System

Abstract

The system according to the embodiment aims to optimize the teaching process using generative AI and provide optimal teaching to individual students.SOLUTION: A system includes a teaching material generation part, a learning plan planning part, an individual instruction part, an automatic scoring part, and an education platform construction part. The teaching material generation unit generates an optimal teaching material according to each grade or subject using the generation AI. The study plan drafting unit drafts a study plan optimal for each student. The individual instruction part performs individual instruction and learning support to individual students. The automatic scorer automatically scores the student's answers and provides personalized feedback. The education platform construction unit integrates data of school education or non-school education to construct an optimized education platform.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] With conventional technology, the educational process was siloed, making it difficult to provide optimal teaching materials and learning plans.

[0005] The system of the embodiment aims to optimize the educational process using generative AI and provide the best possible education for each student. [Means for solving the problem]

[0006] The system according to the embodiment comprises a teaching material generation unit, a learning plan creation unit, an individual instruction unit, an automatic grading unit, and an educational platform construction unit. The teaching material generation unit uses generative AI to create optimal teaching materials for each grade and subject. The learning plan creation unit creates optimal learning plans for each student. The individual instruction unit provides individual instruction and learning support to each student. The automatic grading unit automatically grades students' answers and provides individual feedback. The educational platform construction unit integrates data from formal and informal education to create an optimized educational platform. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the educational process using generative AI and provide optimal education to each individual student. [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 educational system according to the embodiment of the present invention is a system that uses generative AI to drastically update educational programs, educational processes, and educational sites, thereby improving the quality of education and enabling the provision of education efficiently and at low cost.

[0029] An educational system according to an embodiment includes a teaching material generation unit, a learning plan creation unit, an individualized instruction unit, an automatic grading unit, and an educational platform construction unit. The teaching material generation unit uses a generation AI to generate optimal teaching materials for each grade and subject. For example, the generation AI analyzes educational curricula and past teaching material data to generate teaching materials based on the latest educational theories and students' levels of understanding. The generation AI receives inputs from prompts containing educational curriculum and teaching material requirements, and the generation AI generates teaching materials based on the prompts. The learning plan creation unit uses the generation AI to create optimal learning plans for individual students. For example, the generation AI analyzes students' learning histories and grade data to propose optimal learning schedules and learning content for each student. The generation AI receives inputs from prompts containing students' learning histories and grade data, and the generation AI creates learning plans based on the prompts. The individualized instruction unit uses the generation AI to provide individualized instruction and learning support to each student. For example, the generation AI analyzes students' levels of understanding and learning progress in real time and provides appropriate feedback and additional learning materials. The input to the generation AI is prompts containing data on the student's level of understanding and learning progress, and the generation AI provides individualized instruction based on the prompts. The automatic scoring unit uses the generation AI to automatically score students' answers and provide individual feedback. For example, the generation AI analyzes students' answers, evaluates their correct answer rate and tendency toward incorrect answers, and provides specific feedback on areas for improvement. The input to the generation AI is prompts containing student answer data, and the generation AI scores and provides feedback based on the prompts. The educational platform construction unit uses the generation AI to integrate data from formal education and non-formal education and build an optimized educational platform. For example, the generation AI learns tacit knowledge based on formal education curriculum data, cram school know-how, and the experience of the elderly, and proposes an optimal educational process. The input to the generation AI is prompts containing data from formal education and non-formal education, and the generation AI builds an educational platform based on the prompts. This enables the educational system according to the embodiment to improve the quality of education and provide education efficiently and at low cost. For example, the development of teaching materials using the generation AI allows for the rapid provision of teaching materials based on the latest educational theories.In addition, individualized instruction and automatic grading will provide optimal learning support for each student. Furthermore, the construction of an educational platform will integrate data from formal and informal education, realizing an optimal educational process. This is expected to maximize student learning effectiveness and improve the overall quality of education in Japan.

[0030] The teaching material generation unit can generate personalized teaching materials based on the student's interests. For example, the teaching material generation unit uses generative AI to generate personalized teaching materials based on the student's interests. For example, if a student is interested in a particular historical event, detailed teaching materials related to that event can be automatically generated. This can increase the student's motivation to learn by providing them with teaching materials based on their interests.

