system

Abstract

The system according to this embodiment aims to comprehensively support teachers in improving work efficiency, assisting with lessons, developing curricula, and training teachers. [Solution] The system according to the embodiment comprises a business support unit, a lesson support unit, a curriculum development unit, and a teacher training unit. The business support unit supports the work of teachers. The lesson support unit supports lessons based on the business data supported by the business support unit. The curriculum development unit develops a curriculum based on the lesson data supported by the lesson support unit. The teacher training unit trains teachers based on the curriculum developed by the curriculum development unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system that comprehensively supports the improvement of teachers' work efficiency, class support, curriculum development, and teacher training has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively support the improvement of teachers' work efficiency, class support, curriculum development, and teacher training.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a business support unit, a lesson support unit, a curriculum development unit, and a teacher training unit. The business support unit supports teachers' work. The lesson support unit supports lessons based on business data supported by the business support unit. The curriculum development unit develops curricula based on lesson data supported by the lesson support unit. The teacher training unit trains teachers based on the curriculum developed by the curriculum development unit. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively support teachers in improving work efficiency, assisting with lessons, developing curricula, and training teachers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system for education according to an embodiment of the present invention is a system that uses robots active in educational settings as its icon. This system utilizes multiple AI agents to comprehensively support teachers in improving work efficiency, assisting with lessons, developing curricula, and training teachers. This improves the quality of education and contributes to solving the problem of teacher shortages. For example, the work support agent automates administrative tasks and manages schedules, reducing the burden on teachers. For example, by automating routine tasks such as attendance management and grade entry, teachers can concentrate on lesson preparation and student guidance. Next, the lesson support agent provides real-time assistance during lessons and analyzes students' comprehension levels, supporting effective lesson management. For example, it can immediately respond to students' questions during lessons and adjust the progress of lessons by grasping students' comprehension levels in real time. Furthermore, the curriculum development agent analyzes the latest educational trends and student learning data and proposes the optimal curriculum. For example, it creates curricula tailored to each student's learning progress and interests, realizing individualized instruction. Finally, the teacher training agent supports the improvement of teachers' professionalism through evaluation of teaching skills and proposal of training programs. For example, by recording and analyzing teachers' lessons and providing feedback on areas for improvement, teachers' instructional skills can be enhanced. This system improves the quality of education, solves the problem of teacher shortages, and creates a sustainable educational environment. By utilizing AI agents, the burden on teachers is reduced and the quality of education is improved, contributing to the development of human resources that will support Japan's future. In this way, AI agent systems for education can improve the quality of education by streamlining teachers' work and comprehensively supporting lesson support, curriculum development, and teacher training.

[0029] The AI ​​agent system for education according to this embodiment comprises a business support unit, a lesson support unit, a curriculum development unit, and a teacher training unit. The business support unit supports teachers' work. For example, the business support unit automates administrative tasks and manages schedules. For example, the business support unit can automate routine tasks such as attendance management and grade entry. The business support unit can also manage teachers' schedules, allowing them to concentrate on lesson preparation and student guidance. Furthermore, the business support unit can collect teachers' work data and improve work efficiency. For example, the business support unit can analyze teachers' work data and determine task priorities. The lesson support unit provides real-time assistance during lessons and analysis of student comprehension. For example, the lesson support unit can immediately respond to student questions during lessons. Furthermore, the lesson support unit can grasp students' comprehension in real time and adjust the progress of the lesson. Furthermore, the lesson support unit can collect lesson data and analyze the effectiveness of lessons. For example, the lesson support unit can analyze student reactions during lessons and suggest areas for improvement in the lesson. The Curriculum Development Department analyzes the latest educational trends and student learning data to propose optimal curricula. For example, the Curriculum Development Department can create curricula tailored to each student's learning pace and interests. It can also analyze educational trends and propose curricula that incorporate the latest teaching methods. Furthermore, the Curriculum Development Department can analyze student learning data and create curricula optimized for individualized instruction. For example, it can create individualized instruction curricula based on student learning data. The Teacher Development Department supports the improvement of teachers' professionalism through the evaluation of teaching skills and the proposal of training programs. For example, the Teacher Development Department can record and analyze teachers' lessons and provide feedback on areas for improvement. It can also evaluate teachers' teaching skills and propose training programs. Furthermore, the Teacher Development Department can collect teachers' teaching data to improve their teaching abilities. For example, the Teacher Development Department can analyze teachers' teaching data and provide information that helps improve teaching skills.As a result, the AI ​​agent system for education according to this embodiment can streamline teachers' work and comprehensively support classroom instruction, curriculum development, and teacher training, thereby improving the quality of education.

[0030] The Business Support Department assists teachers with their work. For example, it automates administrative tasks and manages schedules. Specifically, it can automate routine tasks such as attendance management and grade entry. This frees teachers from cumbersome manual administrative work, allowing them to dedicate more time to lesson preparation and student guidance. The Business Support Department can also manage teachers' schedules, enabling them to focus on lesson preparation and student guidance. For example, it can automatically adjust teachers' schedules to ensure time for important meetings and lesson preparation. Furthermore, the Business Support Department can collect teachers' work data to improve efficiency. For example, it can analyze teachers' work data to determine task priorities. This allows teachers to work more efficiently and reduce stress. In addition, the Business Support Department can use AI to learn teachers' work patterns and propose optimal workflows. For example, based on past work data, the AI ​​can suggest which tasks teachers should perform at what times, thereby improving efficiency. Furthermore, the Business Support Department can provide reminder and task management functions to reduce the workload of teachers. This allows teachers to avoid forgetting important tasks and to proceed with their work systematically.

[0031] The Classroom Support Department provides real-time assistance during lessons and analysis of student comprehension. For example, the Classroom Support Department can instantly respond to student questions during lessons. Specifically, it uses AI to analyze student questions and provide appropriate answers. This allows teachers to respond quickly to student questions during lessons and ensure smooth lesson progress. The Classroom Support Department can also grasp student comprehension in real time and adjust the lesson pace accordingly. For example, it analyzes students' facial expressions and reactions to determine whether students understand the material. If comprehension is low, the department can slow down the lesson or provide supplementary explanations. Furthermore, the Classroom Support Department can collect lesson data and analyze the effectiveness of lessons. For example, it can analyze student reactions during lessons and suggest areas for improvement. This allows teachers to receive specific advice to improve the quality of their lessons. The Classroom Support Department can also provide advice for individualized instruction based on student learning data. For example, it can analyze students' learning progress and comprehension to suggest the optimal approach for individualized instruction. This allows teachers to provide individualized instruction to each student, maximizing their learning effectiveness.

[0032] The Curriculum Development Department analyzes the latest educational trends and student learning data to propose optimal curricula. For example, it can create curricula tailored to each student's learning pace and interests. Specifically, it uses AI to analyze student learning data and propose curricula that meet individual learning needs. This allows students to learn at their own pace and enhance learning effectiveness. The Curriculum Development Department can also analyze educational trends and propose curricula that incorporate the latest teaching methods. For example, it creates and provides curricula that incorporate the latest educational research and technologies. This allows students to acquire the latest knowledge and skills, which will benefit their future careers. Furthermore, the Curriculum Development Department can analyze student learning data and create curricula optimized for individualized instruction. For example, it can create individualized instruction curricula based on student learning data. This allows students to receive instruction tailored to their learning style and pace, maximizing learning effectiveness. The Curriculum Development Department can also collaborate with teachers to improve curricula. For example, it can review the content and progression of curricula based on teacher feedback to provide more effective curricula. This allows the curriculum development department to consistently provide the most up-to-date curriculum incorporating the latest information and technology, maximizing student learning effectiveness.

