing source code. For example, if a student is in a specific region, the source code provider will provide source code related to that region. For example, if a student is traveling, the source code provider will provide source code related to their travel destination. For example, if a student is at school, the source code provider will provide source code related to the school curriculum. In this way, the source code provider can provide more appropriate source code by considering the student's geographical location information. Some or all of the above processing in the source code provider may be performed using AI, for example, or without AI. For example, the source code provider can input geographical location data into a generating AI and have the generating AI perform the selection of the source code provision method.

[0116] The source code provider can analyze students' social media activity and propose methods for providing source code when providing it. For example, the source code provider can provide source code related to topics that students have shown interest in on social media. For example, the source code provider can propose source code based on the content of accounts that students follow on social media. For example, the source code provider can analyze students' social media activity history and provide highly relevant source code. In this way, the source code provider can provide more appropriate source code by analyzing students' social media activity. Some or all of the above processing in the source code provider may be performed using AI, for example, or without AI. For example, the source code provider can input social media activity data into a generating AI and have the generating AI execute a proposal for a source code provision method.

[0117] The virtual environment provider can estimate a student's emotions and adjust how the virtual environment is provided based on the estimated emotions. For example, if a student is nervous, the virtual environment provider provides a simple and easy-to-use virtual environment. For example, if a student is relaxed, the virtual environment provider provides a virtual environment with detailed settings. For example, if a student is excited, the virtual environment provider provides a visually stimulating virtual environment. In this way, the virtual environment provider can provide a more appropriate virtual environment by adjusting how the virtual environment is provided according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the virtual environment provider may be performed using AI, for example, or without AI. For example, the virtual environment provider can input student emotion data into a generative AI and have the generative AI adjust how the virtual environment is provided.

[0118] The virtual environment provisioning unit can analyze a student's past learning history and select the optimal virtual environment provisioning method when providing a virtual environment. For example, the virtual environment provisioning unit provides the optimal virtual environment based on the virtual environment settings the student has used in the past. For example, the virtual environment provisioning unit selects the optimal tools and software from the student's past learning history. For example, the virtual environment provisioning unit analyzes the student's past learning history and proposes the optimal virtual environment provisioning method. In this way, the virtual environment provisioning unit can select the optimal virtual environment provisioning method by analyzing the student's past learning history. Some or all of the above processes in the virtual environment provisioning unit may be performed using AI, for example, or without AI. For example, the virtual environment provisioning unit can input past learning history data into a generating AI and have the generating AI perform the selection of the virtual environment provisioning method.

[0119] The virtual environment provider can estimate a student's emotions and determine the priority of virtual environment provision based on the estimated emotions. For example, if a student is excited, the virtual environment provider will prioritize providing a visually stimulating virtual environment. For example, if a student is relaxed, the virtual environment provider will prioritize providing a virtual environment that can be used in a relaxed state. For example, if a student is tired, the virtual environment provider will prioritize providing a simple and easy-to-use virtual environment. In this way, the virtual environment provider can provide a more appropriate virtual environment by determining the priority of virtual environment provision according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the virtual environment provider may be performed using AI, for example, or not using AI. For example, the virtual environment provider can input student emotion data into the generative AI and have the generative AI perform the determination of the priority of virtual environment provision.

[0120] The virtual environment provider unit can select the optimal virtual environment provision method by considering the student's geographical location information when providing a virtual environment. For example, if a student is in a specific region, the virtual environment provider unit provides tools and software related to that region. For example, if a student is traveling, the virtual environment provider unit provides virtual environment settings related to the travel destination. For example, if a student is at school, the virtual environment provider unit provides virtual environment settings related to the school curriculum. In this way, the virtual environment provider unit can provide a more appropriate virtual environment by considering the student's geographical location information. Some or all of the above processing in the virtual environment provider unit may be performed using AI, for example, or without AI. For example, the virtual environment provider unit can input geographical location data into a generating AI and have the generating AI perform the selection of the virtual environment provision method.

