System

Abstract

An object of a system according to an embodiment is to reduce a burden on a teacher and to effectively create and implement an individual learning curriculum.SOLUTION: A system according to an embodiment includes a generation AI and a teacher. The generative AI includes a learning support unit and a curriculum generation unit. The learning support unit analyzes the learning data of the student and creates an individual learning curriculum. The curriculum generation unit executes the curriculum generated by the learning support unit. The teacher includes a class operating unit and a feedback providing unit. The class operation unit is in charge of class operation and school event execution. The feedback providing unit utilizes the feedback provided from the generation AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not solved the problem of long teacher working hours, and there is also room for improvement in the creation and implementation of individual learning curricula.

[0005] The system according to the embodiment aims to reduce the burden on teachers and to effectively create and implement individual learning curricula. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI and a teacher. The generation AI comprises a learning support unit and a curriculum creation unit. The learning support unit analyzes students' learning data and creates individual learning curricula. The curriculum creation unit implements the curriculum created by the learning support unit. The teacher comprises a class management unit and a feedback provision unit. The class management unit is responsible for managing the class and carrying out school events. The feedback provision unit utilizes feedback provided by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment reduces the burden on teachers and enables them to effectively create and implement individual learning curricula. [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 an embodiment of the present invention is a system in which the generative AI and teachers share roles to improve the quality of education. In this educational system, the generative AI provides learning support, while the teacher is responsible for class management, school event execution, and feedback from the generative AI. This allows the educational system to utilize the strengths of both the generative AI and teachers to foster knowledge and humanity.

[0029] An educational system according to an embodiment includes a generating AI, a teacher, a learning support unit, a curriculum creation unit, a class management unit, and a feedback provision unit. The generating AI includes a learning support unit that analyzes students' learning data and creates individual learning curricula. For example, the generating AI proposes optimal learning plans based on students' past grades and learning progress. The generating AI also includes a curriculum creation unit that implements the curriculum created by the learning support unit. For example, the generating AI identifies areas in which students are weak and creates curricula that focus on those areas. The teacher includes a class management unit that manages class management and school events. For example, the teacher creates a class timetable and manages the progress of lessons. The teacher also includes a feedback provision unit that utilizes feedback provided by the generating AI. For example, the generating AI provides reports on students' understanding and progress, and the teacher provides individual instruction based on those reports. This allows the educational system to improve the quality of education and address the issue of long teacher working hours by dividing the roles between the generating AI and teachers.

[0030] The learning support unit can propose optimal study plans based on students' past grades and learning progress. For example, the learning support unit uses a generation AI to analyze a student's past grades and learning progress and propose optimal study plans. For example, the generation AI suggests problem sets that should be solved in the next week based on the student's grade data and learning history. The generation AI also proposes study plans that match the student's learning style and level of understanding. For example, the generation AI creates study plans that focus on areas in which the student is weak. This makes it possible to provide the optimal study plan for each student.

[0031] The curriculum creation unit can identify areas in which students are weak and create a curriculum that focuses on those areas. In the curriculum creation unit, for example, the generation AI analyzes the student's learning history and identifies the weak areas. For example, the generation AI extracts the student's weak areas based on test results and assignment evaluations. The generation AI then creates a curriculum that focuses on those areas. For example, the generation AI proposes a study plan aimed at strengthening the weak areas. This makes it possible to provide a curriculum that helps students overcome their weak areas.

[0032] The feedback providing unit provides a report on the student's level of understanding and progress, and the teacher can provide individualized instruction based on that report. In the feedback providing unit, for example, the generation AI analyzes the student's level of understanding and progress and provides it as a report. For example, the generation AI evaluates the student's level of understanding based on test results and assignment evaluations. The generation AI also monitors the student's progress in real time and provides it as a report. For example, the generation AI visualizes the student's learning progress in graphs and charts. The teacher provides individualized instruction based on that report. For example, the teacher refers to the generation AI's report and provides specific advice to the student. This enables the teacher to provide detailed instruction to each student.

[0033] The learning support unit can analyze not only students' learning history, but also their lifestyle and health data to provide comprehensive learning support. For example, the generation AI in the learning support unit analyzes lifestyle data (such as sleep time and dietary content) along with students' learning history to propose an optimal learning plan. For example, the generation AI provides learning content that allows students who are sleep-deprived to concentrate in a short amount of time. The generation AI also analyzes students' health data (such as body temperature and heart rate) to propose a learning plan based on their health condition. For example, the generation AI creates a learning plan that is less stressful for students who are not feeling well. This makes it possible to provide comprehensive learning support that takes into account students' lifestyle and health conditions.

[0034] When analyzing a student's learning progress, the learning support unit can propose plans that incorporate elements of cooperative learning and competition with other students, thereby increasing motivation to learn. In the learning support unit, for example, the generative AI analyzes a student's learning progress and proposes opportunities for cooperative learning. For example, the generative AI pairs students working on the same task and creates a plan for them to solve the problem together. The generative AI also proposes learning plans that incorporate competitive elements. For example, the generative AI creates a plan that encourages competition between students based on test rankings and assignment evaluations. In this way, incorporating elements of cooperative learning and competition can increase students' motivation to learn.

[0035] The learning support department can add a voice assistant function, allowing students to ask questions and give instructions by voice. For example, the learning support department could add a voice assistant function to the generation AI, creating a system where students can ask questions by voice and receive instant answers. For example, when a student asks a question such as "Tell me how to solve this problem," the generation AI would provide an explanation by voice. The generation AI could also adjust its learning plan based on the student's voice instructions. For example, if a student indicates that they would like to move on to the next assignment, the generation AI would update the learning plan. This allows students to ask questions and give instructions by voice, improving the convenience of learning.