[0033] The Teacher Development Department supports the professional development of teachers through the evaluation of teaching skills and the proposal of training programs. For example, the Teacher Development Department can record and analyze teachers' lessons and provide feedback on areas for improvement. Specifically, the Teacher Development Department uses AI to analyze lesson recordings and evaluate teachers' teaching skills and lesson delivery methods. This allows teachers to objectively understand the strengths and areas for improvement in their lessons and receive specific advice on how to improve their teaching skills. The Teacher Development Department can also evaluate teachers' teaching skills and propose training programs. For example, the Teacher Development Department can evaluate teachers' teaching skills and propose training programs tailored to their individual needs. This allows teachers to receive specific training to improve their skills and knowledge. Furthermore, the Teacher Development Department can collect teachers' teaching data to improve their teaching abilities. For example, the Teacher Development Department can analyze teachers' teaching data and provide information that helps improve teaching skills. This allows teachers to continuously improve their teaching skills and maximize the learning effectiveness for students. The Teacher Development Department can also provide a platform to promote information sharing and communication among teachers. For example, the Teacher Development Department holds online forums and workshops to provide a platform for teachers to exchange information on teaching methods and educational trends. This allows teachers to learn from the insights and experiences of other teachers and incorporate them into their own teaching. In this way, the Teacher Development Department can comprehensively support the professional development of teachers and improve the quality of education.

[0034] The Business Support Department can automate administrative tasks and manage schedules. For example, as part of administrative task automation, the Business Support Department can automate document creation. It can also automate data entry. Furthermore, as part of schedule management, the Business Support Department can manage class schedules. For example, the Business Support Department can automatically update class schedules and notify teachers. The Business Support Department can also manage meeting schedules. For example, the Business Support Department can automatically adjust meeting schedules and notify participants. This reduces the burden on teachers through the automation of administrative tasks and schedule management. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not using AI. For example, the Business Support Department can have a generative AI perform the automation of document creation.

[0035] The Classroom Support Unit can provide real-time assistance during lessons and analyze student comprehension. For example, as real-time assistance during lessons, the Classroom Support Unit can immediately respond to student questions during lessons. It can also support the progress of the lesson. Furthermore, as student comprehension analysis, the Classroom Support Unit can analyze test results. For example, it can evaluate student comprehension based on test results and adjust the progress of the lesson. The Classroom Support Unit can also analyze student reactions during lessons to grasp their comprehension. For example, it can analyze students' facial expressions and statements to evaluate their comprehension. In this way, real-time assistance during lessons and analysis of student comprehension can support effective lesson management. Some or all of the above processes in the Classroom Support Unit may be performed using AI, for example, or not. For example, the Classroom Support Unit can have a generating AI produce answers to student questions.

[0036] The Curriculum Development Department can analyze the latest educational trends and student learning data to propose optimal curricula. For example, as part of its analysis of the latest educational trends, the Curriculum Development Department can analyze the latest trends in teaching technologies and teaching methods. Furthermore, as part of its analysis of student learning data, the Curriculum Development Department can analyze test results and learning history. In addition, as part of its proposal of optimal curricula, the Curriculum Development Department can create curricula tailored to each student's learning progress and interests. For example, based on student learning data, the Curriculum Development Department can create individualized instruction curricula. Also, based on educational trends, the Curriculum Development Department can propose curricula that incorporate the latest teaching methods. In this way, by analyzing the latest educational trends and student learning data, the Curriculum Development Department can propose optimal curricula. Some or all of the above processes in the Curriculum Development Department may be performed using AI, for example, or not. For example, the Curriculum Development Department can have a generative AI perform the analysis of educational trends.

[0037] The Teacher Development Department can support the improvement of teachers' professionalism through the evaluation of teaching skills and the proposal of training programs. For example, as part of evaluating teaching skills, the Teacher Development Department can record and analyze teachers' lessons and provide feedback on areas for improvement. It can also conduct lesson observations and evaluate teaching skills. Furthermore, as part of training program proposals, the Teacher Development Department can propose workshops and online courses. For example, it can propose training programs tailored to teachers' teaching skills. It can also propose optimal training programs based on teachers' teaching data. This allows the Teacher Development Department to support the improvement of teachers' professionalism through the evaluation of teaching skills and the proposal of training programs. Some or all of the above processes in the Teacher Development Department may be performed using AI, for example, or not. For example, the Teacher Development Department can have a generative AI perform the evaluation of teaching skills.

[0038] The Business Support Department can analyze teachers' past work history and select the most suitable method for automating administrative tasks. For example, the Business Support Department can automate administrative tasks that teachers frequently performed in the past. It can also prioritize the automation of administrative tasks that teachers previously spent a lot of time on. Furthermore, it can automate administrative tasks that teachers previously found difficult. In this way, by analyzing teachers' past work history, the most suitable method for automating administrative tasks can be selected. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the Business Support Department can input teachers' past work history data into a generating AI and have the generating AI select the most suitable method for automating administrative tasks.

[0039] The Business Support Department can automate administrative tasks and distribute them based on teachers' current workloads. For example, if a teacher is currently busy, the AI ​​can distribute some of the administrative tasks to other teachers. Conversely, if a teacher has free time, the AI ​​can assign a large portion of the administrative tasks to that teacher. Furthermore, if a teacher is currently focused on a specific project, the AI ​​can prioritize assigning administrative tasks related to that project. This allows for increased efficiency by distributing tasks based on teachers' current workloads. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not. For example, the Business Support Department can input teachers' current workload data into a generating AI and have the generating AI perform the task distribution.

[0040] The Business Support Department can prioritize the automation of highly relevant tasks based on teachers' geographical location information when automating administrative tasks. For example, if a teacher is at school, the Business Support Department can prioritize the automation of administrative tasks that can be performed at school. Similarly, if a teacher is at home, the Business Support Department can prioritize the automation of administrative tasks that can be performed at home. Furthermore, if a teacher is on a business trip, the Business Support Department can prioritize the automation of administrative tasks that can be performed at the destination. This allows for increased efficiency by prioritizing the automation of highly relevant tasks based on teachers' geographical location information. Some or all of the above-described processes in the Business Support Department may be performed using AI, or not. For example, the Business Support Department can input teachers' geographical location data into a generating AI and have the generating AI automate highly relevant tasks.

[0041] The Business Support Department can analyze teachers' social media activity and automate related tasks when automating administrative work. For example, the Business Support Department can automate administrative tasks related to the content that teachers frequently post on social media. It can also automate administrative tasks related to the accounts that teachers follow on social media. Furthermore, the Business Support Department can automate administrative tasks related to the groups that teachers participate in on social media. In this way, by analyzing teachers' social media activity, related tasks can be automated. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not using AI. For example, the Business Support Department can input teachers' social media activity data into a generating AI and have the generating AI perform the automation of related tasks.

[0042] The teaching support unit can analyze students' comprehension levels during class and select the optimal teaching method. For example, if students' comprehension levels are low, the AI ​​can slow down the pace of the lesson. Conversely, if students' comprehension levels are high, the AI ​​can speed up the pace of the lesson. Furthermore, if students' comprehension levels vary, the AI ​​can suggest individualized instruction. In this way, the optimal teaching method can be selected by analyzing students' comprehension levels. Some or all of the above processes in the teaching support unit may be performed using AI, for example, or without AI. For example, the teaching support unit can input student comprehension data into a generating AI and have the generating AI select the teaching method.

[0043] The teaching support unit can customize the content of assistance provided in real time during lessons based on the student's past learning history. For example, the teaching support unit can focus assistance on topics that the student has struggled with in the past. It can also simplify and assist with topics that the student has excelled at in the past. Furthermore, the teaching support unit can assist with reviewing content that the student has previously learned. In this way, by customizing the content of assistance based on the student's past learning history, it is possible to support effective lesson management. Some or all of the above processes in the teaching support unit may be performed using AI, for example, or without AI. For example, the teaching support unit can input the student's past learning history data into a generating AI and have the generating AI perform the customization of the assistance content.

