system

Abstract

An object of the system according to the embodiment is to provide educational content customized according to a student's learning style and to realize an immersive learning experience.SOLUTION: A system according to an embodiment includes a learning style analyzer, an educational content provider, and a VR participant. The learning style analyzer analyzes the student's learning style and progress. The educational content providing unit provides the customized educational content based on the result analyzed by the learning style analyzing unit. The VR participant leverages VR technology to join an interactive and immersive class.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies struggle to provide customized educational content tailored to students' individual learning styles, and there is room for improvement in providing an immersive learning experience.

[0005] The system according to the embodiment aims to provide customized educational content according to the learning style of each student, thereby realizing an immersive learning experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning style analysis unit, an educational content provision unit, and a VR participation unit. The learning style analysis unit analyzes a student's learning style and progress. The educational content provision unit provides customized educational content based on the results of the analysis by the learning style analysis unit. The VR participation unit utilizes VR technology to allow students to participate in interactive and immersive classes. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized educational content according to the student's learning style, and can realize an immersive learning experience. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The virtual classroom platform according to an embodiment of the present invention is a system that uses AI to analyze each student's learning style and progress, provides customized educational content in real time, and utilizes VR technology to allow students to participate in interactive and immersive classes, thereby enabling the virtual classroom platform to provide high-quality education and an optimized learning environment for each student regardless of their physical location.

[0029] A virtual classroom platform according to an embodiment includes a learning style analysis unit, an educational content provision unit, and a VR participation unit. The learning style analysis unit analyzes a student's learning style and progress. For example, it analyzes the student's past learning data, test results, and learning behavior data to determine the most effective learning method and the areas in which the student is struggling. The educational content provision unit provides customized educational content based on the results of the analysis by the learning style analysis unit. For example, if a student is struggling in a particular area of ​​mathematics, it provides supplementary materials and practice problems specific to that area. It also provides content in an optimal format, such as visual materials or interactive quizzes, depending on the student's learning style. The VR participation unit utilizes VR technology to enable students to participate in interactive and immersive classes. For example, by wearing VR goggles, students can enter a virtual classroom and communicate with teachers and other students in real time. Furthermore, experiments and simulations can be conducted in the VR environment, providing a learning experience that cannot be experienced in a physical classroom. As a result, the virtual classroom platform according to an embodiment can provide an optimized learning environment for each student and improve learning efficiency.

[0030] The learning style analysis unit can analyze a student's learning rhythm and suggest optimal study times. For example, the learning style analysis unit analyzes a student's past learning data and evaluates performance for each study time period. For example, if a student has high learning efficiency in the morning, it can suggest that they concentrate their studies during that time period. This makes it possible to suggest optimal study times that maximize a student's learning efficiency.

[0031] The learning style analysis unit analyzes a student's facial expressions and voice while studying, and can evaluate their level of concentration and comprehension in real time. For example, the learning style analysis unit captures a student's facial expressions while studying with a camera and uses facial expression recognition technology to evaluate their level of concentration. For example, if a student has a frown on their face, it is determined that they are concentrating. The learning style analysis unit also records the student's voice while studying and uses voice recognition technology to evaluate their level of comprehension. For example, it analyzes the tone and speed of their voice to evaluate their level of comprehension. The learning style analysis unit also combines data on concentration and comprehension to make a comprehensive evaluation. For example, if both the level of concentration and the level of comprehension are high, it is determined that their studies are progressing smoothly. This makes it possible to evaluate a student's level of concentration and comprehension in real time and provide appropriate support.

[0032] The learning style analysis unit can analyze a student's learning style based on at least one external factor, such as the student's home environment or lifestyle. The learning style analysis unit, for example, collects data on the student's home environment and reflects this in the analysis of the student's learning style. For example, it evaluates whether the student's home learning environment is in good condition. The learning style analysis unit also collects data on the student's lifestyle and reflects this in the analysis of the student's learning style. For example, it evaluates the student's sleep pattern and dietary quality. The learning style analysis unit also combines data on the home environment and lifestyle to make a comprehensive evaluation. For example, if the student's home environment is in good condition and their lifestyle is also good, it determines that the student's learning style is good. This enables a comprehensive analysis of the student's learning style that takes into account the student's home environment and lifestyle.

[0033] The learning style analysis unit can optimize learning styles by sharing student learning data with other educational institutions and conducting benchmarking. The learning style analysis unit, for example, anonymizes student learning data and builds a system for sharing it with other educational institutions. For example, it compares data from students in the same grade. The learning style analysis unit also conducts benchmarking based on data provided by other educational institutions. For example, it compares standardized test results and aims to optimize learning styles. The learning style analysis unit also suggests improvements to learning styles based on the benchmarking results. For example, it suggests reviewing learning methods in specific fields. In this way, learning styles can be optimized by sharing data with other educational institutions.

[0034] The educational content providing unit can predict what content should be learned next based on the student's learning history and suggest advanced learning. For example, the educational content providing unit analyzes the student's past learning history and predicts what content should be learned next. For example, once the student has understood the basics of mathematics, it will suggest applied problems to solve next. The educational content providing unit also combines learning history and learning outcome data to make a comprehensive evaluation. For example, if the student's learning outcome in a particular field is high, it will suggest applied problems in that field. The educational content providing unit also provides learning materials for advanced learning. For example, it provides preparatory materials and additional learning resources. This makes it possible to predict what content should be learned next based on the student's learning history and suggest advanced learning.

[0035] The educational content providing unit can set an optimal learning pace and manage progress according to the student's learning style. The educational content providing unit, for example, analyzes the student's learning style and sets an optimal learning pace. For example, it can speed up the progress of students who understand quickly and provide supplementary materials to students who understand slowly. The educational content providing unit also combines learning pace and progress data to make a comprehensive evaluation. For example, if the learning pace is appropriate, it can suggest maintaining that pace. The educational content providing unit also builds a system for managing progress. For example, it can monitor test scores and assignment submission status in real time. This makes it possible to set an optimal learning pace according to the student's learning style and manage progress.