[0031] The teaching material generator can generate multiple modal teaching materials according to students' learning styles. For example, the teaching material generator uses generative AI to generate teaching materials that make extensive use of graphics and visual aids for visual learners. For example, important events in a history class could be explained using illustrations and infographics. This maximizes learning effectiveness by providing teaching materials that suit students' learning styles.

[0032] The teaching material generation unit can generate multilingual teaching materials adapted to different cultures or regions. For example, the teaching material generation unit uses generative AI to generate teaching materials customized for students with different cultural backgrounds. For example, when studying Asian history, teaching materials can be provided that include explanations from the perspectives of each country. This allows the unit to accommodate a diverse range of students by providing teaching materials adapted to different cultures and regions.

[0033] The teaching material generation unit can generate teaching materials that incorporate the latest science and technology or social issues. The teaching material generation unit generates teaching materials related to the latest science and technology, for example, using a generative AI. For example, teaching materials that incorporate the latest research results on artificial intelligence and quantum computers are provided. This allows the provision of teaching materials that incorporate the latest science and technology and social issues, making it possible to provide modern educational content.

[0034] The study plan creation unit can create an optimal study plan based on the student's lifestyle rhythm or activity pattern. For example, the study plan creation unit uses generative AI to analyze the student's lifestyle rhythm and activity pattern and create an optimal study plan. For example, it can propose a study schedule that allows night owls to concentrate at night. This makes it possible to maximize learning effectiveness by providing a study plan based on the student's lifestyle rhythm and activity pattern.

[0035] The learning plan creation unit can integrate a student's short-term and long-term goals and propose achievable steps. For example, the learning plan creation unit can use generative AI to integrate a student's short-term and long-term goals and propose achievable steps. For example, it can create a learning plan that takes into account exams one month from now and academic goals one year from now. This can help students achieve their goals by providing them with a learning plan that integrates their short-term and long-term goals.

[0036] The learning plan creation unit can create an optimal learning plan for group learning or a collaborative project. The learning plan creation unit uses, for example, generative AI to create an optimal learning plan for group learning or a collaborative project. For example, it adjusts each member's schedule and ensures time for collaborative work. This makes it possible to maximize the effectiveness of collaborative learning by providing an optimal learning plan for group learning or a collaborative project.

[0037] The learning plan creation unit can provide a customized plan that takes into account the student's home environment or social background. For example, the learning plan creation unit uses generative AI to provide a customized plan that takes into account the student's home environment and social background. For example, for a student who cannot study at night due to family circumstances, a daytime learning plan can be proposed. This makes it possible to provide a learning plan that takes into account the student's home environment and social background, thereby strengthening individual learning support.

[0038] The individual instruction department can propose the optimal teaching method based on the student's learning history and real-time level of understanding. For example, the individual instruction department uses generative AI to analyze the student's learning history and real-time level of understanding and propose the optimal teaching method. For example, it can create an individual teaching plan based on past grades and test results. This makes it possible to maximize learning effectiveness by providing the optimal teaching method based on the student's learning history and level of understanding.

[0039] The tutoring department can provide customized instruction based on the student's learning style. For example, the tutoring department can use generative AI to provide customized instruction according to the student's learning style (visual, auditory, tactile). For example, visual learners can receive instruction that makes extensive use of visual aids. This maximizes learning effectiveness by providing instruction that is tailored to the student's learning style.

[0040] The individual tutoring department can find the correlation between learning content across different subjects and provide integrated learning support. For example, the individual tutoring department can use generative AI to analyze the correlation between learning content across different subjects and provide integrated learning support. For example, it can provide teaching materials that show the correlation between mathematics and physics. This makes it possible to find the correlation between learning content across different subjects and provide integrated learning support, thereby maximizing learning effectiveness.

[0041] The tutoring department can provide additional learning materials based on the student's interests. For example, the tutoring department can use generative AI to provide additional learning materials based on the student's interests. For example, if a student is interested in a particular scientific field, the tutoring department can provide the latest research materials on that field. This can increase the student's motivation to learn by providing additional learning materials based on the student's interests.

[0042] The automatic grading unit can analyze students' answer patterns and provide individualized study advice. The automatic grading unit can, for example, use generative AI to analyze students' answer patterns and provide individualized study advice. For example, it can analyze incorrect answer patterns for specific questions and point out areas of insufficient understanding. This makes it possible to maximize learning effectiveness by analyzing students' answer patterns and providing individualized study advice.