[0044] The Classroom Support Unit can provide highly relevant assistance during real-time classroom instruction based on students' geographical location information. For example, if a student is at school, the AI ​​can provide assistance within the school. If a student is at home, the AI ​​can provide assistance at home. Furthermore, if a student is on a business trip, the AI ​​can provide assistance at their destination. This allows for the provision of highly relevant assistance based on students' geographical location information, thereby supporting effective classroom management. Some or all of the above-described processes in the Classroom Support Unit may be performed using AI, or not. For example, the Classroom Support Unit can input students' geographical location data into a generating AI and have the generating AI provide highly relevant assistance.

[0045] The Classroom Support Department can analyze students' social media activity during real-time assistance in class and provide relevant assistance. For example, the Classroom Support Department can provide assistance related to the content that students frequently post on social media. It can also provide assistance related to the accounts that students follow on social media. Furthermore, the Classroom Support Department can provide assistance related to the groups that students participate in on social media. In this way, relevant assistance can be provided by analyzing students' social media activity. Some or all of the above processing in the Classroom Support Department may be performed using AI, for example, or not using AI. For example, the Classroom Support Department can input students' social media activity data into a generating AI and have the generating AI perform the provision of relevant assistance.

[0046] The Curriculum Development Department can analyze the latest educational trends and select the optimal method for proposing a curriculum. For example, the Curriculum Development Department can use AI to update curriculum content based on the latest educational trends. Furthermore, the Curriculum Development Department can use AI to suggest new teaching materials based on educational trends. In addition, the Curriculum Development Department can use AI to suggest lesson progression methods based on educational trends. This allows for the proposal of an optimal curriculum by analyzing the latest educational trends. Some or all of the above processes in the Curriculum Development Department may be performed using AI, for example, or without AI. For example, the Curriculum Development Department can have a generative AI perform the analysis of educational trends.

[0047] The Curriculum Development Department can analyze students' learning data and create curricula optimized for individualized instruction. For example, the Curriculum Development Department can use AI to create individualized instruction curricula based on students' learning data. Furthermore, the Curriculum Development Department can use AI to create curricula tailored to students' learning progress based on their learning data. In addition, the Curriculum Development Department can use AI to create curricula tailored to students' interests and preferences based on their learning data. This allows for the creation of curricula optimized for individualized instruction by analyzing students' learning data. Some or all of the above processes in the Curriculum Development Department may be performed using AI, or not. For example, the Curriculum Development Department can input student learning data into a generating AI and have the generating AI create a curriculum optimized for individualized instruction.

[0048] The curriculum development department can propose highly relevant curricula based on students' geographical location information during curriculum development. For example, the curriculum development department can incorporate content related to the area where students live into the curriculum. It can also propose curricula tailored to the characteristics of the school students attend. Furthermore, the curriculum development department can incorporate content related to community activities in which students participate into the curriculum. This enables individualized instruction by proposing highly relevant curricula based on students' geographical location information. Some or all of the above processes in the curriculum development department may be performed using AI, for example, or not. For example, the curriculum development department can input students' geographical location data into a generating AI and have the generating AI propose highly relevant curricula.

[0049] The curriculum development department can analyze students' social media activity during curriculum development and propose relevant curricula. For example, the curriculum development department can propose curricula related to the content that students frequently post on social media. It can also propose curricula related to the accounts that students follow on social media. Furthermore, the curriculum development department can propose curricula related to the groups that students participate in on social media. In this way, relevant curricula can be proposed by analyzing students' social media activity. Some or all of the above processes in the curriculum development department may be performed using AI, for example, or not using AI. For example, the curriculum development department can input students' social media activity data into a generating AI and have the generating AI generate suggestions for relevant curricula.

[0050] The Teacher Development Department can analyze a teacher's past teaching history and propose the most suitable training program. For example, the Teacher Development Department can propose a training program focusing on teaching methods that the teacher has struggled with in the past. It can also propose a training program based on teaching methods that the teacher has been successful with in the past. Furthermore, the Teacher Development Department can analyze the effectiveness of training programs that the teacher has previously participated in and propose the most suitable program. In this way, by analyzing a teacher's past teaching history, the most suitable training program can be proposed. Some or all of the above processes in the Teacher Development Department may be performed using AI, for example, or not. For example, the Teacher Development Department can input the teacher's past teaching history data into a generating AI and have the generating AI propose the most suitable training program.

[0051] The Teacher Development Department can customize the evaluation content when assessing teaching skills based on the teacher's current teaching situation. For example, the Teacher Development Department can customize the evaluation content based on the characteristics of the class the teacher is currently in charge of. It can also customize the evaluation content based on the teaching materials the teacher is currently using. Furthermore, the Teacher Development Department can customize the evaluation content based on the teaching challenges the teacher is currently facing. This allows for the improvement of teachers' professionalism by customizing the evaluation content based on their current teaching situation. Some or all of the above processes in the Teacher Development Department may be performed using AI, for example, or not using AI. For example, the Teacher Development Department can input data on the teacher's current teaching situation into a generating AI and have the generating AI perform the customization of the evaluation content.

[0052] The Teacher Development Department can conduct highly relevant assessments of teaching skills based on teachers' geographical location information. For example, if a teacher is located in an urban area, the Department can conduct assessments appropriate to the urban educational environment. Furthermore, if a teacher is located in a rural area, the Department can conduct assessments appropriate to the rural educational environment. Additionally, if a teacher is overseas, the Department can conduct assessments appropriate to the educational environment of that country. This allows for the improvement of teachers' professional skills by conducting highly relevant assessments based on their geographical location information. Some or all of the above-described processes in the Teacher Development Department may be performed using AI, for example, or without AI. For instance, the Teacher Development Department can input teachers' geographical location data into a generating AI and have the AI ​​perform highly relevant assessments.

[0053] The Teacher Development Department can analyze teachers' social media activity and conduct relevant assessments when evaluating teaching skills. For example, the Teacher Development Department can conduct assessments related to the content teachers frequently post on social media. It can also conduct assessments related to the accounts teachers follow on social media. Furthermore, the Teacher Development Department can conduct assessments related to the groups teachers participate in on social media. In this way, relevant assessments can be conducted by analyzing teachers' social media activity. Some or all of the above processes in the Teacher Development Department may be performed using AI, for example, or not. For example, the Teacher Development Department can input teachers' social media activity data into a generating AI and have the generating AI perform relevant assessments.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] AI agent systems for education can analyze teachers' past lesson data and suggest optimal teaching support methods. For example, the teaching support department can provide assistance based on teaching methods that have been successful in the past. It can also help teachers improve teaching methods they have struggled with in the past. Furthermore, it can suggest areas for improvement in lessons based on feedback from teachers' past lessons. In this way, by analyzing teachers' past lesson data, the system can suggest the most suitable teaching support methods.

[0056] AI agent systems for education can provide classroom support based on teachers' geographical location information. For example, if a teacher is on-campus, the AI ​​can provide classroom support within the school. If the teacher is at home, the AI ​​can provide support for online classes. Furthermore, if the teacher is traveling, the AI ​​can provide classroom support at their destination. In this way, by providing classroom support based on teachers' geographical location information, it is possible to support effective classroom management.

[0057] An AI agent system for education can analyze teachers' past teaching data and propose optimal methods for evaluating their teaching skills. For example, a teacher training department can evaluate teachers based on their past successful teaching methods. It can also evaluate teachers to improve teaching methods they have struggled with in the past. Furthermore, it can suggest areas for improvement in teaching skills based on feedback on teachers' past teaching. In this way, by analyzing teachers' past teaching data, it is possible to propose optimal methods for evaluating their teaching skills.

[0058] AI agent systems for education can analyze teachers' social media activity and provide related support. For example, the support department can automate administrative tasks related to the content teachers frequently post on social media. It can also automate administrative tasks related to the accounts teachers follow on social media. Furthermore, it can automate administrative tasks related to the groups teachers participate in on social media. This allows for relevant support by analyzing teachers' social media activity.