[0036] The learning support unit monitors the learning environment at home and can provide appropriate support to parents. For example, the generating AI in the learning support unit monitors the learning environment at home and makes suggestions to parents to improve the learning environment. For example, the generating AI recommends ensuring a quiet learning space and using appropriate lighting. The generating AI also reports the student's learning progress to parents and encourages them to provide support at home. For example, the generating AI regularly provides reports on the student's learning status and gives specific advice to parents. This makes it possible to monitor the learning environment at home and provide appropriate support to parents.

[0037] The curriculum creation unit can study past success stories and failure stories and propose the optimal curriculum. For example, the generative AI in the curriculum creation unit studies past success stories and failure stories and proposes the optimal curriculum based on that knowledge. For example, the generative AI creates a curriculum that incorporates learning methods that have been effective in the past. The generative AI also proposes a curriculum that reflects areas for improvement based on past failure stories. For example, the generative AI analyzes the causes of past failures and creates a learning plan to avoid them. This allows the unit to study past success stories and failure stories and propose the optimal curriculum.

[0038] The curriculum creation unit can provide interactive learning materials that match the student's learning style, thereby improving learning effectiveness. In the curriculum creation unit, for example, the generation AI analyzes the student's learning style and provides interactive learning materials that match it. For example, the generation AI provides video learning materials to students who prefer visual learning. The generation AI also provides audio learning materials to students who prefer auditory learning. Furthermore, the generation AI provides simulation or game-style learning materials to students who prefer experiential learning. In this way, interactive learning materials that match the student's learning style can be provided, improving learning effectiveness.

[0039] The curriculum creation department can propose a curriculum that allows students to learn across different academic fields. For example, the generative AI proposes a curriculum that allows students to learn across different academic fields. For example, the generative AI provides project-based learning that combines mathematics and science. The generative AI also creates a curriculum that combines history and literature. Furthermore, the generative AI proposes a learning plan that combines technology and art. This allows students to broaden their learning by providing a curriculum that allows students to learn across different academic fields.

[0040] The curriculum creation unit can also support remote learning by linking with online learning platforms. In the curriculum creation unit, for example, the generative AI links with online learning platforms to support remote learning. For example, the generative AI provides online teaching materials and video lessons. The generative AI also monitors students' learning progress through online tests and forums. Furthermore, the generative AI creates curricula for remote learning and provides them to students. This allows the system to flexibly provide students' learning environments by linking with online learning platforms and supporting remote learning.

[0041] The class management department can analyze past event data and propose optimal schedules and resource allocations. For example, the class management department's generation AI analyzes past school event data and proposes optimal schedules. For example, the generation AI adjusts the schedule based on factors that contributed to the success or failure of an event. The generation AI also makes proposals to optimize resource allocation. For example, the generation AI creates plans to optimize budget allocation and personnel deployment. Furthermore, the generation AI monitors the progress of events in real time and makes adjustments as necessary. This allows the class management department to analyze past event data and propose optimal schedules and resource allocations.

[0042] The class management unit can propose individual lesson plans based on students' learning data and support efficient class management. In the class management unit, for example, the generative AI analyzes students' learning data and proposes individual lesson plans to teachers. For example, the generative AI provides additional assignments for specific students. The generative AI also adjusts the lesson plan for the entire class based on students' learning progress. For example, the generative AI suggests supplementary lessons for students who are falling behind. Furthermore, the generative AI provides advice to teachers for efficient class management. For example, the generative AI makes suggestions regarding time management and resource optimization. This makes it possible to propose individual lesson plans based on students' learning data and support efficient class management.

[0043] The class management department supports communication with parents and strengthens collaboration between home and school. For example, the generation AI supports communication with parents and strengthens collaboration between home and school. For example, the generation AI regularly reports students' learning progress to parents. The generation AI also manages schedules for meetings and contact with parents. Furthermore, the generation AI provides specific learning advice to parents. For example, the generation AI makes suggestions for improving the learning environment at home. This supports communication with parents and strengthens collaboration between home and school.

[0044] The class management department can support collaboration with the local community and propose events that utilize local resources. For example, the generation AI can support collaboration with the local community and propose school events that utilize local resources. For example, the generation AI can plan workshops that invite local experts. The generation AI can also propose learning activities that utilize local facilities. Furthermore, the generation AI can encourage participation in local events. For example, the generation AI can suggest participation in local festivals or volunteer activities. This supports collaboration with the local community and proposes events that utilize local resources.

[0045] The feedback providing unit can learn from past teaching results and propose optimal teaching methods. For example, the generation AI of the feedback providing unit learns from past teaching results and proposes optimal teaching methods to teachers. For example, the generation AI creates individual teaching plans based on teaching methods that have been effective in the past. The generation AI also proposes teaching methods that reflect areas for improvement based on past failure cases. For example, the generation AI analyzes the causes of past failures and creates a teaching plan to avoid them. Furthermore, the generation AI monitors teaching results in real time and adjusts teaching methods as necessary. This allows it to learn from past teaching results and propose optimal teaching methods.

[0046] The feedback providing unit is able to grasp students' learning progress in real time and adjust the curriculum as necessary. In the feedback providing unit, for example, the generation AI monitors students' learning progress in real time and adjusts the curriculum as necessary. For example, the generation AI suggests supplementary lessons or additional assignments for students who are lagging behind. The generation AI also provides more advanced learning content for students who are progressing quickly. Furthermore, the generation AI flexibly adjusts the curriculum according to the student's level of understanding. For example, the generation AI provides review opportunities for units that are not fully understood. This makes it possible to grasp students' learning progress in real time and adjust the curriculum as necessary.