[0043] The automatic grading unit can evaluate the quality of students' answers and provide specific feedback on areas for improvement. The automatic grading unit can, for example, use generative AI to evaluate the quality of students' answers and provide specific feedback on areas for improvement. For example, it can evaluate the structure and logical consistency of a paper and point out areas for improvement. This makes it possible to maximize learning effectiveness by evaluating the quality of students' answers and providing specific feedback on areas for improvement.

[0044] The automatic grading unit can integrate different evaluation criteria and provide a comprehensive evaluation. For example, the automatic grading unit can use generative AI to integrate different evaluation criteria and provide a comprehensive evaluation. For example, the evaluation can include not only test scores but also assignment submission status and class participation level. This allows the integration of different evaluation criteria and the provision of a comprehensive evaluation, making it possible to evaluate a student's learning status from multiple angles.

[0045] The automatic grading unit can provide evaluations of student answers from multiple perspectives. The automatic grading unit, for example, uses generative AI to provide evaluations of student answers from multiple perspectives. For example, it evaluates the structure of the paper, logical consistency, creativity, etc. This allows for evaluations of student answers from multiple perspectives, maximizing learning effectiveness.

[0046] The educational platform construction unit can integrate data from formal education and non-formal education and propose the optimal educational process. For example, the educational platform construction unit can use generative AI to integrate data from formal education and non-formal education and propose the optimal educational process. For example, it can provide an educational plan that integrates school curriculum and cram school know-how. This makes it possible to maximize learning effectiveness by integrating data from formal education and non-formal education and providing the optimal educational process.

[0047] The educational platform construction unit can centrally manage students' learning history or grade data and provide individualized learning support. The educational platform construction unit, for example, uses generative AI to build a system on the educational platform that centrally manages students' learning history and grade data. For example, it can provide individual learning plans based on past grades and learning history. This makes it possible to centrally manage students' learning history and grade data and provide individualized learning support, thereby maximizing learning effectiveness.

[0048] The educational platform construction unit can promote data sharing between different educational institutions and build an integrated educational platform. The educational platform construction unit, for example, uses generative AI to build a system that promotes data sharing between different educational institutions. For example, it can realize the sharing of grade data and learning history between schools and cram schools. This promotes data sharing between different educational institutions and provides an integrated educational platform, thereby maximizing learning effectiveness.

[0049] The educational platform construction unit can provide customized learning content based on the student's interests. For example, the educational platform construction unit uses generative AI to provide the educational platform with customized learning content based on the student's interests. For example, if a student is interested in a particular scientific field, the latest research materials on that field can be provided. This can increase the student's motivation to learn by providing customized learning content based on the student's interests.

[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 education system can further include a learning progress monitoring unit. The learning progress monitoring unit can monitor a student's learning progress in real time and adjust the learning plan as necessary. For example, if a student is progressing ahead of schedule, the next learning content can be provided ahead of schedule. Also, if a student is falling behind, supplementary lessons or additional learning materials can be provided. This allows for flexible responses according to the student's learning progress, maximizing learning effectiveness.

[0052] The teaching material generation unit can also generate review materials based on the student's learning history. For example, it can analyze past test results and learning history to provide review materials specific to the student's weak areas. It can also have a reminder function to encourage regular review. This allows students to review efficiently and solidify the content they have learned.

[0053] The teaching material generator can also generate more interactive teaching materials. For example, it can provide teaching materials that include quiz-style questions and simulations. It can also provide a function that allows students to create their own questions and share them with other students. This allows students to take an active role in their learning, improving the effectiveness of their learning.

[0054] The teaching material generator can also generate teaching materials that stimulate students' creativity. For example, it can provide teaching materials that include open-ended questions and project-based assignments. It can also provide a forum for students to present their own ideas. This can bring out students' creativity and promote independent learning.

[0055] The teaching material generator can also generate teaching materials that foster students' social skills. For example, it can provide teaching materials that include group discussions and role-playing. It can also provide teaching materials that encourage interaction with students from different cultures and backgrounds. This helps develop students' social skills and allows them to gain diverse perspectives.

[0056] The learning planning department can also create learning plans that take into account the student's health. For example, it can suggest learning plans that incorporate regular breaks and exercise. It can also provide a schedule to ensure adequate sleep. This allows for learning plans that take into account the student's health, maximizing learning effectiveness.