[0036] The educational content providing unit can suggest different learning modes according to the student's learning needs. For example, the educational content providing unit analyzes the student's learning needs and suggests the optimal learning mode. For example, it provides game-style learning materials to make learning fun. The educational content providing unit also combines data on learning needs and learning outcomes to make a comprehensive assessment. For example, if project-based learning is effective, it suggests that learning mode. The educational content providing unit also provides learning materials for different learning modes. For example, it provides game-style learning materials and project-based learning materials. This makes it possible to suggest different learning modes according to the student's learning needs.

[0037] The educational content providing unit can suggest collaboration with other students based on the student's learning data and promote collaborative learning. For example, the educational content providing unit analyzes the student's learning data and suggests collaboration with other students. For example, it matches students with the same interests. The educational content providing unit also combines learning data and learning outcome data to perform a comprehensive evaluation. For example, it groups students who are highly interested in a particular topic. The educational content providing unit also provides projects and assignments for collaborative learning. For example, it sets up group work and discussion forums. This makes it possible to suggest collaboration with other students based on the student's learning data and promote collaborative learning.

[0038] The VR participation unit can analyze students' movements within the VR environment and suggest optimal interaction methods. For example, the VR participation unit captures students' movements within the VR environment and suggests optimal interaction methods. For example, it analyzes hand movements and gaze direction. The VR participation unit also combines student movements with learning outcome data to make a comprehensive evaluation. For example, if a specific movement has an impact on learning outcomes, it will recommend that movement. The VR participation unit also provides guidelines for interaction methods. For example, it will suggest how to use gesture controls or voice commands. This allows the VR participation unit to analyze students' movements within the VR environment and suggest optimal interaction methods.

[0039] The VR participation department can use VR technology to virtually recreate actual field trips or experiments and provide a practical learning experience. For example, the VR participation department uses VR technology to virtually recreate actual field trips. For example, historical places or natural environments can be experienced in VR. The VR participation department can also virtually recreate experiments. For example, chemistry experiments or physics experiments can be simulated in VR. The VR participation department can also comprehensively evaluate the field trip or experiment data. For example, it can evaluate the impact of the virtual experience on learning outcomes. This allows the virtual recreation of actual field trips or experiments to provide a practical learning experience.

[0040] The VR Participation Department uses VR technology to create virtual worlds with different cultures and historical backgrounds, which can promote intercultural understanding. For example, the VR Participation Department uses VR technology to create virtual worlds with different cultures and historical backgrounds. For example, it provides virtual tours of ancient Egypt or medieval Europe. The VR Participation Department also performs comprehensive evaluations based on data from the virtual world. For example, it evaluates the impact of intercultural understanding on learning outcomes. The VR Participation Department also provides teaching materials to promote intercultural understanding. For example, it provides video teaching materials and interactive exercises related to different cultures. This allows the creation of virtual worlds with different cultures and historical backgrounds, which can promote intercultural understanding.

[0041] The VR participation department can add a function to share learning outcomes in the VR environment with other students and teachers and receive feedback. For example, the VR participation department builds a system for sharing learning outcomes in the VR environment with other students and teachers. For example, it shares project and experimental results in VR. The VR participation department also provides a function to receive feedback on shared learning outcomes. For example, it receives comments and peer reviews from teachers. The VR participation department also performs comprehensive evaluation based on the feedback data. For example, it evaluates the impact of feedback on learning outcomes. This allows learning outcomes in the VR environment to be shared with other students and teachers and receive feedback.

[0042] The interactive class realization unit uses AI to analyze students' comments or questions in real time and can provide feedback at the appropriate time. The interactive class realization unit, for example, builds a system in which AI analyzes students' comments and questions in real time and provides appropriate feedback. For example, it provides immediate answers to questions. The interactive class realization unit also makes a comprehensive evaluation based on data on comments and questions. For example, it analyzes the frequency and content of questions and provides appropriate feedback. The interactive class realization unit also provides guidelines for feedback. For example, it provides detailed explanations and additional teaching materials for questions. This makes it possible to analyze students' comments and questions in real time and provide feedback at the appropriate time.

[0043] The interactive class realization unit uses AI to moderate discussions between students and promote constructive debate. For example, the interactive class realization unit builds a system in which AI moderates discussions between students and promotes constructive discussions. For example, it supports the progress of the discussion. The interactive class realization unit also makes a comprehensive evaluation based on discussion data. For example, it analyzes how the discussion is progressed and the roles of the participants to promote constructive discussions. The interactive class realization unit also provides guidelines for discussions. For example, it provides criteria for evaluating the diversity of opinions and the depth of the discussion. This makes it possible to moderate discussions between students and promote constructive discussions.

[0044] The interactive class realization unit allows AI to dynamically change students' roles within an interactive class, promoting learning from different perspectives. The interactive class realization unit builds a system in which AI dynamically changes students' roles to promote learning from different perspectives. For example, by switching the discussion leader or recorder. The interactive class realization unit also performs a comprehensive evaluation based on data on role changes. For example, it evaluates whether learning outcomes improve by experiencing different roles. The interactive class realization unit also provides guidelines for role changes. For example, it suggests how to assign roles and when to change roles. This allows AI to dynamically change students' roles within an interactive class, promoting learning from different perspectives.

[0045] The interactive class realization unit can add a function that records interactions within a class and allows them to be reviewed later. The interactive class realization unit, for example, builds a system that records interactions within a class and allows them to be reviewed later. For example, it records and films the content of discussions. The interactive class realization unit also performs comprehensive evaluation based on the recorded interaction data. For example, it analyzes the content of the discussion and the comments of participants and evaluates their impact on learning outcomes. The interactive class realization unit also provides guidelines for review. For example, it recommends playing back the recording or reviewing notes. This makes it possible to add a function that records interactions within a class and allows them to be reviewed later.