[0121] The agent teacher creation unit can estimate a student's emotions and adjust the agent teacher creation method based on the estimated emotions. For example, if a student is nervous, the agent teacher creation unit will create an agent teacher with a calm voice and expression. For example, if a student is relaxed, the agent teacher creation unit will create an agent teacher with a cheerful voice and expression. For example, if a student is excited, the agent teacher creation unit will create a visually stimulating agent teacher. In this way, the agent teacher creation unit can provide a more appropriate agent teacher by adjusting the agent teacher creation method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the agent teacher creation unit may be performed using AI, for example, or not using AI. For example, the agent teacher creation unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the agent teacher creation method.

[0122] The Agent Teacher Creation Unit can analyze a student's past learning history to select the optimal Agent Teacher creation method when creating an Agent Teacher. For example, the Agent Teacher Creation Unit can create the optimal Agent Teacher based on the settings of Agent Teachers the student has used in the past. For example, the Agent Teacher Creation Unit can select the optimal Agent Teacher character from the student's past learning history. For example, the Agent Teacher Creation Unit can analyze the student's past learning history and propose the optimal Agent Teacher creation method. In this way, the Agent Teacher Creation Unit can select the optimal Agent Teacher creation method by analyzing the student's past learning history. Some or all of the above processes in the Agent Teacher Creation Unit may be performed using AI, for example, or without AI. For example, the Agent Teacher Creation Unit can input past learning history data into a generating AI and have the generating AI perform the selection of the Agent Teacher creation method.

[0123] The agent teacher creation unit can estimate a student's emotions and determine the priority for creating agent teachers based on the estimated emotions. For example, if a student is excited, the agent teacher creation unit will prioritize creating a visually stimulating agent teacher. If a student is relaxed, the agent teacher creation unit will prioritize creating an agent teacher that can be used in a relaxed state. If a student is tired, the agent teacher creation unit will prioritize creating a simple and easy-to-use agent teacher. In this way, the agent teacher creation unit can provide a more appropriate agent teacher by determining the priority for creating agent teachers according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the agent teacher creation unit may be performed using AI, for example, or not using AI. For example, the agent teacher creation unit can input student emotion data into a generative AI and have the generative AI determine the priority for creating agent teachers.

[0124] The Agent Teacher Creation Unit can select the optimal Agent Teacher Creation Method by considering the student's geographical location information when creating an Agent Teacher. For example, if the student is in a specific region, the Agent Teacher Creation Unit will create an Agent Teacher related to that region. For example, if the student is traveling, the Agent Teacher Creation Unit will create an Agent Teacher related to the travel destination. For example, if the student is at school, the Agent Teacher Creation Unit will create an Agent Teacher related to the school curriculum. In this way, the Agent Teacher Creation Unit can provide a more appropriate Agent Teacher by considering the student's geographical location information. Some or all of the above processing in the Agent Teacher Creation Unit may be performed using AI, for example, or without AI. For example, the Agent Teacher Creation Unit can input geographical location data into a generating AI and have the generating AI select the Agent Teacher Creation Method.

[0125] The Agent Teacher Creation Unit can analyze a student's social media activity and suggest an Agent Teacher character when creating an Agent Teacher. For example, the Agent Teacher Creation Unit can create an Agent Teacher based on a character the student has shown interest in on social media. For example, the Agent Teacher Creation Unit can suggest an Agent Teacher based on the content of accounts the student follows on social media. For example, the Agent Teacher Creation Unit can analyze a student's social media activity history and provide a highly relevant Agent Teacher. In this way, the Agent Teacher Creation Unit can provide a more appropriate Agent Teacher by analyzing the student's social media activity. Some or all of the above processes in the Agent Teacher Creation Unit may be performed using AI, for example, or not using AI. For example, the Agent Teacher Creation Unit can input social media activity data into a generating AI and have the generating AI suggest an Agent Teacher character.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The learning support system can also include a learning style analysis unit that analyzes students' learning styles. This unit analyzes students' past learning history and behavioral patterns to identify their learning style. For example, students who prefer visual learning can be provided with materials that heavily utilize visual aids. Students who prefer auditory learning can be provided with audio guides or podcast-style materials. Furthermore, students who prefer hands-on learning can be provided with interactive simulations and experiments. This allows the learning support system to provide the optimal learning method tailored to each student's learning style.