[0057] The lesson plan creation unit can also create lesson plans that are tailored to each student's learning style. For example, it can suggest a lesson plan that makes heavy use of visual aids to visual learners, or a lesson plan that incorporates audio materials to auditory learners. This allows for the provision of optimal lesson plans tailored to each student's learning style, maximizing learning effectiveness.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The teaching material generation unit uses generative AI to generate optimal teaching materials for each grade and subject. The generative AI analyzes the educational curriculum and past teaching material data, and generates teaching materials based on the latest educational theories and students' levels of understanding. The input to the generative AI is prompts that include the educational curriculum and teaching material requirements, and the generative AI generates teaching materials based on those prompts. Step 2: The learning plan creation unit uses the generation AI to create the optimal learning plan for each student. The generation AI analyzes the student's learning history and grade data and proposes the optimal learning schedule and learning content for each student. The input to the generation AI is a prompt containing the student's learning history and grade data, and the generation AI creates a learning plan based on that prompt. Step 3: The individualized instruction department uses generative AI to provide individualized instruction and learning support to each student. The generative AI analyzes the student's level of understanding and learning progress in real time and provides appropriate feedback and additional learning materials. The generative AI receives inputs from prompts that include data on the student's level of understanding and learning progress, and the generative AI provides individualized instruction based on those prompts. Step 4: The automatic scoring unit uses the generation AI to automatically score the student's answer and provide individual feedback. The generation AI analyzes the student's answer, evaluates the percentage of correct answers and trends in incorrect answers, and provides specific feedback on areas for improvement. The input to the generation AI is a prompt containing the student's answer data, and the generation AI scores and provides feedback based on that prompt. Step 5: The educational platform construction unit uses generative AI to integrate data from formal education and non-formal education to build an optimized educational platform. The generative AI learns formal education curriculum data, cram school know-how, and tacit knowledge based on the experience of the elderly, and proposes an optimal educational process. The generative AI receives inputs from prompts containing formal education and non-formal education data, and the generative AI builds an educational platform based on these prompts.

[0060] (Example 2) The educational system according to the embodiment of the present invention is a system that uses generative AI to drastically update educational programs, educational processes, and educational sites, thereby improving the quality of education and enabling the provision of education efficiently and at low cost.

[0061] An educational system according to an embodiment includes a teaching material generation unit, a learning plan creation unit, an individualized instruction unit, an automatic grading unit, and an educational platform construction unit. The teaching material generation unit uses a generation AI to generate optimal teaching materials for each grade and subject. For example, the generation AI analyzes educational curricula and past teaching material data to generate teaching materials based on the latest educational theories and students' levels of understanding. The generation AI receives inputs from prompts containing educational curriculum and teaching material requirements, and the generation AI generates teaching materials based on the prompts. The learning plan creation unit uses the generation AI to create optimal learning plans for individual students. For example, the generation AI analyzes students' learning histories and grade data to propose optimal learning schedules and learning content for each student. The generation AI receives inputs from prompts containing students' learning histories and grade data, and the generation AI creates learning plans based on the prompts. The individualized instruction unit uses the generation AI to provide individualized instruction and learning support to each student. For example, the generation AI analyzes students' levels of understanding and learning progress in real time and provides appropriate feedback and additional learning materials. The input to the generation AI is prompts containing data on the student's level of understanding and learning progress, and the generation AI provides individualized instruction based on the prompts. The automatic scoring unit uses the generation AI to automatically score students' answers and provide individual feedback. For example, the generation AI analyzes students' answers, evaluates their correct answer rate and tendency toward incorrect answers, and provides specific feedback on areas for improvement. The input to the generation AI is prompts containing student answer data, and the generation AI scores and provides feedback based on the prompts. The educational platform construction unit uses the generation AI to integrate data from formal education and non-formal education and build an optimized educational platform. For example, the generation AI learns tacit knowledge based on formal education curriculum data, cram school know-how, and the experience of the elderly, and proposes an optimal educational process. The input to the generation AI is prompts containing data from formal education and non-formal education, and the generation AI builds an educational platform based on the prompts. This enables the educational system according to the embodiment to improve the quality of education and provide education efficiently and at low cost. For example, the development of teaching materials using the generation AI allows for the rapid provision of teaching materials based on the latest educational theories.In addition, individualized instruction and automatic grading will provide optimal learning support for each student. Furthermore, the construction of an educational platform will integrate data from formal and informal education, realizing an optimal educational process. This is expected to maximize student learning effectiveness and improve the overall quality of education in Japan.