[0047] The feedback providing unit can also provide appropriate support to parents, improving the quality of home learning. For example, the feedback providing unit allows teachers to provide home learning support to parents based on feedback provided by the generation AI. For example, the generation AI makes suggestions for improving the learning environment at home. The generation AI also reports the student's learning progress to parents and encourages them to provide support at home. For example, the generation AI regularly provides reports on the student's learning status and gives specific advice to parents. This makes it possible to provide appropriate support to parents and improve the quality of home learning.

[0048] The feedback providing unit can promote cooperative learning between students and improve learning effectiveness. For example, the feedback providing unit allows teachers to promote cooperative learning between students based on feedback provided by the generation AI. For example, the generation AI pairs students working on the same task and creates a plan to solve the problem together. The generation AI also encourages students to cooperate through group work and discussion. Furthermore, the generation AI evaluates the results of collaborative learning and provides feedback. For example, the generation AI evaluates the students' contributions in the collaborative learning process and provides specific feedback. This can promote cooperative learning between students and improve learning effectiveness.

[0049] By dividing the roles between the generative AI and the teacher, it is possible to cultivate both knowledge and humanity in a balanced manner. The generative AI supports students in improving their academic ability, while the teacher is responsible for developing their social and humanity skills. For example, the generative AI provides an individual learning curriculum, while the teacher carries out activities to foster cooperation among the entire class. The generative AI also monitors students' learning progress in real time and provides feedback to the teacher. For example, the generative AI automatically generates learning progress reports and provides them to the teacher. This division of roles between the generative AI and the teacher makes it possible to cultivate both knowledge and humanity in a balanced manner.

[0050] Generative AI monitors students' learning progress in real time and provides feedback to teachers, allowing teachers to teach more efficiently. Generative AI monitors students' learning progress in real time and provides feedback to teachers. For example, generative AI automatically generates learning progress reports and provides them to teachers. Generative AI also analyzes students' understanding and progress and provides specific teaching advice to teachers. For example, generative AI suggests additional assignments for specific students. Furthermore, generative AI supports teachers' lesson plans and enables more efficient teaching. For example, generative AI makes suggestions regarding time management and resource optimization. As a result, generative AI monitors students' learning progress in real time and provides feedback to teachers, allowing teachers to teach more efficiently.

[0051] By creating an individual learning curriculum, generative AI can provide the optimal learning environment for each student. Generative AI analyzes students' learning data and creates an individual learning curriculum. For example, generative AI proposes an optimal learning plan based on a student's past grades and learning progress. Generative AI also creates a curriculum that suits the student's learning style and level of understanding. For example, generative AI creates a curriculum that focuses on areas in which the student is weak. Furthermore, generative AI monitors students' progress in real time and adjusts the curriculum as needed. In this way, by creating an individual learning curriculum, generative AI can provide the optimal learning environment for each student.

[0052] Generative AI analyzes students' learning data and proposes individual lesson plans to teachers, thereby achieving a balanced education of knowledge and humanity. Generative AI analyzes students' learning data and proposes individual lesson plans to teachers. For example, generative AI may provide additional assignments for specific students. Generative AI also provides specific teaching advice to teachers based on students' learning progress. For example, generative AI may suggest supplementary lessons for students who are falling behind. Furthermore, generative AI supports teachers' lesson plans, achieving efficient instruction. For example, generative AI may make suggestions regarding time management and resource optimization. In this way, generative AI analyzes students' learning data and proposes individual lesson plans to teachers, thereby achieving a balanced education of knowledge and humanity.

[0053] Generative AI monitors students' learning progress in real time and provides feedback to teachers, allowing teachers to teach more efficiently. Generative AI monitors students' learning progress in real time and provides feedback to teachers. For example, generative AI automatically generates learning progress reports and provides them to teachers. Generative AI also analyzes students' understanding and progress and provides specific teaching advice to teachers. For example, generative AI suggests additional assignments for specific students. Furthermore, generative AI supports teachers' lesson plans and enables more efficient teaching. For example, generative AI makes suggestions regarding time management and resource optimization. As a result, generative AI monitors students' learning progress in real time and provides feedback to teachers, allowing teachers to teach more efficiently.

[0054] Generative AI analyzes students' learning data and proposes individual lesson plans to teachers, thereby achieving a balanced education of knowledge and humanity. Generative AI analyzes students' learning data and proposes individual lesson plans to teachers. For example, generative AI may provide additional assignments for specific students. Generative AI also provides specific teaching advice to teachers based on students' learning progress. For example, generative AI may suggest supplementary lessons for students who are falling behind. Furthermore, generative AI supports teachers' lesson plans, achieving efficient instruction. For example, generative AI may make suggestions regarding time management and resource optimization. In this way, generative AI analyzes students' learning data and proposes individual lesson plans to teachers, thereby achieving a balanced education of knowledge and humanity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The education system can further include a creativity support unit to foster students' creativity. In this creativity support unit, for example, a generative AI analyzes students' interests and suggests creative tasks based on that analysis. For example, the generative AI creates project-based learning tasks based on themes that interest students. The generative AI also provides inspiration to bring out students' creativity. For example, the generative AI introduces related art, music, and literature to stimulate students' creative thinking. Furthermore, the generative AI evaluates students' creative achievements and provides feedback. For example, the generative AI points out specific areas for improvement in students' works and suggests next steps. This can help foster students' creativity.

[0057] The curriculum creation department can propose curricula that allow students to study across different academic fields. For example, generative AI proposes curricula that allow students to study across different academic fields. For example, generative AI provides project-based learning that combines mathematics and science. Generative AI also creates curricula that combine history and literature. Furthermore, generative AI proposes learning plans that combine technology and art. This allows students to broaden their learning by providing curricula that allow students to study across different academic fields.