[0128] The learning support system can also include a progress monitoring unit that monitors students' learning progress in real time. The progress monitoring unit understands how far students are progressing in their studies and provides feedback as needed. For example, if a student is falling behind on a particular assignment, the progress monitoring unit can provide additional support or resources. Furthermore, if a student achieves a goal, the progress monitoring unit can provide rewards and praise to enhance their sense of accomplishment. In addition, the progress monitoring unit can analyze students' learning data and suggest the next tasks and goals they should tackle. This allows the learning support system to effectively support students' learning progress.

[0129] The learning support system can also be equipped with a motivation enhancement section to further improve students' learning motivation. This section provides various methods to increase students' desire to learn. For example, it can offer learning content incorporating game elements, allowing students to learn while having fun. Furthermore, the motivation enhancement section can introduce badges or point systems to recognize students' efforts, allowing them to feel a sense of accomplishment. Additionally, the motivation enhancement section can provide group learning functions to promote competition and cooperation among students. In this way, the learning support system can increase students' learning motivation and improve learning effectiveness.

[0130] The learning support system can also be equipped with an environment optimization unit to further optimize the student's learning environment. This unit has various functions to provide an environment where students can concentrate on their studies. For example, it can suggest the optimal learning environment based on the student's study time and location. It can also provide a noise cancellation function to reduce noise that occurs during student study. Furthermore, it can monitor the student's study posture and lighting conditions and provide appropriate advice. In this way, the learning support system can help students progress in an optimal learning environment.

[0131] The learning support system can also include an evaluation unit to assess students' learning outcomes. This evaluation unit comprehensively evaluates students' learning outcomes and provides feedback. For example, it can analyze students' test results and assignment submission status to conduct a comprehensive evaluation. It can also evaluate students' learning processes and identify areas for improvement. Furthermore, the evaluation unit can incorporate student self-assessment and peer assessment to provide a more multifaceted evaluation. This allows the learning support system to accurately assess students' learning outcomes and provide appropriate feedback.

[0132] The learning support system may also include an emotion adjustment unit that estimates the student's emotions and adjusts the learning content based on those emotions. The emotion adjustment unit monitors the student's emotional state in real time and provides appropriate learning content. For example, if a student is stressed, the emotion adjustment unit can provide content that helps them relax. If a student is excited, the unit can provide content to improve their concentration. Furthermore, if a student is tired, the unit can provide content to encourage them to take a break. This allows the learning support system to provide optimal learning content tailored to the student's emotions.

[0133] The learning support system may also include a progress adjustment unit that estimates the student's emotions and adjusts the learning progress based on those emotions. The progress adjustment unit monitors the student's emotional state in real time and appropriately adjusts the learning progress. For example, if a student is focused, the progress adjustment unit can provide support to ensure smooth progress. If a student is tired, the progress adjustment unit can temporarily slow down the learning progress to encourage a break. Furthermore, if a student is stressed, the progress adjustment unit can adjust the learning progress to provide support that helps them relax. In this way, the learning support system can support optimal learning progress tailored to the student's emotions.

[0134] The learning support system may also include a feedback adjustment unit that estimates the student's emotions and adjusts learning feedback based on those emotions. The feedback adjustment unit monitors the student's emotional state in real time and provides appropriate feedback. For example, if a student is feeling down, the feedback adjustment unit can offer words of encouragement or positive feedback. If a student is excited, the unit can offer advice to help them calm down and continue learning. Furthermore, if a student is relaxed, the unit can provide specific feedback to guide them to the next step. This allows the learning support system to provide optimal feedback tailored to the student's emotions.

[0135] The learning support system may also include a goal-setting adjustment unit that estimates the student's emotions and adjusts learning goals based on those emotions. The goal-setting adjustment unit monitors the student's emotional state in real time and sets appropriate learning goals. For example, if a student is highly motivated, the goal-setting adjustment unit can set challenging goals. If a student is tired, the unit can set smaller, achievable goals. Furthermore, if a student is stressed, the unit can set goals that promote relaxation. This allows the learning support system to set optimal learning goals tailored to the student's emotions.