[0062] The teaching material generation unit can generate personalized teaching materials based on the student's interests. For example, the teaching material generation unit uses generative AI to generate personalized teaching materials based on the student's interests. For example, if a student is interested in a particular historical event, detailed teaching materials related to that event can be automatically generated. This can increase the student's motivation to learn by providing them with teaching materials based on their interests.

[0063] The teaching material generator can generate multiple modal teaching materials according to students' learning styles. For example, the teaching material generator uses generative AI to generate teaching materials that make extensive use of graphics and visual aids for visual learners. For example, important events in a history class could be explained using illustrations and infographics. This maximizes learning effectiveness by providing teaching materials that suit students' learning styles.

[0064] The teaching material generation unit can use the emotion estimation function to generate teaching materials based on the emotional state of the student. For example, if the student is feeling stressed, the teaching material generation unit can use the emotion estimation function to generate teaching materials with content that will help the student relax. For example, the teaching materials can be provided that include music or images that have a relaxing effect. This can increase the student's motivation to learn by providing teaching materials that are appropriate for the student's emotional state.

[0065] The teaching material generation unit can generate multilingual teaching materials adapted to different cultures or regions. For example, the teaching material generation unit uses generative AI to generate teaching materials customized for students with different cultural backgrounds. For example, when studying Asian history, teaching materials can be provided that include explanations from the perspectives of each country. This allows the unit to accommodate a diverse range of students by providing teaching materials adapted to different cultures and regions.

[0066] The teaching material generation unit can generate teaching materials that incorporate the latest science and technology or social issues. The teaching material generation unit generates teaching materials related to the latest science and technology, for example, using a generative AI. For example, teaching materials that incorporate the latest research results on artificial intelligence and quantum computers are provided. This allows the provision of teaching materials that incorporate the latest science and technology and social issues, making it possible to provide modern educational content.

[0067] The teaching material generation unit can use the emotion estimation function to identify the topic in which a student is most interested and generate teaching materials based on that topic. For example, the teaching material generation unit can use the emotion estimation function to identify the topic in which a student is most interested and generate teaching materials based on that topic. For example, if a student is interested in space, detailed teaching materials about space exploration can be provided. This can increase students' motivation to learn by providing them with teaching materials based on the topic in which they are most interested.

[0068] The study plan creation unit can create an optimal study plan based on the student's lifestyle rhythm or activity pattern. For example, the study plan creation unit uses generative AI to analyze the student's lifestyle rhythm and activity pattern and create an optimal study plan. For example, it can propose a study schedule that allows night owls to concentrate at night. This makes it possible to maximize learning effectiveness by providing a study plan based on the student's lifestyle rhythm and activity pattern.

[0069] The learning plan creation unit can integrate a student's short-term and long-term goals and propose achievable steps. For example, the learning plan creation unit can use generative AI to integrate a student's short-term and long-term goals and propose achievable steps. For example, it can create a learning plan that takes into account exams one month from now and academic goals one year from now. This can help students achieve their goals by providing them with a learning plan that integrates their short-term and long-term goals.

[0070] The learning plan creation unit can use the emotion estimation function to create a learning plan to maintain the student's motivation. The learning plan creation unit, for example, uses the emotion estimation function to create a learning plan to maintain the student's motivation. For example, if the student is tired, the learning plan creation unit can suggest a plan that incorporates rest. This makes it possible to maximize the learning effect by providing a learning plan that maintains the student's motivation.

[0071] The learning plan creation unit can create an optimal learning plan for group learning or a collaborative project. The learning plan creation unit uses, for example, generative AI to create an optimal learning plan for group learning or a collaborative project. For example, it adjusts each member's schedule and ensures time for collaborative work. This makes it possible to maximize the effectiveness of collaborative learning by providing an optimal learning plan for group learning or a collaborative project.

[0072] The learning plan creation unit can provide a customized plan that takes into account the student's home environment or social background. For example, the learning plan creation unit uses generative AI to provide a customized plan that takes into account the student's home environment and social background. For example, for a student who cannot study at night due to family circumstances, a daytime learning plan can be proposed. This makes it possible to provide a learning plan that takes into account the student's home environment and social background, thereby strengthening individual learning support.