[0058] The Learning Support Department can add a voice assistant function, allowing students to ask questions and give instructions by voice. For example, by adding a voice assistant function to the Generative AI, a system can be created in which students can ask questions by voice and receive instant answers. For example, if a student asks a question such as "Please tell me how to solve this problem," the Generative AI will provide an explanation by voice. The Generative AI can also adjust its learning plan based on the student's voice instructions. For example, if a student indicates that they want to move on to the next assignment, the Generative AI will update the learning plan. This allows students to ask questions and give instructions by voice, improving the convenience of learning.

[0059] The class management department can analyze past event data and propose optimal schedules and resource allocations. For example, a generation AI analyzes past school event data and proposes optimal schedules. For example, the generation AI adjusts the schedule based on factors that contributed to the success or failure of an event. The generation AI also makes proposals to optimize resource allocation. For example, the generation AI creates plans to optimize budget allocation and personnel deployment. Furthermore, the generation AI monitors the progress of events in real time and makes adjustments as necessary. This allows the analysis of past event data to propose optimal schedules and resource allocations.

[0060] The class management department can support communication with parents and strengthen collaboration between home and school. For example, the generation AI can support communication with parents and strengthen collaboration between home and school. For example, the generation AI can regularly report students' learning progress to parents. The generation AI can also manage schedules for parent-teacher meetings and contact. Furthermore, the generation AI can provide parents with specific learning advice. For example, the generation AI can make suggestions for improving the learning environment at home. This can support communication with parents and strengthen collaboration between home and school.

[0061] The feedback providing unit can learn from past teaching results and suggest optimal teaching methods. For example, the generation AI learns from past teaching results and suggests optimal teaching methods to teachers. For example, the generation AI creates individual teaching plans based on teaching methods that have been effective in the past. The generation AI also suggests teaching methods that reflect areas for improvement based on past failure cases. For example, the generation AI analyzes the causes of past failures and creates a teaching plan to avoid them. Furthermore, the generation AI monitors teaching results in real time and adjusts teaching methods as necessary. This allows it to learn from past teaching results and suggest optimal teaching methods.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The Learning Support Department analyzes the student's learning data and creates an individual learning curriculum. For example, it proposes an optimal learning plan based on the student's past grades and learning progress. Step 2: The Curriculum Development Department implements the curriculum created by the Learning Support Department. For example, they identify areas in which students are weak and create curriculum that focuses on those areas. Step 3: The Classroom Management Department is responsible for managing classes and carrying out school events, such as creating class timetables and managing the progress of lessons. Step 4: The feedback provider utilizes the feedback provided by the generation AI. For example, the generation AI may provide a report on the student's understanding and progress, and the teacher may provide individualized instruction based on that report.

[0064] (Example 2) The educational system according to an embodiment of the present invention is a system in which the generative AI and teachers share roles to improve the quality of education. In this educational system, the generative AI provides learning support, while the teacher is responsible for class management, school event execution, and feedback from the generative AI. This allows the educational system to utilize the strengths of both the generative AI and teachers to foster knowledge and humanity.

[0065] An educational system according to an embodiment includes a generating AI, a teacher, a learning support unit, a curriculum creation unit, a class management unit, and a feedback provision unit. The generating AI includes a learning support unit that analyzes students' learning data and creates individual learning curricula. For example, the generating AI proposes optimal learning plans based on students' past grades and learning progress. The generating AI also includes a curriculum creation unit that implements the curriculum created by the learning support unit. For example, the generating AI identifies areas in which students are weak and creates curricula that focus on those areas. The teacher includes a class management unit that manages class management and school events. For example, the teacher creates a class timetable and manages the progress of lessons. The teacher also includes a feedback provision unit that utilizes feedback provided by the generating AI. For example, the generating AI provides reports on students' understanding and progress, and the teacher provides individual instruction based on those reports. This allows the educational system to improve the quality of education and address the issue of long teacher working hours by dividing the roles between the generating AI and teachers.

[0066] The learning support unit can propose optimal study plans based on students' past grades and learning progress. For example, the learning support unit uses a generation AI to analyze a student's past grades and learning progress and propose optimal study plans. For example, the generation AI suggests problem sets that should be solved in the next week based on the student's grade data and learning history. The generation AI also proposes study plans that match the student's learning style and level of understanding. For example, the generation AI creates study plans that focus on areas in which the student is weak. This makes it possible to provide the optimal study plan for each student.

[0067] The curriculum creation unit can identify areas in which students are weak and create a curriculum that focuses on those areas. In the curriculum creation unit, for example, the generation AI analyzes the student's learning history and identifies the weak areas. For example, the generation AI extracts the student's weak areas based on test results and assignment evaluations. The generation AI then creates a curriculum that focuses on those areas. For example, the generation AI proposes a study plan aimed at strengthening the weak areas. This makes it possible to provide a curriculum that helps students overcome their weak areas.

[0068] The feedback providing unit provides a report on the student's level of understanding and progress, and the teacher can provide individualized instruction based on that report. In the feedback providing unit, for example, the generation AI analyzes the student's level of understanding and progress and provides it as a report. For example, the generation AI evaluates the student's level of understanding based on test results and assignment evaluations. The generation AI also monitors the student's progress in real time and provides it as a report. For example, the generation AI visualizes the student's learning progress in graphs and charts. The teacher provides individualized instruction based on that report. For example, the teacher refers to the generation AI's report and provides specific advice to the student. This enables the teacher to provide detailed instruction to each student.

[0069] The learning support unit uses the emotion estimation function to consider the student's emotional state and can propose a study plan that corresponds to fluctuations in stress and motivation. For example, when the generation AI analyzes the student's learning data, the learning support unit uses the emotion estimation function to monitor the student's emotional state in real time. For example, the generation AI analyzes facial expressions and voice while studying, and if stress is rising, proposes a study plan that helps students relax. In addition, if the student's motivation is declining, the generation AI proposes a study plan to increase motivation. For example, the generation AI creates a study plan that incorporates topics that interest the student. This makes it possible to provide a study plan that corresponds to the student's emotional state.

