system

Abstract

The system according to this embodiment aims to streamline teachers' work and improve the quality of education. [Solution] The system according to this embodiment comprises a grade management unit, a lesson preparation unit, a progress monitoring unit, a question answering unit, and an event management unit. The grade management unit performs grade management and grading. The lesson preparation unit performs lesson preparation and material creation. The progress monitoring unit monitors students' progress. The question answering unit answers questions and provides supplementary instruction. The event management unit manages events and activities.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the workload of teachers is large and it is difficult to perform tasks efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of teachers' work and the quality of education.

Means for Solving the Problems

[0006] The system according to the embodiment includes a grade management unit, a class preparation unit, a progress monitoring unit, a question-and-answer unit, and an event management unit. The grade management unit manages grades and conducts scoring. The class preparation unit prepares classes and creates teaching materials. The progress monitoring unit monitors the progress of students. The question-and-answer unit answers questions and provides supplementary guidance. The event management unit manages events and activities. [Effects of the Invention]

[0007] The system according to this embodiment can streamline teachers' work and improve the quality of education. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 ​​assistant teacher system according to an embodiment of the present invention is a solution for improving the efficiency of teachers' work. This AI assistant teacher system uses AI to automate five tasks: grade management and grading, lesson preparation and material creation, student progress monitoring, question answering and supplementary instruction, and event management. For example, the AI ​​assistant teacher system includes a grade management unit that performs grade management and grading, with AI grading tests and homework and digitally recording and managing grades. This saves time on grading, reduces human error, and enables consistent evaluation in real time. It also includes a lesson preparation unit that performs lesson preparation and material creation, with AI searching for and creating materials, planning lessons, and creating presentation materials. This enables the rapid provision of materials, individualized support according to students' proficiency levels, and the provision of high-quality materials. Furthermore, it includes a progress monitoring unit that monitors students' progress, collecting and analyzing learning data, understanding each student's progress, and creating reports. This allows for real-time understanding of each student's proficiency level, enabling early follow-up and improving the quality of data-driven instruction. The system includes a question-answering department that provides real-time answers to student questions and supplementary guidance, with AI providing explanations and supplementary information. This enables 24 / 7 support, enhances individualized attention, and improves learning support outside of class. It also includes an event management department that handles event scheduling, attendance confirmation, and notifications, all performed by AI. This reduces administrative burden, streamlines schedule management, and minimizes communication errors and omissions. By automating these tasks with AI, teachers' working hours are reduced, allowing them to dedicate more time to lesson preparation and student guidance. Furthermore, consistent evaluation and the provision of high-quality teaching materials become possible, improving the quality of education. The total market size is expected to be approximately 6 trillion yen, bringing positive impacts to the education field and society as a whole. In summary, the AI ​​assistant teacher system can improve the efficiency of teachers' work and enhance the quality of education.