[0046] The learning outcome assessment unit uses AI to evaluate students' learning outcomes from multiple angles and can suggest specific areas for improvement. For example, the learning outcome assessment unit builds a system in which AI evaluates students' learning outcomes from multiple angles and suggests specific areas for improvement. For example, it provides feedback based on test results and assignment evaluations. The learning outcome assessment unit also makes a comprehensive assessment based on learning outcome data. For example, it combines multiple evaluation indicators and data from different evaluators for evaluation. The learning outcome assessment unit also provides guidelines for suggesting specific areas for improvement. For example, it suggests improvements to learning methods and time management. This makes it possible to evaluate students' learning outcomes from multiple angles and suggest specific areas for improvement.

[0047] The learning outcome assessment department can track students' learning outcomes over the long term and visualize their growth process. For example, the learning outcome assessment department builds a system that tracks students' learning outcomes over the long term and visualizes their growth process. For example, it displays grades for each semester and progress on assignments in graphs. The learning outcome assessment department also performs comprehensive assessments based on long-term data. For example, it evaluates progress in skill improvement and goal achievement. The learning outcome assessment department also provides guidelines for visualizing the growth process. For example, it recommends the use of graph displays and dashboards. This makes it possible to track students' learning outcomes over the long term and visualize their growth process.

[0048] The learning outcome assessment unit can add a function to share students' learning outcomes with other students and teachers and receive peer reviews. The learning outcome assessment unit, for example, builds a system for sharing students' learning outcomes with other students and teachers and adds a function to receive peer reviews. For example, it receives feedback from other students after submitting an assignment. The learning outcome assessment unit also performs a comprehensive assessment based on the peer review data. For example, it analyzes the assessment criteria and the content of the feedback and evaluates the impact on learning outcomes. The learning outcome assessment unit also provides guidelines for peer reviews. For example, it clarifies the assessment criteria and the content of the feedback. This makes it possible to add a function to share students' learning outcomes with other students and teachers and receive peer reviews.

[0049] The learning outcome assessment unit uses AI to suggest the next learning step based on the learning outcomes, thereby supporting continuous learning. The learning outcome assessment unit, for example, builds a system in which AI suggests the next learning step based on learning outcomes. For example, it may suggest the next learning content based on test results. The learning outcome assessment unit also combines learning outcomes with data on the next learning step to provide a comprehensive assessment. For example, it may suggest the next learning step based on curriculum progress and individual levels of understanding. The learning outcome assessment unit also provides guidelines to support continuous learning. For example, it may provide regular assessments and additional learning resources. This makes it possible to suggest the next learning step based on learning outcomes and support continuous learning.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The learning style analysis unit can analyze a student's posture and movements while studying and evaluate their level of concentration and fatigue. For example, if a student stays in the same position for a long time, it will determine that fatigue is building up and suggest that they take a break. The learning style analysis unit also evaluates the student's level of concentration based on the student's movement data. For example, if the student changes posture frequently, it will determine that their concentration is declining. Furthermore, the learning style analysis unit combines posture and movement data to make a comprehensive evaluation. For example, if the student has good posture and moves little, it will determine that their level of concentration is high. This makes it possible to analyze a student's posture and movements, evaluate their level of concentration and fatigue, and provide appropriate support.

[0052] The educational content provider can predict a student's learning progress based on their learning history and suggest review at the appropriate time. For example, if a certain period of time has passed since they studied a particular topic, it can suggest reviewing that topic. The educational content provider also combines learning history and learning outcome data to comprehensively evaluate the student. For example, it can identify topics that need review based on past test results. The educational content provider also provides materials for review. For example, it can provide past test questions and supplementary materials. This allows it to suggest review at the appropriate time based on the student's learning history.

[0053] The VR Participation Department can analyze students' movements within the VR environment and suggest optimal interaction methods. For example, it can analyze hand movements and gaze direction within the VR environment and suggest optimal operation methods. The VR Participation Department also combines data on student movements and learning outcomes to comprehensively evaluate them. For example, it can evaluate the impact of specific movements on learning outcomes. The VR Participation Department also provides guidelines for interaction methods. For example, it can suggest how to use gesture controls or voice commands. This allows the VR Participation Department to analyze students' movements within the VR environment and suggest optimal interaction methods.

[0054] The interactive class realization unit uses AI to analyze students' comments or questions in real time and provide feedback at the appropriate time. For example, a system can be built in which AI analyzes students' comments and questions in real time and provides appropriate feedback. For example, it can provide immediate answers to questions. The interactive class realization unit also makes a comprehensive evaluation based on the data on comments and questions. For example, it can analyze the frequency and content of questions and provide appropriate feedback. The interactive class realization unit also provides guidelines for feedback. For example, it can provide detailed explanations for questions or additional teaching materials. This makes it possible to analyze students' comments and questions in real time and provide feedback at the appropriate time.

[0055] The learning outcomes assessment unit can add a function that allows students to share their learning outcomes with other students and teachers and receive peer reviews. For example, a system can be built to share students' learning outcomes with other students and teachers, and a function can be added to receive peer reviews. For example, a system can be built to receive feedback from other students after an assignment is submitted. The learning outcomes assessment unit can also perform a comprehensive assessment based on the peer review data. For example, it can analyze the assessment criteria and the content of the feedback, and evaluate the impact on learning outcomes. The learning outcomes assessment unit can also provide guidelines for peer reviews. For example, it can clarify the assessment criteria and the content of the feedback. This makes it possible to add a function that allows students to share their learning outcomes with other students and teachers and receive peer reviews.