[0073] The study plan creation unit can use the emotion estimation function to identify the time period when a student can concentrate best and create a study plan based on that time period. For example, the study plan creation unit can use the emotion estimation function to identify the time period when a student can concentrate best and create a study plan based on that time period. For example, for a student who is a morning person, the unit can suggest a plan to increase study time in the morning. This makes it possible to maximize learning effectiveness by providing a study plan based on the time period when a student can concentrate best.

[0074] The individual instruction department can propose the optimal teaching method based on the student's learning history and real-time level of understanding. For example, the individual instruction department uses generative AI to analyze the student's learning history and real-time level of understanding and propose the optimal teaching method. For example, it can create an individual teaching plan based on past grades and test results. This makes it possible to maximize learning effectiveness by providing the optimal teaching method based on the student's learning history and level of understanding.

[0075] The tutoring department can provide customized instruction based on the student's learning style. For example, the tutoring department can use generative AI to provide customized instruction according to the student's learning style (visual, auditory, tactile). For example, visual learners can receive instruction that makes extensive use of visual aids. This maximizes learning effectiveness by providing instruction that is tailored to the student's learning style.

[0076] The tutoring unit can use the emotion estimation function to provide encouragement and advice based on the student's emotional state. For example, if a student is feeling stressed, the tutoring unit can use the emotion estimation function to provide advice to help the student relax. For example, the tutoring unit can suggest taking a deep breath or taking a short break. This can increase the student's motivation to learn by providing encouragement and advice according to the student's emotional state.

[0077] The individual tutoring department can find the correlation between learning content across different subjects and provide integrated learning support. For example, the individual tutoring department can use generative AI to analyze the correlation between learning content across different subjects and provide integrated learning support. For example, it can provide teaching materials that show the correlation between mathematics and physics. This makes it possible to find the correlation between learning content across different subjects and provide integrated learning support, thereby maximizing learning effectiveness.

[0078] The tutoring department can provide additional learning materials based on the student's interests. For example, the tutoring department can use generative AI to provide additional learning materials based on the student's interests. For example, if a student is interested in a particular scientific field, the tutoring department can provide the latest research materials on that field. This can increase the student's motivation to learn by providing additional learning materials based on the student's interests.

[0079] The individual tutoring department can use the emotion estimation function to identify an environment in which a student is most relaxed and propose learning support in that environment. For example, the individual tutoring department can use the emotion estimation function to identify an environment in which a student is most relaxed and propose learning support in that environment. For example, it can propose studying in a quiet place or in nature. This makes it possible to maximize learning effectiveness by providing learning support in an environment in which the student is most relaxed.

[0080] The automatic grading unit can analyze students' answer patterns and provide individualized study advice. The automatic grading unit can, for example, use generative AI to analyze students' answer patterns and provide individualized study advice. For example, it can analyze incorrect answer patterns for specific questions and point out areas of insufficient understanding. This makes it possible to maximize learning effectiveness by analyzing students' answer patterns and providing individualized study advice.

[0081] The automatic grading unit can evaluate the quality of students' answers and provide specific feedback on areas for improvement. The automatic grading unit can, for example, use generative AI to evaluate the quality of students' answers and provide specific feedback on areas for improvement. For example, it can evaluate the structure and logical consistency of a paper and point out areas for improvement. This makes it possible to maximize learning effectiveness by evaluating the quality of students' answers and providing specific feedback on areas for improvement.

[0082] The automatic grading unit can use the emotion estimation function to provide feedback based on the student's emotional state. For example, if the student is feeling stressed, the automatic grading unit can use the emotion estimation function to provide relaxing feedback. For example, the automatic grading unit can provide encouraging messages or positive feedback. This can increase the student's motivation to learn by providing feedback that is appropriate for the student's emotional state.

[0083] The automatic grading unit can integrate different evaluation criteria and provide a comprehensive evaluation. For example, the automatic grading unit can use generative AI to integrate different evaluation criteria and provide a comprehensive evaluation. For example, the evaluation can include not only test scores but also assignment submission status and class participation level. This allows the integration of different evaluation criteria and the provision of a comprehensive evaluation, making it possible to evaluate a student's learning status from multiple angles.