[0059] AI agent systems for education can provide work support based on teachers' geographical location information. For example, if a teacher is at school, the AI ​​can prioritize automating administrative tasks that can be performed within the school. If the teacher is at home, the AI ​​can prioritize automating administrative tasks that can be performed at home. Furthermore, if the teacher is on a business trip, the AI ​​can prioritize automating administrative tasks that can be performed at the destination. In this way, work efficiency can be improved by providing work support based on teachers' geographical location information.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The Operations Support Department will support teachers' work. Specifically, it will automate administrative tasks and schedule management, automating routine work such as attendance management and grade entry. It will also manage teachers' schedules so that they can concentrate on lesson preparation and student guidance. Furthermore, it will collect teachers' work data, analyze it to improve work efficiency, and determine task priorities. Step 2: The Classroom Support Department provides real-time assistance during lessons and analyzes student comprehension. Specifically, it responds immediately to student questions during lessons, grasps student comprehension in real time, and adjusts the lesson progress accordingly. It also collects lesson data, analyzes the effectiveness of lessons, and analyzes student reactions during lessons to suggest areas for improvement. Step 3: The Curriculum Development Department analyzes the latest educational trends and student learning data to propose the optimal curriculum. Specifically, they create curricula tailored to each student's learning pace and interests, and propose curricula that incorporate the latest teaching methods based on an analysis of educational trends. They also analyze student learning data to create curricula optimized for individualized instruction. Step 4: The Teacher Development Department supports the professional development of teachers through the evaluation of teaching skills and the proposal of training programs. Specifically, it records and analyzes teachers' lessons and provides feedback on areas for improvement. It also evaluates teachers' teaching skills and proposes training programs. Furthermore, it collects teachers' teaching data, analyzes it to improve teaching ability, and provides information useful for improving teaching skills.

[0062] (Example of form 2) The AI ​​agent system for education according to an embodiment of the present invention is a system that uses robots active in educational settings as its icon. This system utilizes multiple AI agents to comprehensively support teachers in improving work efficiency, assisting with lessons, developing curricula, and training teachers. This improves the quality of education and contributes to solving the problem of teacher shortages. For example, the work support agent automates administrative tasks and manages schedules, reducing the burden on teachers. For example, by automating routine tasks such as attendance management and grade entry, teachers can concentrate on lesson preparation and student guidance. Next, the lesson support agent provides real-time assistance during lessons and analyzes students' comprehension levels, supporting effective lesson management. For example, it can immediately respond to students' questions during lessons and adjust the progress of lessons by grasping students' comprehension levels in real time. Furthermore, the curriculum development agent analyzes the latest educational trends and student learning data and proposes the optimal curriculum. For example, it creates curricula tailored to each student's learning progress and interests, realizing individualized instruction. Finally, the teacher training agent supports the improvement of teachers' professionalism through evaluation of teaching skills and proposal of training programs. For example, by recording and analyzing teachers' lessons and providing feedback on areas for improvement, teachers' instructional skills can be enhanced. This system improves the quality of education, solves the problem of teacher shortages, and creates a sustainable educational environment. By utilizing AI agents, the burden on teachers is reduced and the quality of education is improved, contributing to the development of human resources that will support Japan's future. In this way, AI agent systems for education can improve the quality of education by streamlining teachers' work and comprehensively supporting lesson support, curriculum development, and teacher training.

[0063] The AI ​​agent system for education according to this embodiment comprises a business support unit, a lesson support unit, a curriculum development unit, and a teacher training unit. The business support unit supports teachers' work. For example, the business support unit automates administrative tasks and manages schedules. For example, the business support unit can automate routine tasks such as attendance management and grade entry. The business support unit can also manage teachers' schedules, allowing them to concentrate on lesson preparation and student guidance. Furthermore, the business support unit can collect teachers' work data and improve work efficiency. For example, the business support unit can analyze teachers' work data and determine task priorities. The lesson support unit provides real-time assistance during lessons and analysis of student comprehension. For example, the lesson support unit can immediately respond to student questions during lessons. Furthermore, the lesson support unit can grasp students' comprehension in real time and adjust the progress of the lesson. Furthermore, the lesson support unit can collect lesson data and analyze the effectiveness of lessons. For example, the lesson support unit can analyze student reactions during lessons and suggest areas for improvement in the lesson. The Curriculum Development Department analyzes the latest educational trends and student learning data to propose optimal curricula. For example, the Curriculum Development Department can create curricula tailored to each student's learning pace and interests. It can also analyze educational trends and propose curricula that incorporate the latest teaching methods. Furthermore, the Curriculum Development Department can analyze student learning data and create curricula optimized for individualized instruction. For example, it can create individualized instruction curricula based on student learning data. The Teacher Development Department supports the improvement of teachers' professionalism through the evaluation of teaching skills and the proposal of training programs. For example, the Teacher Development Department can record and analyze teachers' lessons and provide feedback on areas for improvement. It can also evaluate teachers' teaching skills and propose training programs. Furthermore, the Teacher Development Department can collect teachers' teaching data to improve their teaching abilities. For example, the Teacher Development Department can analyze teachers' teaching data and provide information that helps improve teaching skills.As a result, the AI ​​agent system for education according to this embodiment can streamline teachers' work and comprehensively support classroom instruction, curriculum development, and teacher training, thereby improving the quality of education.

[0064] The Business Support Department assists teachers with their work. For example, it automates administrative tasks and manages schedules. Specifically, it can automate routine tasks such as attendance management and grade entry. This frees teachers from cumbersome manual administrative work, allowing them to dedicate more time to lesson preparation and student guidance. The Business Support Department can also manage teachers' schedules, enabling them to focus on lesson preparation and student guidance. For example, it can automatically adjust teachers' schedules to ensure time for important meetings and lesson preparation. Furthermore, the Business Support Department can collect teachers' work data to improve efficiency. For example, it can analyze teachers' work data to determine task priorities. This allows teachers to work more efficiently and reduce stress. In addition, the Business Support Department can use AI to learn teachers' work patterns and propose optimal workflows. For example, based on past work data, the AI ​​can suggest which tasks teachers should perform at what times, thereby improving efficiency. Furthermore, the Business Support Department can provide reminder and task management functions to reduce the workload of teachers. This allows teachers to avoid forgetting important tasks and to proceed with their work systematically.

[0065] The Classroom Support Department provides real-time assistance during lessons and analysis of student comprehension. For example, the Classroom Support Department can instantly respond to student questions during lessons. Specifically, it uses AI to analyze student questions and provide appropriate answers. This allows teachers to respond quickly to student questions during lessons and ensure smooth lesson progress. The Classroom Support Department can also grasp student comprehension in real time and adjust the lesson pace accordingly. For example, it analyzes students' facial expressions and reactions to determine whether students understand the material. If comprehension is low, the department can slow down the lesson or provide supplementary explanations. Furthermore, the Classroom Support Department can collect lesson data and analyze the effectiveness of lessons. For example, it can analyze student reactions during lessons and suggest areas for improvement. This allows teachers to receive specific advice to improve the quality of their lessons. The Classroom Support Department can also provide advice for individualized instruction based on student learning data. For example, it can analyze students' learning progress and comprehension to suggest the optimal approach for individualized instruction. This allows teachers to provide individualized instruction to each student, maximizing their learning effectiveness.

[0066] The Curriculum Development Department analyzes the latest educational trends and student learning data to propose optimal curricula. For example, it can create curricula tailored to each student's learning pace and interests. Specifically, it uses AI to analyze student learning data and propose curricula that meet individual learning needs. This allows students to learn at their own pace and enhance learning effectiveness. The Curriculum Development Department can also analyze educational trends and propose curricula that incorporate the latest teaching methods. For example, it creates and provides curricula that incorporate the latest educational research and technologies. This allows students to acquire the latest knowledge and skills, which will benefit their future careers. Furthermore, the Curriculum Development Department can analyze student learning data and create curricula optimized for individualized instruction. For example, it can create individualized instruction curricula based on student learning data. This allows students to receive instruction tailored to their learning style and pace, maximizing learning effectiveness. The Curriculum Development Department can also collaborate with teachers to improve curricula. For example, it can review the content and progression of curricula based on teacher feedback to provide more effective curricula. This allows the curriculum development department to consistently provide the most up-to-date curriculum incorporating the latest information and technology, maximizing student learning effectiveness.