[0056] The Learning Outcome Assessment Department can track students' learning outcomes over the long term and visualize their growth process. For example, a system can be built to track students' learning outcomes over the long term and visualize their growth process. For example, semester grades and assignment progress can be displayed in graphs. The Learning Outcome Assessment Department can also provide comprehensive evaluations based on long-term data. For example, it can evaluate progress in skill improvement and goal achievement. The Learning Outcome Assessment Department can also provide guidelines for visualizing the growth process. For example, it can recommend the use of graph displays and dashboards. This makes it possible to track students' learning outcomes over the long term and visualize their growth process.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The Learning Style Analysis Department analyzes students' learning styles and progress. Specifically, it analyzes students' past learning data, test results, and learning behavior data to determine the most effective learning methods and areas where they are struggling. Step 2: The educational content provider provides customized educational content based on the results of the learning style analysis. For example, if a student is struggling with a particular area of ​​mathematics, the provider will provide supplementary materials and practice questions specific to that area. The content provider will also provide the content in the most appropriate format, such as visual materials or interactive quizzes, depending on the student's learning style. Step 3: VR participation utilizes VR technology to provide interactive and immersive classes. For example, by wearing VR goggles, students can enter a virtual classroom and communicate with teachers and other students in real time. Also, experiments and simulations can be conducted in the VR environment, providing a learning experience that cannot be experienced in a physical classroom.

[0059] (Example 2) The virtual classroom platform according to an embodiment of the present invention is a system that uses AI to analyze each student's learning style and progress, provides customized educational content in real time, and utilizes VR technology to allow students to participate in interactive and immersive classes, thereby enabling the virtual classroom platform to provide high-quality education and an optimized learning environment for each student regardless of their physical location.

[0060] A virtual classroom platform according to an embodiment includes a learning style analysis unit, an educational content provision unit, and a VR participation unit. The learning style analysis unit analyzes a student's learning style and progress. For example, it analyzes the student's past learning data, test results, and learning behavior data to determine the most effective learning method and the areas in which the student is struggling. The educational content provision unit provides customized educational content based on the results of the analysis by the learning style analysis unit. For example, if a student is struggling in a particular area of ​​mathematics, it provides supplementary materials and practice problems specific to that area. It also provides content in an optimal format, such as visual materials or interactive quizzes, depending on the student's learning style. The VR participation unit utilizes VR technology to enable students to participate in interactive and immersive classes. For example, by wearing VR goggles, students can enter a virtual classroom and communicate with teachers and other students in real time. Furthermore, experiments and simulations can be conducted in the VR environment, providing a learning experience that cannot be experienced in a physical classroom. As a result, the virtual classroom platform according to an embodiment can provide an optimized learning environment for each student and improve learning efficiency.

[0061] The learning style analysis unit can analyze a student's learning rhythm and suggest optimal study times. For example, the learning style analysis unit analyzes a student's past learning data and evaluates performance for each study time period. For example, if a student has high learning efficiency in the morning, it can suggest that they concentrate their studies during that time period. This makes it possible to suggest optimal study times that maximize a student's learning efficiency.

[0062] The learning style analysis unit analyzes a student's facial expressions and voice while studying, and can evaluate their level of concentration and comprehension in real time. For example, the learning style analysis unit captures a student's facial expressions while studying with a camera and uses facial expression recognition technology to evaluate their level of concentration. For example, if a student has a frown on their face, it is determined that they are concentrating. The learning style analysis unit also records the student's voice while studying and uses voice recognition technology to evaluate their level of comprehension. For example, it analyzes the tone and speed of their voice to evaluate their level of comprehension. The learning style analysis unit also combines data on concentration and comprehension to make a comprehensive evaluation. For example, if both the level of concentration and the level of comprehension are high, it is determined that their studies are progressing smoothly. This makes it possible to evaluate a student's level of concentration and comprehension in real time and provide appropriate support.

[0063] The learning style analysis unit uses an emotion estimation function to analyze a student's motivation and stress level for learning, and can provide appropriate support. The learning style analysis unit, for example, analyzes a student's facial expressions and voice, and uses the emotion estimation function to evaluate their motivation and stress level. For example, if they smile a lot, it determines that they are highly motivated. The learning style analysis unit also collects biometric data such as heart rate and electrodermal activity using sensors to evaluate their stress level. For example, if their heart rate is high, it determines that they are highly stressed. The learning style analysis unit also combines the motivation and stress level data to make a comprehensive evaluation. For example, if their motivation is high and their stress level is low, it determines that their studies are progressing smoothly. This makes it possible to analyze a student's motivation and stress level and provide appropriate support.

[0064] The learning style analysis unit can analyze a student's learning style based on at least one external factor, such as the student's home environment or lifestyle. The learning style analysis unit, for example, collects data on the student's home environment and reflects this in the analysis of the student's learning style. For example, it evaluates whether the student's home learning environment is in good condition. The learning style analysis unit also collects data on the student's lifestyle and reflects this in the analysis of the student's learning style. For example, it evaluates the student's sleep pattern and dietary quality. The learning style analysis unit also combines data on the home environment and lifestyle to make a comprehensive evaluation. For example, if the student's home environment is in good condition and their lifestyle is also good, it determines that the student's learning style is good. This enables a comprehensive analysis of the student's learning style that takes into account the student's home environment and lifestyle.

[0065] The learning style analysis unit can optimize learning styles by sharing student learning data with other educational institutions and conducting benchmarking. The learning style analysis unit, for example, anonymizes student learning data and builds a system for sharing it with other educational institutions. For example, it compares data from students in the same grade. The learning style analysis unit also conducts benchmarking based on data provided by other educational institutions. For example, it compares standardized test results and aims to optimize learning styles. The learning style analysis unit also suggests improvements to learning styles based on the benchmarking results. For example, it suggests reviewing learning methods in specific fields. In this way, learning styles can be optimized by sharing data with other educational institutions.