[0029] The AI ​​assistant teacher system according to this embodiment comprises a grade management unit, a lesson preparation unit, a progress monitoring unit, a question answering unit, and an event management unit. The grade management unit performs grade management and grading. For example, the grade management unit grades tests and homework and digitally records and manages grades. For example, the grade management unit uses AI to automatically grade test papers and saves grades as digital data. The grade management unit can also analyze grade data and understand trends in grades. For example, the grade management unit analyzes fluctuations in grades based on past grade data and identifies the causes of improvement or decline in grades. The grade management unit can also create individual student grade reports based on grade data. For example, the grade management unit analyzes students' strengths and weaknesses based on grade data and develops individualized instruction plans. The lesson preparation unit performs lesson preparation and material creation. For example, the lesson preparation unit searches for and creates teaching materials, plans lessons, and creates presentation materials. For example, the lesson preparation unit uses AI to automatically search for teaching materials and create the necessary materials. The Lesson Preparation Department can also plan lessons and support the progress of those lessons. For example, the Lesson Preparation Department can create lesson plans tailored to each student's proficiency level, enabling individualized instruction. The Lesson Preparation Department can also create presentation materials to visually communicate the lesson content. For example, the Lesson Preparation Department can use AI to automatically create presentation materials and effectively communicate the lesson content. The Progress Monitoring Department monitors students' progress. For example, the Progress Monitoring Department can collect and analyze learning data to understand each student's progress and create reports. For example, the Progress Monitoring Department can use AI to automatically collect learning data and understand progress in real time. The Progress Monitoring Department can also create individual student progress reports based on the progress data. For example, the Progress Monitoring Department can analyze students' proficiency levels based on the progress data and create individualized instruction plans. The Progress Monitoring Department can also identify students' strengths and weaknesses based on the progress data and provide early support. For example, the Progress Monitoring Department can understand students' learning progress in real time based on the progress data and provide necessary support. The question-and-answer section provides question-and-answer and supplementary instruction. For example, the question-and-answer section answers students' questions in real time and provides supplementary explanations.The question-answering unit can, for example, use AI to automatically answer students' questions and provide supplementary explanations. The question-answering unit can also analyze the content of students' questions and provide appropriate answers. For example, it can search for relevant information based on the content of students' questions and provide appropriate answers. The question-answering unit can also record answers to students' questions for later reference. For example, it can store students' questions and answers in a database for later retrieval. The event management unit manages events and activities. For example, the event management unit handles tasks such as event scheduling, attendance confirmation, and notifications. For example, the event management unit can automatically manage event schedules and confirm attendance using AI. The event management unit can also automatically send notifications about events. For example, it can send notifications to participants based on the event schedule. The event management unit can also record event attendance data for later reference. For example, it can store event attendance data in a database for later retrieval. As a result, the AI ​​assistant teacher system according to this embodiment can improve the efficiency of teachers' work and enhance the quality of education.

[0030] The Grade Management Department is responsible for managing and grading grades. For example, it grades tests and homework, and digitally records and manages grades. Specifically, the Grade Management Department uses AI to automatically grade test papers and save the grades as digital data. The AI ​​uses natural language processing technology to analyze the content of the answers and grade them accurately. For example, even for written response questions, the AI ​​can understand the context and provide appropriate evaluations. The Grade Management Department can also analyze grade data to understand trends in performance. For example, based on past grade data, the Department can analyze fluctuations in grades and identify the causes of improvements or declines. This allows teachers to understand students' learning situations in detail and provide appropriate guidance. Furthermore, the Grade Management Department can create individual student performance reports based on the performance data. For example, based on the performance data, the Department can analyze students' strengths and weaknesses and develop individualized instruction plans. This allows teachers to provide instruction tailored to each student. The Grade Management Department also stores performance data in the cloud, making it accessible anytime, anywhere. This allows teachers to check grade data and take necessary actions even outside of school. Furthermore, the grade management department can automatically generate and send grade reports to parents. This allows parents to understand their child's learning progress and provide support at home. The grade management department also takes security into consideration for grade data, preventing data leaks through encryption and access restrictions. As a result, the grade management department can manage grades safely and efficiently, reducing the workload of teachers.

[0031] The Lesson Preparation Department is responsible for lesson preparation and material creation. For example, it searches for and creates teaching materials, develops lesson plans, and creates presentation materials. Specifically, the Lesson Preparation Department uses AI to automatically search for teaching materials and create the necessary materials. The AI ​​selects materials suitable for the lesson content from a vast amount of information on the internet and provides them to teachers. For example, the AI ​​can search for the latest research papers and educational materials on a specific topic and present them to teachers. The Lesson Preparation Department can also develop lesson plans and support the progress of lessons. For example, it can develop lesson plans tailored to students' proficiency levels, enabling individualized instruction. The AI ​​can analyze students' past learning data and suggest the most suitable lesson content for each student. This allows teachers to conduct lessons tailored to each individual student. Furthermore, the Lesson Preparation Department can create presentation materials to visually communicate the lesson content. For example, the Lesson Preparation Department uses AI to automatically create presentation materials and effectively communicate the lesson content. Based on the lesson content, the AI ​​can select appropriate images and graphs to create visually easy-to-understand presentation materials. Furthermore, the lesson preparation department can monitor the progress of lessons in real time and revise the lesson plan as needed. This allows the department to prepare lessons efficiently and effectively, reducing the workload of teachers. In addition, the department can collect feedback on lesson content and incorporate it into subsequent lessons. This enables the department to consistently provide lessons based on the latest information, improving the quality of education.