[0070] The learning support unit can analyze not only students' learning history, but also their lifestyle and health data to provide comprehensive learning support. For example, the generation AI in the learning support unit analyzes lifestyle data (such as sleep time and dietary content) along with students' learning history to propose an optimal learning plan. For example, the generation AI provides learning content that allows students who are sleep-deprived to concentrate in a short amount of time. The generation AI also analyzes students' health data (such as body temperature and heart rate) to propose a learning plan based on their health condition. For example, the generation AI creates a learning plan that is less stressful for students who are not feeling well. This makes it possible to provide comprehensive learning support that takes into account students' lifestyle and health conditions.

[0071] When analyzing a student's learning progress, the learning support unit can propose plans that incorporate elements of cooperative learning and competition with other students, thereby increasing motivation to learn. In the learning support unit, for example, the generative AI analyzes a student's learning progress and proposes opportunities for cooperative learning. For example, the generative AI pairs students working on the same task and creates a plan for them to solve the problem together. The generative AI also proposes learning plans that incorporate competitive elements. For example, the generative AI creates a plan that encourages competition between students based on test rankings and assignment evaluations. In this way, incorporating elements of cooperative learning and competition can increase students' motivation to learn.

[0072] The learning support department can add a voice assistant function, allowing students to ask questions and give instructions by voice. For example, the learning support department could add a voice assistant function to the generation AI, creating a system where students can ask questions by voice and receive instant answers. For example, when a student asks a question such as "Tell me how to solve this problem," the generation AI would provide an explanation by voice. The generation AI could also adjust its learning plan based on the student's voice instructions. For example, if a student indicates that they would like to move on to the next assignment, the generation AI would update the learning plan. This allows students to ask questions and give instructions by voice, improving the convenience of learning.

[0073] The learning support unit monitors the learning environment at home and can provide appropriate support to parents. For example, the generating AI in the learning support unit monitors the learning environment at home and makes suggestions to parents to improve the learning environment. For example, the generating AI recommends ensuring a quiet learning space and using appropriate lighting. The generating AI also reports the student's learning progress to parents and encourages them to provide support at home. For example, the generating AI regularly provides reports on the student's learning status and gives specific advice to parents. This makes it possible to monitor the learning environment at home and provide appropriate support to parents.

[0074] The learning support unit uses the emotion estimation function to monitor students' emotional state in real time and can suggest breaks and refreshment at appropriate times. For example, the learning support unit's generation AI monitors students' emotional state in real time and suggests breaks if stress levels rise. For example, the generation AI may issue a notification such as, "Take a short break to refresh yourself." In addition, if a student's motivation is declining, the generation AI may suggest refreshing activities. For example, the generation AI may advise, "Take a short walk to change your mood." This makes it possible to suggest breaks and refreshment at appropriate times according to the student's emotional state.

[0075] The curriculum creation unit can use the emotion estimation function to design a curriculum that will increase students' motivation to learn. For example, the generation AI analyzes students' emotional data and designs a curriculum that will increase students' motivation to learn. For example, the generation AI provides learning materials that incorporate topics that interest students. The generation AI also adjusts the curriculum according to the student's emotional state. For example, the generation AI provides relaxing learning content to students who are experiencing high levels of stress. This makes it possible to design a curriculum that will increase students' motivation to learn.

[0076] The curriculum creation unit can study past success stories and failure stories and propose the optimal curriculum. For example, the generative AI in the curriculum creation unit studies past success stories and failure stories and proposes the optimal curriculum based on that knowledge. For example, the generative AI creates a curriculum that incorporates learning methods that have been effective in the past. The generative AI also proposes a curriculum that reflects areas for improvement based on past failure stories. For example, the generative AI analyzes the causes of past failures and creates a learning plan to avoid them. This allows the unit to study past success stories and failure stories and propose the optimal curriculum.

[0077] The curriculum creation unit can provide interactive learning materials that match the student's learning style, thereby improving learning effectiveness. In the curriculum creation unit, for example, the generation AI analyzes the student's learning style and provides interactive learning materials that match it. For example, the generation AI provides video learning materials to students who prefer visual learning. The generation AI also provides audio learning materials to students who prefer auditory learning. Furthermore, the generation AI provides simulation or game-style learning materials to students who prefer experiential learning. In this way, interactive learning materials that match the student's learning style can be provided, improving learning effectiveness.

[0078] The curriculum creation department can propose a curriculum that allows students to learn across different academic fields. For example, the generative AI proposes a curriculum that allows students to learn across different academic fields. For example, the generative AI provides project-based learning that combines mathematics and science. The generative AI also creates a curriculum that combines history and literature. Furthermore, the generative AI proposes a learning plan that combines technology and art. This allows students to broaden their learning by providing a curriculum that allows students to learn across different academic fields.

[0079] The curriculum creation unit can also support remote learning by linking with online learning platforms. In the curriculum creation unit, for example, the generative AI links with online learning platforms to support remote learning. For example, the generative AI provides online teaching materials and video lessons. The generative AI also monitors students' learning progress through online tests and forums. Furthermore, the generative AI creates curricula for remote learning and provides them to students. This allows the system to flexibly provide students' learning environments by linking with online learning platforms and supporting remote learning.

[0080] The curriculum creation unit uses the emotion estimation function to take into account the student's emotional state and can flexibly adjust the curriculum according to their learning progress. For example, the generation AI in the curriculum creation unit monitors the student's emotional state in real time and flexibly adjusts the curriculum according to their learning progress. For example, the generation AI reduces the learning content if stress increases. Also, if motivation is declining, the generation AI provides a learning plan that incorporates interesting topics. Furthermore, the generation AI suggests times for breaks and refreshment according to the student's emotional state. This makes it possible to provide a flexible curriculum that is tailored to the student's learning progress according to their emotional state.