[0067] The Teacher Development Department supports the professional development of teachers through the evaluation of teaching skills and the proposal of training programs. For example, the Teacher Development Department can record and analyze teachers' lessons and provide feedback on areas for improvement. Specifically, the Teacher Development Department uses AI to analyze lesson recordings and evaluate teachers' teaching skills and lesson delivery methods. This allows teachers to objectively understand the strengths and areas for improvement in their lessons and receive specific advice on how to improve their teaching skills. The Teacher Development Department can also evaluate teachers' teaching skills and propose training programs. For example, the Teacher Development Department can evaluate teachers' teaching skills and propose training programs tailored to their individual needs. This allows teachers to receive specific training to improve their skills and knowledge. Furthermore, the Teacher Development Department can collect teachers' teaching data to improve their teaching abilities. For example, the Teacher Development Department can analyze teachers' teaching data and provide information that helps improve teaching skills. This allows teachers to continuously improve their teaching skills and maximize the learning effectiveness for students. The Teacher Development Department can also provide a platform to promote information sharing and communication among teachers. For example, the Teacher Development Department holds online forums and workshops to provide a platform for teachers to exchange information on teaching methods and educational trends. This allows teachers to learn from the insights and experiences of other teachers and incorporate them into their own teaching. In this way, the Teacher Development Department can comprehensively support the professional development of teachers and improve the quality of education.

[0068] The Business Support Department can automate administrative tasks and manage schedules. For example, as part of administrative task automation, the Business Support Department can automate document creation. It can also automate data entry. Furthermore, as part of schedule management, the Business Support Department can manage class schedules. For example, the Business Support Department can automatically update class schedules and notify teachers. The Business Support Department can also manage meeting schedules. For example, the Business Support Department can automatically adjust meeting schedules and notify participants. This reduces the burden on teachers through the automation of administrative tasks and schedule management. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not using AI. For example, the Business Support Department can have a generative AI perform the automation of document creation.

[0069] The Classroom Support Unit can provide real-time assistance during lessons and analyze student comprehension. For example, as real-time assistance during lessons, the Classroom Support Unit can immediately respond to student questions during lessons. It can also support the progress of the lesson. Furthermore, as student comprehension analysis, the Classroom Support Unit can analyze test results. For example, it can evaluate student comprehension based on test results and adjust the progress of the lesson. The Classroom Support Unit can also analyze student reactions during lessons to grasp their comprehension. For example, it can analyze students' facial expressions and statements to evaluate their comprehension. In this way, real-time assistance during lessons and analysis of student comprehension can support effective lesson management. Some or all of the above processes in the Classroom Support Unit may be performed using AI, for example, or not. For example, the Classroom Support Unit can have a generating AI produce answers to student questions.

[0070] The Curriculum Development Department can analyze the latest educational trends and student learning data to propose optimal curricula. For example, as part of its analysis of the latest educational trends, the Curriculum Development Department can analyze the latest trends in teaching technologies and teaching methods. Furthermore, as part of its analysis of student learning data, the Curriculum Development Department can analyze test results and learning history. In addition, as part of its proposal of optimal curricula, the Curriculum Development Department can create curricula tailored to each student's learning progress and interests. For example, based on student learning data, the Curriculum Development Department can create individualized instruction curricula. Also, based on educational trends, the Curriculum Development Department can propose curricula that incorporate the latest teaching methods. In this way, by analyzing the latest educational trends and student learning data, the Curriculum Development Department can propose optimal curricula. Some or all of the above processes in the Curriculum Development Department may be performed using AI, for example, or not. For example, the Curriculum Development Department can have a generative AI perform the analysis of educational trends.

[0071] The Teacher Development Department can support the improvement of teachers' professionalism through the evaluation of teaching skills and the proposal of training programs. For example, as part of evaluating teaching skills, the Teacher Development Department can record and analyze teachers' lessons and provide feedback on areas for improvement. It can also conduct lesson observations and evaluate teaching skills. Furthermore, as part of training program proposals, the Teacher Development Department can propose workshops and online courses. For example, it can propose training programs tailored to teachers' teaching skills. It can also propose optimal training programs based on teachers' teaching data. This allows the Teacher Development Department to support the improvement of teachers' professionalism through the evaluation of teaching skills and the proposal of training programs. Some or all of the above processes in the Teacher Development Department may be performed using AI, for example, or not. For example, the Teacher Development Department can have a generative AI perform the evaluation of teaching skills.

[0072] The business support department can estimate teachers' emotions and adjust the priority of administrative tasks based on the estimated emotions. For example, if a teacher is stressed, the business support department can have the AI ​​prioritize assigning the easiest administrative tasks. If a teacher is relaxed, the business support department can have the AI ​​prioritize assigning more complex administrative tasks. Furthermore, if a teacher is tired, the business support department can have the AI ​​prioritize assigning the least time-consuming administrative tasks. This reduces the burden on teachers by adjusting the priority of administrative tasks based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the business support department may be performed using AI or not. For example, the business support department can input teacher emotion data into a generative AI and have the generative AI adjust the priority of administrative tasks.

[0073] The Business Support Department can analyze teachers' past work history and select the most suitable method for automating administrative tasks. For example, the Business Support Department can automate administrative tasks that teachers frequently performed in the past. It can also prioritize the automation of administrative tasks that teachers previously spent a lot of time on. Furthermore, it can automate administrative tasks that teachers previously found difficult. In this way, by analyzing teachers' past work history, the most suitable method for automating administrative tasks can be selected. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or without AI. For example, the Business Support Department can input teachers' past work history data into a generating AI and have the generating AI select the most suitable method for automating administrative tasks.

[0074] The Business Support Department can automate administrative tasks and distribute them based on teachers' current workloads. For example, if a teacher is currently busy, the AI ​​can distribute some of the administrative tasks to other teachers. Conversely, if a teacher has free time, the AI ​​can assign a large portion of the administrative tasks to that teacher. Furthermore, if a teacher is currently focused on a specific project, the AI ​​can prioritize assigning administrative tasks related to that project. This allows for increased efficiency by distributing tasks based on teachers' current workloads. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not. For example, the Business Support Department can input teachers' current workload data into a generating AI and have the generating AI perform the task distribution.

[0075] The Business Support Department can estimate teachers' emotions and adjust schedule management methods based on the estimated emotions. For example, if a teacher is stressed, the Business Support Department can use AI to loosely adjust the schedule. Conversely, if a teacher is relaxed, the Business Support Department can use AI to strictly manage the schedule. Furthermore, if a teacher is tired, the Business Support Department can use AI to adjust the schedule to allow for more breaks. In this way, the burden on teachers can be reduced by adjusting schedule management methods based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Business Support Department may be performed using AI, for example, or not using AI. For example, the Business Support Department can input teacher emotion data into a generative AI and have the generative AI perform schedule management adjustments.

[0076] The Business Support Department can prioritize the automation of highly relevant tasks based on teachers' geographical location information when automating administrative tasks. For example, if a teacher is at school, the Business Support Department can prioritize the automation of administrative tasks that can be performed at school. Similarly, if a teacher is at home, the Business Support Department can prioritize the automation of administrative tasks that can be performed at home. Furthermore, if a teacher is on a business trip, the Business Support Department can prioritize the automation of administrative tasks that can be performed at the destination. This allows for increased efficiency by prioritizing the automation of highly relevant tasks based on teachers' geographical location information. Some or all of the above-described processes in the Business Support Department may be performed using AI, or not. For example, the Business Support Department can input teachers' geographical location data into a generating AI and have the generating AI automate highly relevant tasks.