[0066] The learning style analysis unit uses the emotion estimation function to track changes in students' emotions toward learning over the long term, which can be used to improve learning plans. The learning style analysis unit, for example, tracks students' emotions while studying over the long term, which can be used to improve learning plans. For example, it can identify periods when motivation is declining and take measures. The learning style analysis unit also uses the emotion estimation function to monitor changes in students' emotions in real time. For example, it can evaluate emotions based on facial expressions and voice. The learning style analysis unit also combines data on emotional changes and learning outcomes to make a comprehensive evaluation. For example, if learning outcomes are improving during periods when emotions are stable, it can suggest continuing that learning plan. This makes it possible to track changes in students' emotions over the long term, which can be used to improve learning plans.

[0067] The educational content providing unit can predict what content should be learned next based on the student's learning history and suggest advanced learning. For example, the educational content providing unit analyzes the student's past learning history and predicts what content should be learned next. For example, once the student has understood the basics of mathematics, it will suggest applied problems to solve next. The educational content providing unit also combines learning history and learning outcome data to make a comprehensive evaluation. For example, if the student's learning outcome in a particular field is high, it will suggest applied problems in that field. The educational content providing unit also provides learning materials for advanced learning. For example, it provides preparatory materials and additional learning resources. This makes it possible to predict what content should be learned next based on the student's learning history and suggest advanced learning.

[0068] The educational content providing unit can set an optimal learning pace and manage progress according to the student's learning style. The educational content providing unit, for example, analyzes the student's learning style and sets an optimal learning pace. For example, it can speed up the progress of students who understand quickly and provide supplementary materials to students who understand slowly. The educational content providing unit also combines learning pace and progress data to make a comprehensive evaluation. For example, if the learning pace is appropriate, it can suggest maintaining that pace. The educational content providing unit also builds a system for managing progress. For example, it can monitor test scores and assignment submission status in real time. This makes it possible to set an optimal learning pace according to the student's learning style and manage progress.

[0069] The educational content providing unit can use the emotion estimation function to identify topics that students are most interested in and provide content based on them. The educational content providing unit, for example, uses the emotion estimation function to identify topics that students are most interested in. For example, it provides learning materials related to the topics that students have shown interest in. The educational content providing unit also combines the student's learning history and emotion data to make a comprehensive evaluation. For example, if there is a high level of interest in a particular topic, it provides additional learning materials related to that topic. The educational content providing unit also suggests a study plan based on the topics of interest. For example, it suggests a project related to the topic of interest. This makes it possible to identify topics that students are most interested in and provide content based on them.

[0070] The educational content providing unit can suggest different learning modes according to the student's learning needs. For example, the educational content providing unit analyzes the student's learning needs and suggests the optimal learning mode. For example, it provides game-style learning materials to make learning fun. The educational content providing unit also combines data on learning needs and learning outcomes to make a comprehensive assessment. For example, if project-based learning is effective, it suggests that learning mode. The educational content providing unit also provides learning materials for different learning modes. For example, it provides game-style learning materials and project-based learning materials. This makes it possible to suggest different learning modes according to the student's learning needs.

[0071] The educational content providing unit can suggest collaboration with other students based on the student's learning data and promote collaborative learning. For example, the educational content providing unit analyzes the student's learning data and suggests collaboration with other students. For example, it matches students with the same interests. The educational content providing unit also combines learning data and learning outcome data to perform a comprehensive evaluation. For example, it groups students who are highly interested in a particular topic. The educational content providing unit also provides projects and assignments for collaborative learning. For example, it sets up group work and discussion forums. This makes it possible to suggest collaboration with other students based on the student's learning data and promote collaborative learning.

[0072] The educational content providing unit can use the emotion estimation function to identify an environment in which a student can learn most relaxedly and provide content tailored to that environment. The educational content providing unit, for example, uses the emotion estimation function to identify an environment in which a student can learn most relaxedly. For example, it can recommend learning in a quiet environment. The educational content providing unit also combines the student's learning history and emotional data to perform a comprehensive evaluation. For example, if learning results are high in a particular environment, it can recommend that environment. The educational content providing unit also provides learning materials tailored to an environment in which a student can learn most relaxedly. For example, it can provide relaxing music or visual learning materials. This makes it possible to identify an environment in which a student can learn most relaxedly and provide content tailored to that environment.

[0073] The VR participation unit can analyze students' movements within the VR environment and suggest optimal interaction methods. For example, the VR participation unit captures students' movements within the VR environment and suggests optimal interaction methods. For example, it analyzes hand movements and gaze direction. The VR participation unit also combines student movements with learning outcome data to make a comprehensive evaluation. For example, if a specific movement has an impact on learning outcomes, it will recommend that movement. The VR participation unit also provides guidelines for interaction methods. For example, it will suggest how to use gesture controls or voice commands. This allows the VR participation unit to analyze students' movements within the VR environment and suggest optimal interaction methods.

[0074] The VR participation department can use VR technology to virtually recreate actual field trips or experiments and provide a practical learning experience. For example, the VR participation department uses VR technology to virtually recreate actual field trips. For example, historical places or natural environments can be experienced in VR. The VR participation department can also virtually recreate experiments. For example, chemistry experiments or physics experiments can be simulated in VR. The VR participation department can also comprehensively evaluate the field trip or experiment data. For example, it can evaluate the impact of the virtual experience on learning outcomes. This allows the virtual recreation of actual field trips or experiments to provide a practical learning experience.

[0075] The VR participation unit can use the emotion estimation function to analyze students' emotional reactions in the VR environment and provide an optimal learning scenario. The VR participation unit, for example, uses the emotion estimation function to analyze students' emotional reactions in the VR environment. For example, emotions are evaluated based on facial expressions and voice. The VR participation unit also combines emotional reactions with learning outcome data to provide a comprehensive evaluation. For example, the VR participation unit evaluates the impact of a specific scenario on emotions. The VR participation unit also provides an optimal learning scenario. For example, a specific scenario is provided at a time when emotions are stable. This makes it possible to analyze students' emotional reactions in the VR environment and provide an optimal learning scenario.