[0081] Using the generative AI's emotion estimation function, the class management department can grasp the overall class atmosphere and the emotional state of students in real time and suggest appropriate responses. For example, the generative AI monitors the overall class atmosphere in real time and suggests appropriate responses to the teacher. For example, if the atmosphere in the class is tense, the generative AI will suggest a relaxing activity. The generative AI can also analyze students' emotional states and suggest individual responses to the teacher. For example, the generative AI might suggest offering words of encouragement to a student who is experiencing increasing stress. This allows the class management department to grasp the overall class atmosphere and the emotional state of students in real time and suggest appropriate responses.

[0082] The class management department can analyze past event data and propose optimal schedules and resource allocations. For example, the class management department's generation AI analyzes past school event data and proposes optimal schedules. For example, the generation AI adjusts the schedule based on factors that contributed to the success or failure of an event. The generation AI also makes proposals to optimize resource allocation. For example, the generation AI creates plans to optimize budget allocation and personnel deployment. Furthermore, the generation AI monitors the progress of events in real time and makes adjustments as necessary. This allows the class management department to analyze past event data and propose optimal schedules and resource allocations.

[0083] The class management unit can propose individual lesson plans based on students' learning data and support efficient class management. In the class management unit, for example, the generative AI analyzes students' learning data and proposes individual lesson plans to teachers. For example, the generative AI provides additional assignments for specific students. The generative AI also adjusts the lesson plan for the entire class based on students' learning progress. For example, the generative AI suggests supplementary lessons for students who are falling behind. Furthermore, the generative AI provides advice to teachers for efficient class management. For example, the generative AI makes suggestions regarding time management and resource optimization. This makes it possible to propose individual lesson plans based on students' learning data and support efficient class management.

[0084] The class management department supports communication with parents and strengthens collaboration between home and school. For example, the generation AI supports communication with parents and strengthens collaboration between home and school. For example, the generation AI regularly reports students' learning progress to parents. The generation AI also manages schedules for meetings and contact with parents. Furthermore, the generation AI provides specific learning advice to parents. For example, the generation AI makes suggestions for improving the learning environment at home. This supports communication with parents and strengthens collaboration between home and school.

[0085] The class management department can support collaboration with the local community and propose events that utilize local resources. For example, the generation AI can support collaboration with the local community and propose school events that utilize local resources. For example, the generation AI can plan workshops that invite local experts. The generation AI can also propose learning activities that utilize local facilities. Furthermore, the generation AI can encourage participation in local events. For example, the generation AI can suggest participation in local festivals or volunteer activities. This supports collaboration with the local community and proposes events that utilize local resources.

[0086] The class management department can use the generative AI's emotion estimation function to monitor the emotional state of the entire class in real time and suggest refreshing activities at the appropriate time. For example, the generative AI can monitor the emotional state of the entire class in real time and suggest refreshing activities. For example, if the entire class is tired, the generative AI can suggest a short break. Also, if the atmosphere in the class is tense, the generative AI can suggest relaxing activities. Furthermore, the generative AI can suggest individual refreshing activities depending on the emotional state of each student. For example, the generative AI can suggest relaxation methods for students who are experiencing increasing stress. This allows the class to monitor the emotional state of the entire class in real time and suggest refreshing activities at the appropriate time.

[0087] The feedback providing unit incorporates an emotion estimation function and can provide feedback that takes into account the student's emotional state. For example, the generation AI analyzes the student's emotional state and provides feedback according to the emotion. For example, the generation AI sends an encouraging message if the student is feeling stressed. Furthermore, the generation AI provides advice to increase motivation if the student's motivation is low. Furthermore, the generation AI adjusts the content of the feedback according to the student's emotional state. For example, if the student's emotions are stable, the generation AI provides feedback that points out specific areas for improvement. This makes it possible to provide feedback that takes into account the student's emotional state.

[0088] The feedback providing unit can learn from past teaching results and propose optimal teaching methods. For example, the generation AI of the feedback providing unit learns from past teaching results and proposes optimal teaching methods to teachers. For example, the generation AI creates individual teaching plans based on teaching methods that have been effective in the past. The generation AI also proposes teaching methods that reflect areas for improvement based on past failure cases. For example, the generation AI analyzes the causes of past failures and creates a teaching plan to avoid them. Furthermore, the generation AI monitors teaching results in real time and adjusts teaching methods as necessary. This allows it to learn from past teaching results and propose optimal teaching methods.

[0089] The feedback providing unit is able to grasp students' learning progress in real time and adjust the curriculum as necessary. In the feedback providing unit, for example, the generation AI monitors students' learning progress in real time and adjusts the curriculum as necessary. For example, the generation AI suggests supplementary lessons or additional assignments for students who are lagging behind. The generation AI also provides more advanced learning content for students who are progressing quickly. Furthermore, the generation AI flexibly adjusts the curriculum according to the student's level of understanding. For example, the generation AI provides review opportunities for units that are not fully understood. This makes it possible to grasp students' learning progress in real time and adjust the curriculum as necessary.

[0090] The feedback providing unit can also provide appropriate support to parents, improving the quality of home learning. For example, the feedback providing unit allows teachers to provide home learning support to parents based on feedback provided by the generation AI. For example, the generation AI makes suggestions for improving the learning environment at home. The generation AI also reports the student's learning progress to parents and encourages them to provide support at home. For example, the generation AI regularly provides reports on the student's learning status and gives specific advice to parents. This makes it possible to provide appropriate support to parents and improve the quality of home learning.