[0136] The learning support system may also include a scheduling unit that estimates the student's emotions and adjusts the learning schedule based on those emotions. The scheduling unit monitors the student's emotional state in real time and provides an appropriate learning schedule. For example, if the student is concentrating, the scheduling unit can extend the learning time. If the student is tired, the scheduling unit can increase the break time. Furthermore, if the student is stressed, the scheduling unit can provide time for relaxation. This allows the learning support system to provide an optimal learning schedule tailored to the student's emotions.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The reception desk receives prompts to understand what students want to do. For example, it can receive text or audio input from students. The reception desk can also use AI to analyze the content of the prompts and understand the students' intentions. Step 2: The analysis unit analyzes the information based on the prompt received by the reception unit. For example, it may use natural language processing technology to analyze the content of the prompt and identify what the student wants to do. It can also use AI to analyze the content of the prompt and extract the necessary information. Step 3: The environment construction unit constructs the environment based on the information analyzed by the analysis unit. For example, it sets up the development environment and installs the necessary tools. It is also possible to automate the environment construction using AI. Step 4: The initial setup section performs the initial setup of the environment built by the environment construction section. For example, it may edit configuration files or create user accounts. Initial setup can also be automated using AI. Step 5: The source code provision unit provides specific source code in the environment configured by the initial setup unit. For example, it provides program templates or code to implement specific functions. It can also generate source code using AI.

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0140] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0141] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, environment construction unit, initial setup unit, source code provision unit, virtual environment provision unit, and agent teacher creation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input from students. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the student's intentions. The environment construction unit is implemented by the specific processing unit 290 of the data processing unit 12 and constructs the necessary environment. The initial setup unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs the initial setup of the environment. The source code provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific source code. The virtual environment provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a virtual environment. The agent teacher creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an agent teacher tailored to the student. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, environment construction unit, initial setup unit, source code provision unit, virtual environment provision unit, and agent teacher creation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input from the student. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the student's intentions. The environment construction unit is implemented by the specific processing unit 290 of the data processing unit 12 and constructs the necessary environment. The initial setup unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs the initial setup of the environment. The source code provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific source code. The virtual environment provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a virtual environment. The agent teacher creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an agent teacher tailored to the student. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, analysis unit, environment construction unit, initial setup unit, source code provision unit, virtual environment provision unit, and agent teacher creation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives input from students. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the student's intentions. The environment construction unit is implemented by the specific processing unit 290 of the data processing unit 12 and constructs the necessary environment. The initial setup unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs the initial setup of the environment. The source code provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific source code. The virtual environment provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a virtual environment. The agent teacher creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an agent teacher tailored to the student. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] As shown in Figure 7, the 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.