[0032] The Progress Monitoring Department monitors students' progress. For example, it collects and analyzes learning data, understands each student's progress, and creates reports. Specifically, the Progress Monitoring Department uses AI to automatically collect learning data and understand progress in real time. The AI ​​analyzes students' learning history and test results, allowing for a detailed understanding of each student's progress. For example, the AI ​​can identify which subjects a student is struggling with and provide the necessary support. The Progress Monitoring Department can also create individual student progress reports based on the progress data. For example, it can analyze students' proficiency levels based on the progress data and develop individualized instruction plans. This allows teachers to provide instruction tailored to each student. Furthermore, the Progress Monitoring Department can identify students' strengths and weaknesses based on the progress data and provide early support. For example, it can understand students' learning progress in real time based on the progress data and provide necessary support. This allows the Progress Monitoring Department to understand students' learning situations in detail and provide appropriate instruction. Furthermore, the progress monitoring unit stores progress data in the cloud, making it accessible anytime, anywhere. This allows teachers to check progress data and take necessary actions even when outside of school. In addition, the progress monitoring unit can automatically create and send progress reports to parents. This allows parents to understand their child's learning progress and provide support at home. The progress monitoring unit also prioritizes the security of progress data, preventing data leaks through encryption and access restrictions. As a result, the progress monitoring unit can monitor progress safely and efficiently, reducing the workload of teachers.

[0033] The question-answering unit provides question-answering and supplementary instruction. For example, the question-answering unit answers students' questions in real time and provides supplementary explanations. Specifically, the question-answering unit uses AI to automatically answer students' questions and provide supplementary explanations. The AI ​​uses natural language processing technology to analyze the content of students' questions and generate appropriate answers. For example, the AI ​​can understand the questions submitted by students, search for relevant information, and provide answers. The question-answering unit can also analyze the content of students' questions and provide appropriate answers. For example, the question-answering unit searches for relevant information based on the content of students' questions and provides appropriate answers. This allows students to resolve their doubts and proceed with their learning. Furthermore, the question-answering unit can record answers to students' questions for later reference. For example, the question-answering unit saves students' questions and answers in a database for later retrieval. This allows students to refer to past questions and answers and deepen their learning. The question-answering unit can also provide supplementary instruction based on the content of students' questions. For example, the question-answering unit can identify parts that students find difficult to understand and provide additional materials or explanations. This allows students to deepen their understanding and advance their learning. Furthermore, the question-answering unit can analyze trends in question content and identify common points of confusion. This enables teachers to review their lesson content and provide more effective instruction. The question-answering unit also takes security into consideration for question data, preventing data leakage through encryption and access restrictions. As a result, the question-answering unit can answer questions safely and efficiently, reducing the workload of teachers.

[0034] The Event Management Department is responsible for managing events and activities. For example, the Event Management Department handles tasks such as scheduling events, confirming attendance, and sending notifications. Specifically, the Event Management Department uses AI to automatically manage event schedules and confirm attendance. The AI ​​uses a calendar function to centrally manage event schedules and send notifications to participants. For example, the AI ​​can send reminders to participants based on the event schedule and confirm attendance. The Event Management Department can also automatically send notifications for events. For example, the Event Management Department sends notifications to participants based on the event schedule. This allows participants to understand the event schedule and take appropriate action. Furthermore, the Event Management Department can record event attendance data for later reference. For example, the Event Management Department saves event attendance data to a database for later searching. This allows teachers to understand the attendance status of events and take appropriate action. Furthermore, the Event Management Department can collect feedback on events and incorporate it into future events. This allows the Event Management Department to always provide events based on the latest information, improving participant satisfaction. In addition, the Event Management Department stores event schedules in the cloud, making them accessible anytime, anywhere. This allows participants to check event schedules and take appropriate action even when outside of school. The Event Management Department also takes security into consideration for schedule data, preventing data leaks by encrypting data and restricting access. As a result, the Event Management Department can manage events safely and efficiently, reducing the workload of teachers.