[0091] The feedback providing unit can promote cooperative learning between students and improve learning effectiveness. For example, the feedback providing unit allows teachers to promote cooperative learning between students based on feedback provided by the generation AI. For example, the generation AI pairs students working on the same task and creates a plan to solve the problem together. The generation AI also encourages students to cooperate through group work and discussion. Furthermore, the generation AI evaluates the results of collaborative learning and provides feedback. For example, the generation AI evaluates the students' contributions in the collaborative learning process and provides specific feedback. This can promote cooperative learning between students and improve learning effectiveness.

[0092] The feedback providing unit incorporates an emotion estimation function, and is able to monitor the student's emotional state in real time and provide feedback at the appropriate time. In the feedback providing unit, for example, the generation AI monitors the student's emotional state in real time and provides feedback at the appropriate time. For example, if stress is increasing, the generation AI sends an encouraging message. Also, if motivation is decreasing, the generation AI provides advice to increase motivation. Furthermore, the generation AI adjusts the content of the feedback according to the student's emotional state. For example, if emotions are stable, the generation AI provides feedback that points out specific areas for improvement. This makes it possible to monitor the student's emotional state in real time and provide feedback at the appropriate time.

[0093] By dividing the roles between the generative AI and the teacher, it is possible to cultivate both knowledge and humanity in a balanced manner. The generative AI supports students in improving their academic ability, while the teacher is responsible for developing their social and humanity skills. For example, the generative AI provides an individual learning curriculum, while the teacher carries out activities to foster cooperation among the entire class. The generative AI also monitors students' learning progress in real time and provides feedback to the teacher. For example, the generative AI automatically generates learning progress reports and provides them to the teacher. This division of roles between the generative AI and the teacher makes it possible to cultivate both knowledge and humanity in a balanced manner.

[0094] Generative AI monitors students' learning progress in real time and provides feedback to teachers, allowing teachers to teach more efficiently. Generative AI monitors students' learning progress in real time and provides feedback to teachers. For example, generative AI automatically generates learning progress reports and provides them to teachers. Generative AI also analyzes students' understanding and progress and provides specific teaching advice to teachers. For example, generative AI suggests additional assignments for specific students. Furthermore, generative AI supports teachers' lesson plans and enables more efficient teaching. For example, generative AI makes suggestions regarding time management and resource optimization. As a result, generative AI monitors students' learning progress in real time and provides feedback to teachers, allowing teachers to teach more efficiently.

[0095] By creating an individual learning curriculum, generative AI can provide the optimal learning environment for each student. Generative AI analyzes students' learning data and creates an individual learning curriculum. For example, generative AI proposes an optimal learning plan based on a student's past grades and learning progress. Generative AI also creates a curriculum that suits the student's learning style and level of understanding. For example, generative AI creates a curriculum that focuses on areas in which the student is weak. Furthermore, generative AI monitors students' progress in real time and adjusts the curriculum as needed. In this way, by creating an individual learning curriculum, generative AI can provide the optimal learning environment for each student.

[0096] Generative AI analyzes students' learning data and proposes individual lesson plans to teachers, thereby achieving a balanced education of knowledge and humanity. Generative AI analyzes students' learning data and proposes individual lesson plans to teachers. For example, generative AI may provide additional assignments for specific students. Generative AI also provides specific teaching advice to teachers based on students' learning progress. For example, generative AI may suggest supplementary lessons for students who are falling behind. Furthermore, generative AI supports teachers' lesson plans, achieving efficient instruction. For example, generative AI may make suggestions regarding time management and resource optimization. In this way, generative AI analyzes students' learning data and proposes individual lesson plans to teachers, thereby achieving a balanced education of knowledge and humanity.

[0097] Generative AI monitors students' learning progress in real time and provides feedback to teachers, allowing teachers to teach more efficiently. Generative AI monitors students' learning progress in real time and provides feedback to teachers. For example, generative AI automatically generates learning progress reports and provides them to teachers. Generative AI also analyzes students' understanding and progress and provides specific teaching advice to teachers. For example, generative AI suggests additional assignments for specific students. Furthermore, generative AI supports teachers' lesson plans and enables more efficient teaching. For example, generative AI makes suggestions regarding time management and resource optimization. As a result, generative AI monitors students' learning progress in real time and provides feedback to teachers, allowing teachers to teach more efficiently.

[0098] Generative AI analyzes students' learning data and proposes individual lesson plans to teachers, thereby achieving a balanced education of knowledge and humanity. Generative AI analyzes students' learning data and proposes individual lesson plans to teachers. For example, generative AI may provide additional assignments for specific students. Generative AI also provides specific teaching advice to teachers based on students' learning progress. For example, generative AI may suggest supplementary lessons for students who are falling behind. Furthermore, generative AI supports teachers' lesson plans, achieving efficient instruction. For example, generative AI may make suggestions regarding time management and resource optimization. In this way, generative AI analyzes students' learning data and proposes individual lesson plans to teachers, thereby achieving a balanced education of knowledge and humanity.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The education system can further include a creativity support unit to foster students' creativity. In this creativity support unit, for example, a generative AI analyzes students' interests and suggests creative tasks based on that analysis. For example, the generative AI creates project-based learning tasks based on themes that interest students. The generative AI also provides inspiration to bring out students' creativity. For example, the generative AI introduces related art, music, and literature to stimulate students' creative thinking. Furthermore, the generative AI evaluates students' creative achievements and provides feedback. For example, the generative AI points out specific areas for improvement in students' works and suggests next steps. This can help foster students' creativity.

[0101] The learning support unit uses the emotion estimation function to consider the student's emotional state and can flexibly adjust the curriculum according to their learning progress. For example, the generation AI monitors the student's emotional state in real time and flexibly adjusts the curriculum according to their learning progress. For example, the generation AI reduces the learning content if stress increases. Also, if motivation decreases, the generation AI provides a learning plan that incorporates topics that pique the student's interest. Furthermore, the generation AI suggests times for breaks and refreshment according to the student's emotional state. This makes it possible to provide a flexible curriculum that matches the student's learning progress according to their emotional state.