[0077] The Business Support Department can analyze teachers' social media activity and automate related tasks when automating administrative work. For example, the Business Support Department can automate administrative tasks related to the content that teachers frequently post on social media. It can also automate administrative tasks related to the accounts that teachers follow on social media. Furthermore, the Business Support Department can automate administrative tasks related to the groups that teachers participate in on social media. In this way, by analyzing teachers' social media activity, related tasks can be automated. Some or all of the above processes in the Business Support Department may be performed using AI, for example, or not using AI. For example, the Business Support Department can input teachers' social media activity data into a generating AI and have the generating AI perform the automation of related tasks.

[0078] The classroom support unit can estimate students' emotions and adjust the content of real-time assistance based on the estimated emotions. For example, if a student is feeling anxious, the AI ​​can provide assistance using gentle language. Similarly, if a student is agitated, the AI ​​can provide assistance using calm language. Furthermore, if a student is tired, the AI ​​can provide assistance with simple content. This allows for effective classroom management by adjusting the content of real-time assistance based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the classroom support unit may be performed using AI or not. For example, the classroom support unit can input student emotion data into a generative AI and have the generative AI adjust the content of real-time assistance.

[0079] The teaching support unit can analyze students' comprehension levels during class and select the optimal teaching method. For example, if students' comprehension levels are low, the AI ​​can slow down the pace of the lesson. Conversely, if students' comprehension levels are high, the AI ​​can speed up the pace of the lesson. Furthermore, if students' comprehension levels vary, the AI ​​can suggest individualized instruction. In this way, the optimal teaching method can be selected by analyzing students' comprehension levels. Some or all of the above processes in the teaching support unit may be performed using AI, for example, or without AI. For example, the teaching support unit can input student comprehension data into a generating AI and have the generating AI select the teaching method.

[0080] The teaching support unit can customize the content of assistance provided in real time during lessons based on the student's past learning history. For example, the teaching support unit can focus assistance on topics that the student has struggled with in the past. It can also simplify and assist with topics that the student has excelled at in the past. Furthermore, the teaching support unit can assist with reviewing content that the student has previously learned. In this way, by customizing the content of assistance based on the student's past learning history, it is possible to support effective lesson management. Some or all of the above processes in the teaching support unit may be performed using AI, for example, or without AI. For example, the teaching support unit can input the student's past learning history data into a generating AI and have the generating AI perform the customization of the assistance content.

[0081] The teaching support unit can estimate students' emotions and adjust the comprehension analysis method based on the estimated emotions. For example, if a student is nervous, the teaching support unit can have the AI ​​ask questions to help them relax. If a student is relaxed, the teaching support unit can have the AI ​​ask more detailed questions. Furthermore, if a student is tired, the teaching support unit can have the AI ​​ask simple questions. This allows for effective lesson management by adjusting the comprehension analysis method based on students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the teaching support unit may be performed using AI or not. For example, the teaching support unit can input student emotion data into a generative AI and have the generative AI adjust the comprehension analysis method.

[0082] The Classroom Support Unit can provide highly relevant assistance during real-time classroom instruction based on students' geographical location information. For example, if a student is at school, the AI ​​can provide assistance within the school. If a student is at home, the AI ​​can provide assistance at home. Furthermore, if a student is on a business trip, the AI ​​can provide assistance at their destination. This allows for the provision of highly relevant assistance based on students' geographical location information, thereby supporting effective classroom management. Some or all of the above-described processes in the Classroom Support Unit may be performed using AI, or not. For example, the Classroom Support Unit can input students' geographical location data into a generating AI and have the generating AI provide highly relevant assistance.

[0083] The Classroom Support Department can analyze students' social media activity during real-time assistance in class and provide relevant assistance. For example, the Classroom Support Department can provide assistance related to the content that students frequently post on social media. It can also provide assistance related to the accounts that students follow on social media. Furthermore, the Classroom Support Department can provide assistance related to the groups that students participate in on social media. In this way, relevant assistance can be provided by analyzing students' social media activity. Some or all of the above processing in the Classroom Support Department may be performed using AI, for example, or not using AI. For example, the Classroom Support Department can input students' social media activity data into a generating AI and have the generating AI perform the provision of relevant assistance.

[0084] The curriculum development department can estimate students' emotions and adjust the curriculum content based on those estimated emotions. For example, if a student is interested in something, the AI ​​can add content related to that interest to the curriculum. The curriculum development department can also use the AI ​​to modify the curriculum content to engage students if they are bored. Furthermore, if a student is feeling anxious, the AI ​​can simplify the curriculum content. This allows for individualized instruction by adjusting the curriculum content based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the curriculum development department may be performed using AI or not. For example, the curriculum development department can input student emotion data into a generative AI and have the generative AI adjust the curriculum content.

[0085] The Curriculum Development Department can analyze the latest educational trends and select the optimal method for proposing a curriculum. For example, the Curriculum Development Department can use AI to update curriculum content based on the latest educational trends. Furthermore, the Curriculum Development Department can use AI to suggest new teaching materials based on educational trends. In addition, the Curriculum Development Department can use AI to suggest lesson progression methods based on educational trends. This allows for the proposal of an optimal curriculum by analyzing the latest educational trends. Some or all of the above processes in the Curriculum Development Department may be performed using AI, for example, or without AI. For example, the Curriculum Development Department can have a generative AI perform the analysis of educational trends.

[0086] The Curriculum Development Department can analyze students' learning data and create curricula optimized for individualized instruction. For example, the Curriculum Development Department can use AI to create individualized instruction curricula based on students' learning data. Furthermore, the Curriculum Development Department can use AI to create curricula tailored to students' learning progress based on their learning data. In addition, the Curriculum Development Department can use AI to create curricula tailored to students' interests and preferences based on their learning data. This allows for the creation of curricula optimized for individualized instruction by analyzing students' learning data. Some or all of the above processes in the Curriculum Development Department may be performed using AI, or not. For example, the Curriculum Development Department can input student learning data into a generating AI and have the generating AI create a curriculum optimized for individualized instruction.

[0087] The curriculum development department can estimate students' emotions and determine curriculum priorities based on those estimated emotions. For example, the curriculum development department can prioritize incorporating content that students are interested in into the curriculum. It can also postpone incorporating content that students find difficult. Furthermore, it can simplify content that students feel anxious about and incorporate it into the curriculum. This allows for individualized instruction by determining curriculum priorities based on students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the curriculum development department may be performed using AI or not. For example, the curriculum development department can input student emotion data into a generative AI and have the generative AI determine curriculum priorities.

[0088] The curriculum development department can propose highly relevant curricula based on students' geographical location information during curriculum development. For example, the curriculum development department can incorporate content related to the area where students live into the curriculum. It can also propose curricula tailored to the characteristics of the school students attend. Furthermore, the curriculum development department can incorporate content related to community activities in which students participate into the curriculum. This enables individualized instruction by proposing highly relevant curricula based on students' geographical location information. Some or all of the above processes in the curriculum development department may be performed using AI, for example, or not. For example, the curriculum development department can input students' geographical location data into a generating AI and have the generating AI propose highly relevant curricula.

[0089] The curriculum development department can analyze students' social media activity during curriculum development and propose relevant curricula. For example, the curriculum development department can propose curricula related to the content that students frequently post on social media. It can also propose curricula related to the accounts that students follow on social media. Furthermore, the curriculum development department can propose curricula related to the groups that students participate in on social media. In this way, relevant curricula can be proposed by analyzing students' social media activity. Some or all of the above processes in the curriculum development department may be performed using AI, for example, or not using AI. For example, the curriculum development department can input students' social media activity data into a generating AI and have the generating AI generate suggestions for relevant curricula.