[0035] The grade management department can grade tests and homework and digitally record and manage grades. For example, the grade management department can scan test and homework answer sheets and automatically grade them using AI. For example, the grade management department can use AI to analyze the content of answer sheets and understand the correct answer rate and the trend of incorrect answers. The grade management department can also digitally record and manage grade data. For example, the grade management department can save grade data as digital data and search and refer to it as needed. The grade management department can also create individual student grade reports based on the grade data. For example, the grade management department can analyze students' strengths and weaknesses based on the grade data and develop individualized instruction plans. This allows the grade management department to save time on grading and reduce human error. Some or all of the above processes in the grade management department may be performed using AI or not. For example, the grade management department can scan test and homework answers, input the resulting image data into a generating AI, and have the AI ​​generate text data from the image data.

[0036] The lesson preparation department can search for and create teaching materials, plan lessons, and create presentation materials. For example, the lesson preparation department can use AI to automatically search for teaching materials and create the necessary materials. For example, the lesson preparation department can use AI to search for and download relevant teaching materials from online teaching material databases. The lesson preparation department can also plan lessons and support the progress of the lessons. For example, the lesson preparation department can use AI to plan lessons according to the students' proficiency levels and provide individualized support. The lesson preparation department can also create presentation materials to visually convey the content of the lessons. For example, the lesson preparation department can use AI to automatically create presentation materials and effectively convey the content of the lessons. This enables the lesson preparation department to provide teaching materials quickly and to provide individualized support according to the students' proficiency levels. Some or all of the above processes in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input teaching material data obtained from online teaching material databases into a generating AI and have the generating AI perform the searching and creation of teaching materials.

[0037] The progress monitoring unit can collect and analyze learning data, understand the progress of each student, and create reports. For example, the progress monitoring unit can use AI to automatically collect learning data and understand the progress in real time. For example, the progress monitoring unit can use AI to analyze students' learning data and understand their progress. Furthermore, the progress monitoring unit can create individual student progress reports based on the progress data. For example, the progress monitoring unit can use AI to analyze progress data and evaluate students' proficiency levels. Based on the progress data, the progress monitoring unit can identify students' strengths and weaknesses and provide early support. For example, the progress monitoring unit can use AI to analyze progress data, understand students' learning progress in real time, and provide necessary support. This allows the progress monitoring unit to understand each student's proficiency level in real time and enable early support. Some or all of the above processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input students' learning data into a generating AI and have the generating AI perform progress analysis and report creation.

[0038] The question-answering unit can answer students' questions in real time and provide supplementary explanations. For example, the question-answering unit can use AI to automatically answer students' questions and provide supplementary explanations. For example, the question-answering unit can use AI to analyze the content of students' questions and generate appropriate answers. The question-answering unit can also search for relevant information based on the content of students' questions and provide appropriate answers. For example, the question-answering unit can use AI to search for information on the internet and generate answers to students' questions. The question-answering unit can also record answers to students' questions for later reference. For example, the question-answering unit can use AI to save students' questions and answers in a database for later retrieval. This enables the question-answering unit to be available 24 hours a day and to provide more personalized support. Some or all of the above processes in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input the content of students' questions into a generating AI and have the generating AI generate appropriate answers and supplementary explanations.

[0039] The Event Management Department can perform tasks such as scheduling events, confirming attendance, and sending notifications. For example, the Event Management Department can use AI to automatically manage event schedules and confirm attendance. For example, the Event Management Department can use AI to create event schedules and send notifications to participants. The Event Management Department can also record event attendance data for later reference. For example, the Event Management Department can use AI to save event attendance data to a database for later retrieval. This reduces the administrative burden on the Event Management Department and streamlines schedule management. Some or all of the above processes performed by the Event Management Department may be performed using AI or not. For example, the Event Management Department can input event schedule data into a generating AI and have the generating AI create schedules and send notifications.