[0084] The automatic grading unit can provide evaluations of student answers from multiple perspectives. The automatic grading unit, for example, uses generative AI to provide evaluations of student answers from multiple perspectives. For example, it evaluates the structure of the paper, logical consistency, creativity, etc. This allows for evaluations of student answers from multiple perspectives, maximizing learning effectiveness.

[0085] The automatic grading unit can use the emotion estimation function to identify the feedback format that is most acceptable to the student and provide the feedback in that format. For example, the automatic grading unit can use the emotion estimation function to identify the feedback format that is most acceptable to the student and provide the feedback in that format. For example, visual learners can be provided with feedback that makes extensive use of visual aids. This makes it possible to maximize the learning effect by providing the feedback format that is most acceptable to the student.

[0086] The educational platform construction unit can integrate data from formal education and non-formal education and propose the optimal educational process. For example, the educational platform construction unit can use generative AI to integrate data from formal education and non-formal education and propose the optimal educational process. For example, it can provide an educational plan that integrates school curriculum and cram school know-how. This makes it possible to maximize learning effectiveness by integrating data from formal education and non-formal education and providing the optimal educational process.

[0087] The educational platform construction unit can centrally manage students' learning history or grade data and provide individualized learning support. The educational platform construction unit, for example, uses generative AI to build a system on the educational platform that centrally manages students' learning history and grade data. For example, it can provide individual learning plans based on past grades and learning history. This makes it possible to centrally manage students' learning history and grade data and provide individualized learning support, thereby maximizing learning effectiveness.

[0088] The education platform construction unit can use the emotion estimation function to propose an education process based on the student's emotional state. For example, the education platform construction unit uses the emotion estimation function to propose an education process according to the student's emotional state. For example, it can provide a relaxing learning environment for a student who is feeling stressed. This makes it possible to maximize learning effectiveness by providing an education process according to the student's emotional state.

[0089] The educational platform construction unit can promote data sharing between different educational institutions and build an integrated educational platform. The educational platform construction unit, for example, uses generative AI to build a system that promotes data sharing between different educational institutions. For example, it can realize the sharing of grade data and learning history between schools and cram schools. This promotes data sharing between different educational institutions and provides an integrated educational platform, thereby maximizing learning effectiveness.

[0090] The educational platform construction unit can provide customized learning content based on the student's interests. For example, the educational platform construction unit uses generative AI to provide the educational platform with customized learning content based on the student's interests. For example, if a student is interested in a particular scientific field, the latest research materials on that field can be provided. This can increase the student's motivation to learn by providing customized learning content based on the student's interests.

[0091] The educational platform construction unit can use the emotion estimation function to identify an environment in which a student is most motivated to learn and provide learning support in that environment. For example, the educational platform construction unit can use the emotion estimation function to identify an environment in which a student is most motivated to learn and provide learning support in that environment. For example, it can suggest studying in a quiet place or in nature. This can maximize learning effectiveness by providing learning support in an environment in which a student is most motivated to learn.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The education system can further include a learning progress monitoring unit. The learning progress monitoring unit can monitor a student's learning progress in real time and adjust the learning plan as necessary. For example, if a student is progressing ahead of schedule, the next learning content can be provided ahead of schedule. Also, if a student is falling behind, supplementary lessons or additional learning materials can be provided. This allows for flexible responses according to the student's learning progress, maximizing learning effectiveness.

[0094] The teaching material generation unit can also generate review materials based on the student's learning history. For example, it can analyze past test results and learning history to provide review materials specific to the student's weak areas. It can also have a reminder function to encourage regular review. This allows students to review efficiently and solidify the content they have learned.

[0095] The teaching material generator can also generate more interactive teaching materials. For example, it can provide teaching materials that include quiz-style questions and simulations. It can also provide a function that allows students to create their own questions and share them with other students. This allows students to take an active role in their learning, improving the effectiveness of their learning.

[0096] The learning material generation unit can use the emotion estimation function to adjust the learning pace based on the student's emotional state. For example, if the student is tired, it will suggest slowing down the learning pace. If the student is concentrating, it will suggest speeding up the learning pace. This makes it possible to maximize learning effectiveness by providing the optimal learning pace according to the student's emotional state.

[0097] The teaching material generator can also generate teaching materials that stimulate students' creativity. For example, it can provide teaching materials that include open-ended questions and project-based assignments. It can also provide a forum for students to present their own ideas. This can bring out students' creativity and promote independent learning.