[0090] The Teacher Training Department can estimate teachers' emotions and adjust the evaluation method for teaching skills based on the estimated emotions. For example, if a teacher is stressed, the AI ​​can provide an evaluation method to help them relax. If a teacher is relaxed, the AI ​​can provide a more detailed evaluation method. Furthermore, if a teacher is tired, the AI ​​can provide a simpler evaluation method. This allows for the improvement of teachers' professionalism by adjusting the evaluation method for teaching skills based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Teacher Training Department may be performed using AI or not. For example, the Teacher Training Department can input teacher emotion data into a generative AI and have the generative AI perform the adjustment of the evaluation method for teaching skills.

[0091] The Teacher Development Department can analyze a teacher's past teaching history and propose the most suitable training program. For example, the Teacher Development Department can propose a training program focusing on teaching methods that the teacher has struggled with in the past. It can also propose a training program based on teaching methods that the teacher has been successful with in the past. Furthermore, the Teacher Development Department can analyze the effectiveness of training programs that the teacher has previously participated in and propose the most suitable program. In this way, by analyzing a teacher's past teaching history, the most suitable training program can be proposed. Some or all of the above processes in the Teacher Development Department may be performed using AI, for example, or not. For example, the Teacher Development Department can input the teacher's past teaching history data into a generating AI and have the generating AI propose the most suitable training program.

[0092] The Teacher Development Department can customize the evaluation content when assessing teaching skills based on the teacher's current teaching situation. For example, the Teacher Development Department can customize the evaluation content based on the characteristics of the class the teacher is currently in charge of. It can also customize the evaluation content based on the teaching materials the teacher is currently using. Furthermore, the Teacher Development Department can customize the evaluation content based on the teaching challenges the teacher is currently facing. This allows for the improvement of teachers' professionalism by customizing the evaluation content based on their current teaching situation. Some or all of the above processes in the Teacher Development Department may be performed using AI, for example, or not using AI. For example, the Teacher Development Department can input data on the teacher's current teaching situation into a generating AI and have the generating AI perform the customization of the evaluation content.

[0093] The teacher training department can estimate teachers' emotions and adjust the content of training programs based on the estimated emotions. For example, if a teacher is stressed, the AI ​​can provide a training program to help them relax. If a teacher is relaxed, the AI ​​can provide a more detailed training program. Furthermore, if a teacher is tired, the AI ​​can provide a simpler training program. This allows the department to support the improvement of teachers' professionalism by adjusting the content of training programs based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the teacher training department may be performed using AI or not. For example, the teacher training department can input teacher emotion data into a generative AI and have the generative AI adjust the content of the training program.

[0094] The Teacher Development Department can conduct highly relevant assessments of teaching skills based on teachers' geographical location information. For example, if a teacher is located in an urban area, the Department can conduct assessments appropriate to the urban educational environment. Furthermore, if a teacher is located in a rural area, the Department can conduct assessments appropriate to the rural educational environment. Additionally, if a teacher is overseas, the Department can conduct assessments appropriate to the educational environment of that country. This allows for the improvement of teachers' professional skills by conducting highly relevant assessments based on their geographical location information. Some or all of the above-described processes in the Teacher Development Department may be performed using AI, for example, or without AI. For instance, the Teacher Development Department can input teachers' geographical location data into a generating AI and have the AI ​​perform highly relevant assessments.

[0095] The Teacher Development Department can analyze teachers' social media activity and conduct relevant assessments when evaluating teaching skills. For example, the Teacher Development Department can conduct assessments related to the content teachers frequently post on social media. It can also conduct assessments related to the accounts teachers follow on social media. Furthermore, the Teacher Development Department can conduct assessments related to the groups teachers participate in on social media. In this way, relevant assessments can be conducted by analyzing teachers' social media activity. Some or all of the above processes in the Teacher Development Department may be performed using AI, for example, or not. For example, the Teacher Development Department can input teachers' social media activity data into a generating AI and have the generating AI perform relevant assessments.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] AI agent systems for education can estimate teachers' emotions and provide classroom support based on those estimates. For example, if a teacher is feeling stressed, the AI ​​can support the lesson and reduce the teacher's burden. If the teacher is relaxed, the AI ​​can take over the lesson, respecting the teacher's autonomy. Furthermore, if the teacher is tired, the AI ​​can take over part of the lesson, ensuring the teacher gets some rest. In this way, providing classroom support based on the teacher's emotions can support effective lesson management.

[0098] AI agent systems for education can analyze teachers' past lesson data and suggest optimal teaching support methods. For example, the teaching support department can provide assistance based on teaching methods that have been successful in the past. It can also help teachers improve teaching methods they have struggled with in the past. Furthermore, it can suggest areas for improvement in lessons based on feedback from teachers' past lessons. In this way, by analyzing teachers' past lesson data, the system can suggest the most suitable teaching support methods.

[0099] An AI agent system for education can estimate teachers' emotions and develop curricula based on those estimates. For example, if a teacher is excited, the curriculum development department can have the AI ​​suggest a curriculum incorporating new teaching methods. If a teacher is feeling anxious, the AI ​​can suggest a simple and easy-to-understand curriculum. Furthermore, if a teacher is relaxed, the AI ​​can suggest a challenging curriculum. By developing curricula based on teachers' emotions, effective education can be achieved.

[0100] AI agent systems for education can provide classroom support based on teachers' geographical location information. For example, if a teacher is on-campus, the AI ​​can provide classroom support within the school. If the teacher is at home, the AI ​​can provide support for online classes. Furthermore, if the teacher is traveling, the AI ​​can provide classroom support at their destination. In this way, by providing classroom support based on teachers' geographical location information, it is possible to support effective classroom management.

[0101] An AI agent system for education can estimate teachers' emotions and provide teacher training based on those estimates. For example, if a teacher is feeling stressed, the AI ​​can provide a training program to help them relax. If the teacher is relaxed, the AI ​​can provide a more detailed training program. Furthermore, if the teacher is tired, the AI ​​can provide a simpler training program. In this way, by providing teacher training based on teachers' emotions, it is possible to support the improvement of teachers' professionalism.

[0102] An AI agent system for education can analyze teachers' past teaching data and propose optimal methods for evaluating their teaching skills. For example, a teacher training department can evaluate teachers based on their past successful teaching methods. It can also evaluate teachers to improve teaching methods they have struggled with in the past. Furthermore, it can suggest areas for improvement in teaching skills based on feedback on teachers' past teaching. In this way, by analyzing teachers' past teaching data, it is possible to propose optimal methods for evaluating their teaching skills.

[0103] AI agent systems for education can estimate teachers' emotions and provide work support based on those estimates. For example, if a teacher is stressed, the AI ​​can prioritize assigning the easiest administrative tasks. If the teacher is relaxed, the AI ​​can prioritize assigning more complex administrative tasks. Furthermore, if the teacher is tired, the AI ​​can prioritize assigning the least time-consuming administrative tasks. By providing work support based on teachers' emotions, the burden on teachers can be reduced.

[0104] AI agent systems for education can analyze teachers' social media activity and provide related support. For example, the support department can automate administrative tasks related to the content teachers frequently post on social media. It can also automate administrative tasks related to the accounts teachers follow on social media. Furthermore, it can automate administrative tasks related to the groups teachers participate in on social media. This allows for relevant support by analyzing teachers' social media activity.

[0105] An AI agent system for education can estimate teachers' emotions and manage their schedules based on those estimates. For example, if a teacher is feeling stressed, the AI ​​can loosely adjust their schedule. Conversely, if a teacher is relaxed, the AI ​​can strictly manage their schedule. Furthermore, if a teacher is tired, the AI ​​can adjust their schedule to allow for more breaks. By managing schedules based on teachers' emotions, the system can reduce their workload.