[0076] The VR Participation Department uses VR technology to create virtual worlds with different cultures and historical backgrounds, which can promote intercultural understanding. For example, the VR Participation Department uses VR technology to create virtual worlds with different cultures and historical backgrounds. For example, it provides virtual tours of ancient Egypt or medieval Europe. The VR Participation Department also performs comprehensive evaluations based on data from the virtual world. For example, it evaluates the impact of intercultural understanding on learning outcomes. The VR Participation Department also provides teaching materials to promote intercultural understanding. For example, it provides video teaching materials and interactive exercises related to different cultures. This allows the creation of virtual worlds with different cultures and historical backgrounds, which can promote intercultural understanding.

[0077] The VR participation department can add a function to share learning outcomes in the VR environment with other students and teachers and receive feedback. For example, the VR participation department builds a system for sharing learning outcomes in the VR environment with other students and teachers. For example, it shares project and experimental results in VR. The VR participation department also provides a function to receive feedback on shared learning outcomes. For example, it receives comments and peer reviews from teachers. The VR participation department also performs comprehensive evaluation based on the feedback data. For example, it evaluates the impact of feedback on learning outcomes. This allows learning outcomes in the VR environment to be shared with other students and teachers and receive feedback.

[0078] The VR participation department can use emotion estimation to monitor students' emotional changes in the VR environment in real time and optimize the learning experience. For example, the VR participation department uses emotion estimation to build a system that monitors students' emotional changes in the VR environment in real time. For example, it analyzes facial expressions and voice. The VR participation department also combines emotional changes with learning outcome data to perform a comprehensive evaluation. For example, if learning outcomes improve during periods of stable emotions, it suggests continuing that learning experience. The VR participation department also provides guidelines for providing an optimal learning experience. For example, it provides a relaxing scenario during periods of emotional instability. This allows students' emotional changes in the VR environment to be monitored in real time and the learning experience to be optimized.

[0079] The interactive class realization unit uses AI to analyze students' comments or questions in real time and can provide feedback at the appropriate time. The interactive class realization unit, for example, builds a system in which AI analyzes students' comments and questions in real time and provides appropriate feedback. For example, it provides immediate answers to questions. The interactive class realization unit also makes a comprehensive evaluation based on data on comments and questions. For example, it analyzes the frequency and content of questions and provides appropriate feedback. The interactive class realization unit also provides guidelines for feedback. For example, it provides detailed explanations and additional teaching materials for questions. This makes it possible to analyze students' comments and questions in real time and provide feedback at the appropriate time.

[0080] The interactive class realization unit uses AI to moderate discussions between students and promote constructive debate. For example, the interactive class realization unit builds a system in which AI moderates discussions between students and promotes constructive discussions. For example, it supports the progress of the discussion. The interactive class realization unit also makes a comprehensive evaluation based on discussion data. For example, it analyzes how the discussion is progressed and the roles of the participants to promote constructive discussions. The interactive class realization unit also provides guidelines for discussions. For example, it provides criteria for evaluating the diversity of opinions and the depth of the discussion. This makes it possible to moderate discussions between students and promote constructive discussions.

[0081] The interactive class realization unit can use the emotion estimation function to analyze the emotional states of students in a class and suggest appropriate interactions. The interactive class realization unit, for example, uses the emotion estimation function to analyze the emotional states of students in a class. For example, emotions are evaluated based on facial expressions and voice. The interactive class realization unit also combines the emotional states with learning outcome data to make a comprehensive evaluation. For example, if learning outcomes are improving during periods of stable emotions, it suggests continuing that interaction. The interactive class realization unit also provides guidelines for providing appropriate interactions. For example, it suggests relaxing interactions during periods of unstable emotions. In this way, the emotional states of students in a class can be analyzed and appropriate interactions can be suggested.

[0082] The interactive class realization unit allows AI to dynamically change students' roles within an interactive class, promoting learning from different perspectives. The interactive class realization unit builds a system in which AI dynamically changes students' roles to promote learning from different perspectives. For example, by switching the discussion leader or recorder. The interactive class realization unit also performs a comprehensive evaluation based on data on role changes. For example, it evaluates whether learning outcomes improve by experiencing different roles. The interactive class realization unit also provides guidelines for role changes. For example, it suggests how to assign roles and when to change roles. This allows AI to dynamically change students' roles within an interactive class, promoting learning from different perspectives.

[0083] The interactive class realization unit can add a function that records interactions within a class and allows them to be reviewed later. The interactive class realization unit, for example, builds a system that records interactions within a class and allows them to be reviewed later. For example, it records and films the content of discussions. The interactive class realization unit also performs comprehensive evaluation based on the recorded interaction data. For example, it analyzes the content of the discussion and the comments of participants and evaluates their impact on learning outcomes. The interactive class realization unit also provides guidelines for review. For example, it recommends playing back the recording or reviewing notes. This makes it possible to add a function that records interactions within a class and allows them to be reviewed later.

[0084] The interactive class realization unit uses an emotion estimation function to monitor changes in the emotions of students in a class in real time and provide optimal interactions. The interactive class realization unit, for example, uses the emotion estimation function to build a system that monitors changes in the emotions of students in a class in real time. For example, it analyzes facial expressions and voice. The interactive class realization unit also combines data on emotional changes and learning outcomes to make a comprehensive evaluation. For example, if learning outcomes are improving during periods of stable emotions, it suggests continuing that interaction. The interactive class realization unit also provides guidelines for providing optimal interactions. For example, it suggests relaxing interactions during periods of unstable emotions. This makes it possible to monitor changes in the emotions of students in a class in real time and provide optimal interactions.