[0040] The performance management department can analyze performance trends by referring to past performance data and develop individualized instruction plans. For example, the performance management department can use AI to analyze past performance data and understand performance trends. For example, the performance management department can identify a student's strengths and weaknesses from past performance data and develop instruction plans. The performance management department can also analyze performance trends and set individual assignments according to the student's learning progress. The performance management department can also create instruction plans tailored to the student's learning pace based on past performance data. This enables the performance management department to develop instruction plans based on past performance data. Some or all of the above processes in the performance management department may be performed using AI or not. For example, the performance management department can input past performance data into a generating AI and have the generating AI perform performance trend analysis and instruction plan development.

[0041] The performance management department can analyze the difficulty level and question trends of tests and reflect this in the creation of future tests. For example, the performance management department can use AI to analyze past test data and create tests with adjusted difficulty levels. For example, the performance management department can use AI to analyze question trends and create tests to measure the level of understanding of the learning material. Furthermore, the performance management department can create tests tailored to individual learning needs based on students' performance data. In this way, the performance management department can use the analysis of test difficulty and question trends to help create future tests. Some or all of the above processes in the performance management department may be performed using AI or not. For example, the performance management department can input past test data into a generating AI and have the generating AI perform an analysis of difficulty level and question trends.

[0042] The performance management department can conduct comprehensive evaluations based on student attendance and extracurricular activity data. For example, the performance management department can use AI to analyze student attendance and extracurricular activity data and conduct comprehensive evaluations. For example, the performance management department can consider attendance and add points to students with high attendance rates. The performance management department can also evaluate students who are active in extracurricular activities based on their data. The performance management department can comprehensively evaluate attendance and extracurricular activity data and reflect this in grades. This enables the performance management department to conduct comprehensive evaluations that take attendance and extracurricular activity data into account. Some or all of the above processes in the performance management department may be performed using AI or not. For example, the performance management department can input student attendance and extracurricular activity data into a generating AI and have the generating AI perform the comprehensive evaluation.

[0043] The performance management department can customize notification methods to parents and share grades at the appropriate time. For example, the performance management department can use AI to customize notification methods to parents and share grades. For example, the performance management department can share grades via email or app notifications, depending on the parents' preferences. The performance management department can also immediately notify parents if there are significant changes in grades. The performance management department can also strengthen communication with parents by providing regular grade reports. This allows the performance management department to share grades at the appropriate time by customizing notification methods to parents. Some or all of the above processes in the performance management department may be performed using AI or not. For example, the performance management department can input notification methods to parents into a generating AI and have the generating AI customize and send notifications.

[0044] The lesson preparation department can optimize lesson plans by referring to past lesson data. For example, the lesson preparation department can use AI to analyze past lesson data and optimize lesson plans. For example, the lesson preparation department can formulate effective lesson plans based on past lesson data. The lesson preparation department can also adjust lesson content considering students' level of understanding. The lesson preparation department can also analyze past lesson data and optimize the pace of the lesson. This enables the lesson preparation department to optimize lesson plans based on past lesson data. Some or all of the above processes in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input past lesson data into a generating AI and have the generating AI perform the optimization of lesson plans.

[0045] The lesson preparation department can provide different teaching material formats depending on the students' learning styles. For example, the lesson preparation department can use AI to analyze students' learning styles and provide appropriate teaching material formats. For example, the lesson preparation department can provide teaching materials that heavily utilize diagrams and videos for students with a visual learning style. It can also provide teaching materials that include audio explanations for students with an auditory learning style. It can also provide teaching materials that include practical exercises for students with a tactile learning style. This enables the lesson preparation department to provide teaching material formats tailored to students' learning styles. Some or all of the above processing in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input student learning style data into a generating AI and have the generating AI provide the teaching material formats.