[0177] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0178] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0179] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0181] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0182] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0183] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0184] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0185] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0186] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0187] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0188] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0189] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0190] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0191] Each of the multiple elements described above, including the reception unit, analysis unit, environment construction unit, initial setup unit, source code provision unit, virtual environment provision unit, and agent teacher creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives input from students. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the intentions of students. The environment construction unit is implemented by the specific processing unit 290 of the data processing unit 12 and constructs the necessary environment. The initial setup unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs the initial setup of the environment. The source code provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific source code. The virtual environment provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a virtual environment. The agent teacher creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an agent teacher tailored to the student. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0192] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0193] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0194] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0195] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0196] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0197] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0199] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0200] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0201] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0202] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0203] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0204] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0205] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0206] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0207] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0208] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0209] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0210] (Note 1) A reception desk that accepts prompts to understand what students "want to do," An analysis unit analyzes information based on the prompt received by the reception unit, An environment construction unit constructs an environment based on the information analyzed by the aforementioned analysis unit, An initial setup unit that performs the initial setup of the environment constructed by the aforementioned environment construction unit, The system comprises a source code providing unit that provides specific source code in the environment initialized by the initial setup unit. A system characterized by the following features. (Note 2) It includes a virtual environment provisioning unit that provides virtual environments. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an agent teacher creation department that creates agent teachers tailored to each individual student. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Using generative AI to understand what students "want to do". The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned environment construction unit, We will use generative AI to build the necessary environment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The initial setting unit is, Initial setup is performed using a generator AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned source code provision unit is: We will provide specific source code using a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the student's emotions and adjusts the timing of prompt responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the student's past prompt history to select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a prompt is received, filtering is performed based on the student's current learning situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the student's emotions and determines the priority of prompts to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When students receive prompts, their geographical location is taken into consideration to prioritize the reception of the most relevant prompts. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When a prompt is received, the system analyzes the student's social media activity and receives relevant prompts. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the students' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the prompts. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the prompt category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the students' emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when prompts are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the prompts. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned environment construction unit, We estimate students' emotions and adjust the method of creating the learning environment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned environment construction unit, During environment setup, the system analyzes students' past learning history to select the optimal environment setup method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned environment construction unit, During environment setup, customize the setup method based on the student's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned environment construction unit, We estimate students' emotions and determine the priority of environment design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned environment construction unit, When setting up the environment, the optimal environment setup method will be selected considering the students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned environment construction unit, When setting up the environment, we will analyze students' social media activity and propose methods for setting up the environment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The initial setting unit is, The system estimates the students' emotions and adjusts the initial setup method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The initial setting unit is, During initial setup, the system analyzes the student's past setup history to select the optimal initial setup method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The initial setting unit is, During initial setup, customize the initial setup method based on the student's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The initial setting unit is, The system estimates the students' emotions and determines the priority of initial settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The initial setting unit is, During initial setup, the optimal setup method is selected considering the students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The initial setting unit is, During the initial setup, we analyze students' social media activity and suggest methods for initial setup. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned source code provision unit is: The system estimates students' emotions and adjusts how the source code is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned source code provision unit is: When providing source code, the system analyzes students' past learning history to select the most suitable method for providing the source code. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned source code provision unit is: When providing source code, customize the method of providing the source code based on the student's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned source code provision unit is: The system estimates students' emotions and determines the priority of source code provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned source code provision unit is: When providing source code, the most suitable method of providing the source code will be selected, taking into account the students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned source code provision unit is: When providing source code, we will analyze students' social media activity and propose methods for providing the source code. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned virtual environment provisioning unit, The system estimates students' emotions and adjusts how the virtual environment is provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned virtual environment provisioning unit, When providing a virtual environment, the system analyzes students' past learning history to select the optimal method for providing the virtual environment. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned virtual environment provisioning unit, The system estimates students' emotions and determines the priority of providing virtual environments based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned virtual environment provisioning unit, When providing a virtual environment, the optimal method of providing the virtual environment will be selected, taking into account the students' geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 42) The aforementioned Agent Teacher creation unit is, The system estimates the students' emotions and adjusts how the agent teacher is created based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned Agent Teacher creation unit is, When creating an agent teacher, the system analyzes the student's past learning history to select the most suitable method for creating the agent teacher. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned Agent Teacher creation unit is, The system estimates the students' emotions and determines the priority for creating agent teachers based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned Agent Teacher creation unit is, When creating an agent teacher, the optimal method for creating the agent teacher is selected, taking into account the student's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 46) The aforementioned Agent Teacher creation unit is, When creating an agent teacher, we analyze students' social media activity to suggest an agent teacher's character. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives prompts to understand what students want to do, An analysis unit analyzes information based on the prompt received by the reception unit, An environment construction unit constructs an environment based on the information analyzed by the aforementioned analysis unit, An initial setup unit that performs the initial setup of the environment constructed by the aforementioned environment construction unit, The system comprises a source code providing unit that provides specific source code in the environment initialized by the initial setup unit. A system characterized by the following features.

2. It includes a virtual environment provisioning unit that provides virtual environments. The system according to feature 1.

3. It features an agent teacher creation department that creates agent teachers tailored to each individual student. The system according to feature 1.

4. The aforementioned analysis unit, Using generative AI to understand what students want to do. The system according to feature 1.

5. The aforementioned environment construction unit, We will use generative AI to build the necessary environment. The system according to feature 1.

6. The initial setting unit is, Initial setup is performed using a generation AI. The system according to feature 1.

7. The aforementioned source code provision unit is: We provide specific source code using generative AI. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the student's emotions and adjusts the timing of prompt responses based on those emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze the student's past prompt history to select the optimal registration method. The system according to feature 1.

10. The aforementioned reception unit is When a prompt is received, filtering is performed based on the student's current learning situation and areas of interest. The system according to feature 1.

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