[0085] The learning outcome assessment unit uses AI to evaluate students' learning outcomes from multiple angles and can suggest specific areas for improvement. For example, the learning outcome assessment unit builds a system in which AI evaluates students' learning outcomes from multiple angles and suggests specific areas for improvement. For example, it provides feedback based on test results and assignment evaluations. The learning outcome assessment unit also makes a comprehensive assessment based on learning outcome data. For example, it combines multiple evaluation indicators and data from different evaluators for evaluation. The learning outcome assessment unit also provides guidelines for suggesting specific areas for improvement. For example, it suggests improvements to learning methods and time management. This makes it possible to evaluate students' learning outcomes from multiple angles and suggest specific areas for improvement.

[0086] The learning outcome assessment department can track students' learning outcomes over the long term and visualize their growth process. For example, the learning outcome assessment department builds a system that tracks students' learning outcomes over the long term and visualizes their growth process. For example, it displays grades for each semester and progress on assignments in graphs. The learning outcome assessment department also performs comprehensive assessments based on long-term data. For example, it evaluates progress in skill improvement and goal achievement. The learning outcome assessment department also provides guidelines for visualizing the growth process. For example, it recommends the use of graph displays and dashboards. This makes it possible to track students' learning outcomes over the long term and visualize their growth process.

[0087] The learning outcome assessment unit can use the emotion estimation function to analyze students' emotional reactions to learning and provide feedback that increases motivation. The learning outcome assessment unit, for example, uses the emotion estimation function to analyze students' emotional reactions to learning. For example, emotions are assessed based on facial expressions and voice. The learning outcome assessment unit also combines emotional reaction and learning outcome data to provide a comprehensive assessment. For example, if learning outcomes improve during periods of stable emotions, the learning outcome assessment unit provides feedback to increase motivation. The learning outcome assessment unit also provides feedback to increase motivation. For example, it suggests specific areas for improvement or next learning steps. In this way, it is possible to analyze students' emotional reactions to learning and provide feedback that increases motivation.

[0088] The learning outcome assessment unit can add a function to share students' learning outcomes with other students and teachers and receive peer reviews. The learning outcome assessment unit, for example, builds a system for sharing students' learning outcomes with other students and teachers and adds a function to receive peer reviews. For example, it receives feedback from other students after submitting an assignment. The learning outcome assessment unit also performs a comprehensive assessment based on the peer review data. For example, it analyzes the assessment criteria and the content of the feedback and evaluates the impact on learning outcomes. The learning outcome assessment unit also provides guidelines for peer reviews. For example, it clarifies the assessment criteria and the content of the feedback. This makes it possible to add a function to share students' learning outcomes with other students and teachers and receive peer reviews.

[0089] The learning outcome assessment unit uses AI to suggest the next learning step based on the learning outcomes, thereby supporting continuous learning. The learning outcome assessment unit, for example, builds a system in which AI suggests the next learning step based on learning outcomes. For example, it may suggest the next learning content based on test results. The learning outcome assessment unit also combines learning outcomes with data on the next learning step to provide a comprehensive assessment. For example, it may suggest the next learning step based on curriculum progress and individual levels of understanding. The learning outcome assessment unit also provides guidelines to support continuous learning. For example, it may provide regular assessments and additional learning resources. This makes it possible to suggest the next learning step based on learning outcomes and support continuous learning.

[0090] The learning outcome assessment unit can use the emotion estimation function to monitor changes in students' emotions regarding learning in real time and provide optimal feedback. The learning outcome assessment unit, for example, uses the emotion estimation function to build a system that monitors changes in students' emotions regarding learning in real time. For example, it analyzes facial expressions and voice. The learning outcome assessment unit also combines emotional changes with learning outcome data to make a comprehensive assessment. For example, if learning outcomes improve during periods of stable emotions, it provides that feedback. The learning outcome assessment unit also provides guidelines for providing optimal feedback. For example, it suggests relaxing feedback during periods of emotional instability. This makes it possible to monitor changes in students' emotions regarding learning in real time and provide optimal feedback.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The learning style analysis unit can analyze a student's posture and movements while studying and evaluate their level of concentration and fatigue. For example, if a student stays in the same position for a long time, it will determine that fatigue is building up and suggest that they take a break. The learning style analysis unit also evaluates the student's level of concentration based on the student's movement data. For example, if the student changes posture frequently, it will determine that their concentration is declining. Furthermore, the learning style analysis unit combines posture and movement data to make a comprehensive evaluation. For example, if the student has good posture and moves little, it will determine that their level of concentration is high. This makes it possible to analyze a student's posture and movements, evaluate their level of concentration and fatigue, and provide appropriate support.

[0093] The educational content provider can predict a student's learning progress based on their learning history and suggest review at the appropriate time. For example, if a certain period of time has passed since they studied a particular topic, it can suggest reviewing that topic. The educational content provider also combines learning history and learning outcome data to comprehensively evaluate the student. For example, it can identify topics that need review based on past test results. The educational content provider also provides materials for review. For example, it can provide past test questions and supplementary materials. This allows it to suggest review at the appropriate time based on the student's learning history.

[0094] The VR Participation Department can analyze students' movements within the VR environment and suggest optimal interaction methods. For example, it can analyze hand movements and gaze direction within the VR environment and suggest optimal operation methods. The VR Participation Department also combines data on student movements and learning outcomes to comprehensively evaluate them. For example, it can evaluate the impact of specific movements on learning outcomes. The VR Participation Department also provides guidelines for interaction methods. For example, it can suggest how to use gesture controls or voice commands. This allows the VR Participation Department to analyze students' movements within the VR environment and suggest optimal interaction methods.

[0095] The learning style analysis unit uses an emotion estimation function to analyze a student's motivation and stress level for learning, and can provide appropriate support. For example, it analyzes a student's facial expressions and voice and uses the emotion estimation function to evaluate their motivation and stress level. For example, if they smile a lot, it determines that they are highly motivated. The learning style analysis unit also uses sensors to collect biometric data such as heart rate and electrodermal activity to evaluate their stress level. For example, if their heart rate is high, it determines that they are highly stressed. The learning style analysis unit also combines the motivation and stress level data to make a comprehensive evaluation. For example, if their motivation is high and their stress level is low, it determines that their studies are progressing smoothly. This makes it possible to analyze a student's motivation and stress level and provide appropriate support.