[0098] The teaching material generator can also generate teaching materials that foster students' social skills. For example, it can provide teaching materials that include group discussions and role-playing. It can also provide teaching materials that encourage interaction with students from different cultures and backgrounds. This helps develop students' social skills and allows them to gain diverse perspectives.

[0099] The learning material generation unit can use the emotion estimation function to provide feedback based on the student's emotional state. For example, if a student is feeling anxious, it can provide an encouraging message. If a student is feeling confident, it can provide feedback encouraging them to try harder. This allows the student to be provided with appropriate feedback according to their emotional state, thereby increasing their motivation to learn.

[0100] The learning planning department can also create learning plans that take into account the student's health. For example, it can suggest learning plans that incorporate regular breaks and exercise. It can also provide a schedule to ensure adequate sleep. This allows for learning plans that take into account the student's health, maximizing learning effectiveness.

[0101] The learning plan creation unit can use the emotion estimation function to set learning goals based on the student's emotional state. For example, if the student is feeling motivated, it sets challenging goals. On the other hand, if the student is feeling stressed, it sets realistic goals. This allows the system to provide appropriate learning goals according to the student's emotional state, thereby increasing their motivation to learn.

[0102] The lesson plan creation unit can also create lesson plans that are tailored to each student's learning style. For example, it can suggest a lesson plan that makes heavy use of visual aids to visual learners, or a lesson plan that incorporates audio materials to auditory learners. This allows for the provision of optimal lesson plans tailored to each student's learning style, maximizing learning effectiveness.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The teaching material generation unit uses generative AI to generate optimal teaching materials for each grade and subject. The generative AI analyzes the educational curriculum and past teaching material data, and generates teaching materials based on the latest educational theories and students' levels of understanding. The input to the generative AI is prompts that include the educational curriculum and teaching material requirements, and the generative AI generates teaching materials based on those prompts. Step 2: The learning plan creation unit uses the generation AI to create the optimal learning plan for each student. The generation AI analyzes the student's learning history and grade data and proposes the optimal learning schedule and learning content for each student. The input to the generation AI is a prompt containing the student's learning history and grade data, and the generation AI creates a learning plan based on that prompt. Step 3: The individualized instruction department uses generative AI to provide individualized instruction and learning support to each student. The generative AI analyzes the student's level of understanding and learning progress in real time and provides appropriate feedback and additional learning materials. The generative AI receives inputs from prompts that include data on the student's level of understanding and learning progress, and the generative AI provides individualized instruction based on those prompts. Step 4: The automatic scoring unit uses the generation AI to automatically score the student's answer and provide individual feedback. The generation AI analyzes the student's answer, evaluates the percentage of correct answers and trends in incorrect answers, and provides specific feedback on areas for improvement. The input to the generation AI is a prompt containing the student's answer data, and the generation AI scores and provides feedback based on that prompt. Step 5: The educational platform construction unit uses generative AI to integrate data from formal education and non-formal education to build an optimized educational platform. The generative AI learns formal education curriculum data, cram school know-how, and tacit knowledge based on the experience of the elderly, and proposes an optimal educational process. The generative AI receives inputs from prompts containing formal education and non-formal education data, and the generative AI builds an educational platform based on these prompts.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. Note that 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 teaching material generation department uses generative AI to generate optimal teaching materials for each grade and subject. The Learning Planning Department creates the best learning plans for each student, The individual instruction department provides individual instruction and learning support to each student. An automatic grading section that automatically grades students' answers and provides individual feedback; and an education platform construction unit that integrates data on school education or non-school education and constructs an optimized education platform. A system characterized by:

2. The teaching material generation unit Generate teaching materials based on the emotional state of the student 2. The system of claim 1.

3. The teaching material generation unit Generate multilingual learning materials adapted to different cultures or regions 2. The system of claim 1.

4. The learning plan creation unit Develop an optimal learning plan based on the student's daily rhythm or activity pattern.

2. The system of claim 1.

5. The individual instruction department: Propose optimal teaching methods based on the student's learning history and real-time understanding 2. The system of claim 1.

6. The automatic scoring unit Analyzing the student's answer patterns and providing personalized study advice 2. The system of claim 1.

7. The educational platform construction unit Integrate data on formal and informal education to propose optimal educational processes 2. The system of claim 1.

8. The educational platform construction unit Propose an educational process based on the emotional state of the student.

2. The system of claim 1.

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