[0106] AI agent systems for education can provide work support based on teachers' geographical location information. For example, if a teacher is at school, the AI ​​can prioritize automating administrative tasks that can be performed within the school. If the teacher is at home, the AI ​​can prioritize automating administrative tasks that can be performed at home. Furthermore, if the teacher is on a business trip, the AI ​​can prioritize automating administrative tasks that can be performed at the destination. In this way, work efficiency can be improved by providing work support based on teachers' geographical location information.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The Operations Support Department will support teachers' work. Specifically, it will automate administrative tasks and schedule management, automating routine work such as attendance management and grade entry. It will also manage teachers' schedules so that they can concentrate on lesson preparation and student guidance. Furthermore, it will collect teachers' work data, analyze it to improve work efficiency, and determine task priorities. Step 2: The Classroom Support Department provides real-time assistance during lessons and analyzes student comprehension. Specifically, it responds immediately to student questions during lessons, grasps student comprehension in real time, and adjusts the lesson progress accordingly. It also collects lesson data, analyzes the effectiveness of lessons, and analyzes student reactions during lessons to suggest areas for improvement. Step 3: The Curriculum Development Department analyzes the latest educational trends and student learning data to propose the optimal curriculum. Specifically, they create curricula tailored to each student's learning pace and interests, and propose curricula that incorporate the latest teaching methods based on an analysis of educational trends. They also analyze student learning data to create curricula optimized for individualized instruction. Step 4: The Teacher Development Department supports the professional development of teachers through the evaluation of teaching skills and the proposal of training programs. Specifically, it records and analyzes teachers' lessons and provides feedback on areas for improvement. It also evaluates teachers' teaching skills and proposes training programs. Furthermore, it collects teachers' teaching data, analyzes it to improve teaching ability, and provides information useful for improving teaching skills.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements mentioned above, including the business support department, lesson support department, curriculum development department, and teacher training department, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the business support department is implemented by the control unit 46A of the smart device 14 and automates administrative tasks and manages schedules. The lesson support department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and provides real-time assistance during lessons and analysis of students' comprehension. The curriculum development department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the latest educational trends and student learning data to propose an optimal curriculum. The teacher training department is implemented by, for example, the control unit 46A of the smart device 14 and supports the improvement of teachers' professionalism through evaluation of teaching skills and proposal of training programs. The correspondence between each department and the devices and control units is not limited to the examples given above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements mentioned above, including the business support unit, lesson support unit, curriculum development unit, and teacher training unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the business support unit is implemented by the control unit 46A of the smart glasses 214 and automates administrative tasks and manages schedules. The lesson support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time assistance during lessons and analysis of students' comprehension. The curriculum development unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the latest educational trends and student learning data to propose an optimal curriculum. The teacher training unit is implemented by the control unit 46A of the smart glasses 214 and supports the improvement of teachers' professionalism through evaluation of teaching skills and proposal of training programs. The correspondence between each unit and the devices and control units is not limited to the examples above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the business support department, lesson support department, curriculum development department, and teacher training department, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the business support department is implemented by the control unit 46A of the headset terminal 314 and performs automation of administrative tasks and schedule management. The lesson support department is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time assistance during lessons and analysis of students' comprehension. The curriculum development department is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the latest educational trends and student learning data to propose an optimal curriculum. The teacher training department is implemented by the control unit 46A of the headset terminal 314 and supports the improvement of teachers' professionalism through evaluation of teaching skills and proposal of training programs. The correspondence between each department and the devices and control units is not limited to the examples described above and can be changed in various ways.

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

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

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements mentioned above, including the business support unit, lesson support unit, curriculum development unit, and teacher training unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the business support unit is implemented by the control unit 46A of the robot 414 and automates administrative tasks and manages schedules. The lesson support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides real-time assistance during lessons and analysis of students' comprehension. The curriculum development unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the latest educational trends and student learning data to propose an optimal curriculum. The teacher training unit is implemented by, for example, the control unit 46A of the robot 414 and supports the improvement of teachers' professionalism through evaluation of teaching skills and proposal of training programs. The correspondence between each unit and the devices and control units is not limited to the examples above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The Business Support Department assists teachers with their duties, The teaching support department provides support for classes based on the business data supported by the aforementioned business support department, The Curriculum Development Department develops a curriculum based on the lesson data supported by the aforementioned Lesson Support Department, The system comprises a teacher training department that trains teachers based on the curriculum developed by the aforementioned curriculum development department. A system characterized by the following features. (Note 2) The aforementioned Business Support Department, Automate administrative tasks and manage schedules. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned teaching support department, It provides real-time assistance during lessons and analysis of students' comprehension levels. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned curriculum development department, We analyze the latest educational trends and student learning data to propose the optimal curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned teacher training department, We support the improvement of teachers' professionalism through the evaluation of teaching skills and the proposal of training programs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Business Support Department, Estimate teachers' emotions and adjust the priority of administrative tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned Business Support Department, Analyze teachers' past work history to select the most suitable method for automating administrative tasks. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned Business Support Department, When automating administrative tasks, distribute the work based on the teachers' current workload. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned Business Support Department, The system estimates the teacher's emotions and adjusts the scheduling method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned Business Support Department, When automating administrative tasks, prioritize automating tasks that are highly relevant based on teachers' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned Business Support Department, When automating administrative tasks, analyze teachers' social media activity and automate related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned teaching support department, The system estimates the student's emotions and adjusts the real-time assistance content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned teaching support department, Analyze students' comprehension levels during lessons and select the most appropriate teaching method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned teaching support department, During real-time assistance in class, the content of the assistance is customized based on the student's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned teaching support department, We estimate students' emotions and adjust the comprehension analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned teaching support department, During real-time assistance in class, the system provides highly relevant assistance based on students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned teaching support department, During real-time assistance in class, we analyze students' social media activity and provide relevant assistance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned curriculum development department, The system estimates students' emotions and adjusts the curriculum content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned curriculum development department, We analyze the latest educational trends and select the optimal method for proposing a curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned curriculum development department, We analyze students' learning data and create a curriculum optimized for individualized instruction. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned curriculum development department, The system estimates students' emotions and prioritizes the curriculum based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned curriculum development department, When developing the curriculum, we propose a highly relevant curriculum based on students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned curriculum development department, During curriculum development, we analyze students' social media activity and propose relevant curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned teacher training department, Estimate teachers' emotions and adjust the method of evaluating teaching skills based on the estimated teachers' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned teacher training department, We analyze teachers' past teaching records and propose the most suitable training program. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned teacher training department, When evaluating teaching skills, customize the evaluation content based on the teacher's current teaching situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned teacher training department, The program estimates the teachers' emotions and adjusts the content of the training program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned teacher training department, When evaluating teaching skills, conduct highly relevant assessments based on the teacher's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned teacher training department, When evaluating teaching skills, analyze teachers' social media activity and conduct relevant assessments. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Business Support Department assists teachers with their duties, The teaching support department provides support for classes based on the business data supported by the aforementioned business support department, The Curriculum Development Department develops a curriculum based on the lesson data supported by the aforementioned Lesson Support Department, The system comprises a teacher training department that trains teachers based on the curriculum developed by the aforementioned curriculum development department. A system characterized by the following features.

2. The aforementioned Business Support Department, Automate administrative tasks and manage schedules. The system according to feature 1.

3. The aforementioned teaching support department, It provides real-time assistance during lessons and analysis of students' comprehension levels. The system according to feature 1.

4. The aforementioned curriculum development department, We analyze the latest educational trends and student learning data to propose the optimal curriculum. The system according to feature 1.

5. The aforementioned teacher training department, We support the improvement of teachers' professionalism through the evaluation of teaching skills and the proposal of training programs. The system according to feature 1.

6. The aforementioned Business Support Department, Estimate teachers' emotions and adjust the priority of administrative tasks based on those estimated emotions. The system according to feature 1.

7. The aforementioned Business Support Department, Analyze teachers' past work history to select the most suitable method for automating administrative tasks. The system according to feature 1.

8. The aforementioned Business Support Department, When automating administrative tasks, distribute the work based on the teachers' current workload. The system according to feature 1.

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