[0096] The educational content providing unit can use the emotion estimation function to identify topics that students are most interested in and provide content based on that. For example, the emotion estimation function can be used to identify topics that students are most interested in. For example, educational materials related to the topics that students have shown interest in can be provided. The educational content providing unit also combines the student's learning history and emotion data to make a comprehensive evaluation. For example, if there is a high level of interest in a particular topic, additional educational materials related to that topic can be provided. The educational content providing unit also suggests a study plan based on the topics of interest. For example, it can suggest a project related to the topic of interest. This makes it possible to identify topics that students are most interested in and provide content based on that.

[0097] The interactive class realization unit uses AI to analyze students' comments or questions in real time and provide feedback at the appropriate time. For example, a system can be built in which AI analyzes students' comments and questions in real time and provides appropriate feedback. For example, it can provide immediate answers to questions. The interactive class realization unit also makes a comprehensive evaluation based on the data on comments and questions. For example, it can analyze the frequency and content of questions and provide appropriate feedback. The interactive class realization unit also provides guidelines for feedback. For example, it can provide detailed explanations for questions or additional teaching materials. This makes it possible to analyze students' comments and questions in real time and provide feedback at the appropriate time.

[0098] The interactive class realization unit can use the emotion estimation function to analyze the emotional state of students in a class and suggest appropriate interactions. For example, the emotion estimation function is used to analyze the emotional state of students in a class. For example, emotions are evaluated based on facial expressions and voice. The interactive class realization unit also combines emotional state and learning outcome data to make a comprehensive evaluation. For example, if learning outcomes are improving during periods of stable emotions, it will suggest continuing that interaction. The interactive class realization unit also provides guidelines for providing appropriate interactions. For example, it will suggest relaxing interactions during periods of emotional instability. This makes it possible to analyze the emotional state of students in a class and suggest appropriate interactions.

[0099] The learning outcomes assessment unit can add a function that allows students to share their learning outcomes with other students and teachers and receive peer reviews. For example, a system can be built to share students' learning outcomes with other students and teachers, and a function can be added to receive peer reviews. For example, a system can be built to receive feedback from other students after an assignment is submitted. The learning outcomes assessment unit can also perform a comprehensive assessment based on the peer review data. For example, it can analyze the assessment criteria and the content of the feedback, and evaluate the impact on learning outcomes. The learning outcomes assessment unit can also provide guidelines for peer reviews. For example, it can clarify the assessment criteria and the content of the feedback. This makes it possible to add a function that allows students to share their learning outcomes with other students and teachers and receive peer reviews.

[0100] The learning outcome assessment unit can use the emotion estimation function to analyze students' emotional reactions to learning and provide feedback that increases motivation. For example, the emotion estimation function is used to analyze students' emotional reactions to learning. For example, emotions are assessed based on facial expressions and voice. The learning outcome assessment unit also combines emotional reaction and learning outcome data to provide a comprehensive assessment. For example, if learning outcomes improve during periods of stable emotions, the learning outcome assessment unit provides feedback to increase motivation. For example, the learning outcome assessment unit suggests specific areas for improvement or next learning steps. This makes it possible to analyze students' emotional reactions to learning and provide feedback that increases motivation.

[0101] The Learning Outcome Assessment Department can track students' learning outcomes over the long term and visualize their growth process. For example, a system can be built to track students' learning outcomes over the long term and visualize their growth process. For example, semester grades and assignment progress can be displayed in graphs. The Learning Outcome Assessment Department can also provide comprehensive evaluations based on long-term data. For example, it can evaluate progress in skill improvement and goal achievement. The Learning Outcome Assessment Department can also provide guidelines for visualizing the growth process. For example, it can recommend the use of graph displays and dashboards. This makes it possible to track students' learning outcomes over the long term and visualize their growth process.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The Learning Style Analysis Department analyzes students' learning styles and progress. Specifically, it analyzes students' past learning data, test results, and learning behavior data to determine the most effective learning methods and areas where they are struggling. Step 2: The educational content provider provides customized educational content based on the results of the learning style analysis. For example, if a student is struggling with a particular area of ​​mathematics, the provider will provide supplementary materials and practice questions specific to that area. The content provider will also provide the content in the most appropriate format, such as visual materials or interactive quizzes, depending on the student's learning style. Step 3: VR participation utilizes VR technology to provide interactive and immersive classes. For example, by wearing VR goggles, students can enter a virtual classroom and communicate with teachers and other students in real time. Also, experiments and simulations can be conducted in the VR environment, providing a learning experience that cannot be experienced in a physical classroom.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A learning style analysis department that analyzes students' learning styles and progress; an educational content providing unit that provides customized educational content based on the analysis result of the learning style analysis unit; and a VR participation department that utilizes VR technology to create an interactive and immersive experience for students in classes. A system characterized by:

2. The learning style analysis unit Analyze the student's facial expressions and voice while studying to evaluate their level of concentration and understanding in real time.

2. The system of claim 1.

3. The educational content providing unit Based on the student's learning history, predict what they should learn next and suggest advanced learning.

2. The system of claim 1.

4. The VR participation department: Analyze the student's movements within the VR environment and suggest optimal interaction methods 2. The system of claim 1.

5. The interactive class implementation includes: Using emotion estimation capabilities, the emotional state of the students in the class is analyzed and appropriate interactions are suggested.

2. The system of claim 1.

6. The Learning Outcomes Assessment Department Using emotion estimation, the emotional response of the student to the learning is analyzed and feedback that enhances motivation is provided.

2. The system of claim 1.

7. The learning style analysis unit Using emotion estimation functionality, the system analyzes the student's motivation and stress level for learning and provides appropriate support.

2. The system of claim 1.

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