[0102] The curriculum creation department can propose curricula that allow students to study across different academic fields. For example, generative AI proposes curricula that allow students to study across different academic fields. For example, generative AI provides project-based learning that combines mathematics and science. Generative AI also creates curricula that combine history and literature. Furthermore, generative AI proposes learning plans that combine technology and art. This allows students to broaden their learning by providing curricula that allow students to study across different academic fields.

[0103] The feedback providing unit incorporates an emotion estimation function and can provide feedback that takes into account the student's emotional state. For example, the generation AI analyzes the student's emotional state and provides feedback that corresponds to the emotion. For example, if stress is rising, the generation AI will send an encouraging message. Also, if motivation is declining, the generation AI will provide advice to increase motivation. Furthermore, the generation AI will adjust the content of the feedback according to the student's emotional state. For example, if emotions are stable, the generation AI will provide feedback that points out specific areas for improvement. This makes it possible to provide feedback that takes into account the student's emotional state.

[0104] The Learning Support Department can add a voice assistant function, allowing students to ask questions and give instructions by voice. For example, by adding a voice assistant function to the Generative AI, a system can be created in which students can ask questions by voice and receive instant answers. For example, if a student asks a question such as "Please tell me how to solve this problem," the Generative AI will provide an explanation by voice. The Generative AI can also adjust its learning plan based on the student's voice instructions. For example, if a student indicates that they want to move on to the next assignment, the Generative AI will update the learning plan. This allows students to ask questions and give instructions by voice, improving the convenience of learning.

[0105] The Learning Support Department uses the emotion estimation function to monitor students' emotional state in real time and suggests breaks and refreshment at appropriate times. For example, the generation AI monitors students' emotional state in real time and suggests breaks if stress levels rise. For example, the generation AI may issue a notification such as, "Take a short break to refresh yourself." In addition, if a student's motivation is declining, the generation AI may suggest refreshing activities. For example, the generation AI may advise, "Take a short walk to change your mood." This allows the system to suggest breaks and refreshment at appropriate times according to the student's emotional state.

[0106] The class management department can analyze past event data and propose optimal schedules and resource allocations. For example, a generation AI analyzes past school event data and proposes optimal schedules. For example, the generation AI adjusts the schedule based on factors that contributed to the success or failure of an event. The generation AI also makes proposals to optimize resource allocation. For example, the generation AI creates plans to optimize budget allocation and personnel deployment. Furthermore, the generation AI monitors the progress of events in real time and makes adjustments as necessary. This allows the analysis of past event data to propose optimal schedules and resource allocations.

[0107] Using the generative AI's emotion estimation function, class management departments can grasp the overall class atmosphere and the emotional state of students in real time and suggest appropriate responses. For example, the generative AI monitors the overall class atmosphere in real time and suggests appropriate responses to the teacher. For example, if the atmosphere in the class is tense, the generative AI suggests a relaxing activity. The generative AI also analyzes students' emotional states and suggests individual responses to the teacher. For example, the generative AI might suggest offering words of encouragement to a student who is experiencing increasing stress. This makes it possible to grasp the overall class atmosphere and the emotional state of students in real time and suggest appropriate responses.

[0108] The class management department can support communication with parents and strengthen collaboration between home and school. For example, the generation AI can support communication with parents and strengthen collaboration between home and school. For example, the generation AI can regularly report students' learning progress to parents. The generation AI can also manage schedules for parent-teacher meetings and contact. Furthermore, the generation AI can provide parents with specific learning advice. For example, the generation AI can make suggestions for improving the learning environment at home. This can support communication with parents and strengthen collaboration between home and school.

[0109] The feedback providing unit can learn from past teaching results and suggest optimal teaching methods. For example, the generation AI learns from past teaching results and suggests optimal teaching methods to teachers. For example, the generation AI creates individual teaching plans based on teaching methods that have been effective in the past. The generation AI also suggests teaching methods that reflect areas for improvement based on past failure cases. For example, the generation AI analyzes the causes of past failures and creates a teaching plan to avoid them. Furthermore, the generation AI monitors teaching results in real time and adjusts teaching methods as necessary. This allows it to learn from past teaching results and suggest optimal teaching methods.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The Learning Support Department analyzes the student's learning data and creates an individual learning curriculum. For example, it proposes an optimal learning plan based on the student's past grades and learning progress. Step 2: The Curriculum Development Department implements the curriculum created by the Learning Support Department. For example, they identify areas in which students are weak and create curriculum that focuses on those areas. Step 3: The Classroom Management Department is responsible for managing classes and carrying out school events, such as creating class timetables and managing the progress of lessons. Step 4: The feedback provider utilizes the feedback provided by the generation AI. For example, the generation AI may provide a report on the student's understanding and progress, and the teacher may provide individualized instruction based on that report.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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]

[0179] 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. Generative AI and With teachers, The generated AI is The Learning Support Department analyzes students' learning data and creates individual learning curricula. a curriculum creation unit that implements the curriculum created by the learning support unit; The teacher: The Class Management Department is responsible for managing classes and carrying out school events, and a feedback providing unit that utilizes feedback provided by the generation AI; A system characterized by:

2. The learning support unit Add a voice assistant function so that the student can ask questions or give instructions by voice.

2. The system of claim 1.

3. The curriculum creation department Propose a curriculum that allows students to study across different academic fields.

2. The system of claim 1.

4. The class management department: Grasp the overall atmosphere of the class and the emotional state of the student in real time and suggest appropriate responses 2. The system of claim 1.

5. The feedback providing unit: Incorporating an emotion estimation function to provide the feedback taking into account the emotional state of the student.

2. The system of claim 1.

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