[0046] The lesson preparation department can incorporate best practices by referring to other teachers' lesson plans. For example, the lesson preparation department can use AI to analyze other teachers' lesson plans and adopt effective methods. For example, the lesson preparation department can develop lesson plans by referring to other teachers' success stories. The lesson preparation department can also analyze other teachers' lesson plans and adopt effective methods. The lesson preparation department can also create its own lesson plans based on other teachers' lesson plans. This enables the lesson preparation department to develop effective lesson plans by referring to other teachers' lesson plans. Some or all of the above processes in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input other teachers' lesson plan data into a generating AI and have the generating AI implement the incorporation of best practices.

[0047] The lesson preparation department can customize teaching materials based on the local educational curriculum. For example, the lesson preparation department can use AI to analyze the local educational curriculum and adjust the content of the teaching materials. The lesson preparation department can adjust the content of the teaching materials based on the local educational curriculum. The lesson preparation department can also plan lessons by referring to the local educational curriculum. The lesson preparation department can also provide teaching materials that are in line with the local educational curriculum. This enables the lesson preparation department to customize teaching materials based on the local educational curriculum. Some or all of the above processes in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input local educational curriculum data into a generating AI and have the generating AI perform the customization of teaching materials.

[0048] The progress monitoring unit can predict learning progress by referring to past learning data. For example, the progress monitoring unit can use AI to analyze past learning data and predict learning progress. For example, the progress monitoring unit can predict future learning progress based on past learning data. The progress monitoring unit can also analyze learning data and formulate instructional plans according to learning progress. The progress monitoring unit can also predict learning progress by referring to past learning data and provide early follow-up. This enables the progress monitoring unit to predict learning progress based on past learning data. Some or all of the above processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input past learning data into a generating AI and have the generating AI perform learning progress prediction.

[0049] The progress monitoring unit can provide individualized support based on students' learning environments and home circumstances. For example, the progress monitoring unit can use AI to analyze students' learning environments and home circumstances and provide appropriate support. For example, the progress monitoring unit can consider students' learning environments and provide appropriate learning support. Furthermore, the progress monitoring unit can consider students' home circumstances and develop individualized learning plans. The progress monitoring unit can also provide support tailored to students' learning environments and home circumstances, thereby assisting their learning progress. This enables the progress monitoring unit to provide individualized support tailored to students' learning environments and home circumstances. Some or all of the above-described processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input student learning environment and home circumstances data into a generating AI and have the generating AI perform the provision of individualized support.

[0050] The progress monitoring unit can improve motivation by providing comparative data with other students. For example, the progress monitoring unit can use AI to analyze comparative data with other students and improve motivation. For example, the progress monitoring unit can compare performance with other students and improve motivation. Furthermore, the progress monitoring unit can set goals by referring to the learning progress of other students. The progress monitoring unit can also evaluate learning progress based on comparative data with other students. Thus, the progress monitoring unit can improve student motivation by providing comparative data with other students. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input comparative data with other students into a generating AI and have the generating AI perform analysis for motivation improvement.

[0051] The progress monitoring unit can strengthen collaboration with parents and counselors to provide comprehensive support. For example, the progress monitoring unit can use AI to strengthen collaboration with parents and counselors and provide comprehensive support. For example, the progress monitoring unit can strengthen collaboration with parents and share information about students' learning progress. Furthermore, the progress monitoring unit can strengthen collaboration with counselors and provide learning support to students. Based on collaboration with parents and counselors, the progress monitoring unit can also provide comprehensive learning support. This enables the progress monitoring unit to provide comprehensive learning support by strengthening collaboration with parents and counselors. Some or all of the above-described processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input collaboration data with parents and counselors into a generating AI and have the generating AI perform the provision of comprehensive support.

[0052] The question-answering unit can provide the best answer by referring to past question data. The question-answering unit can, for example, use AI to analyze past question data and provide the best answer. The question-answering unit can, for example, provide the best answer to common questions based on past question data. The question-answering unit can also refer to a student's past question history and provide relevant answers. The question-answering unit can also analyze past question data and provide the most effective answer. This enables the question-answering unit to provide the best answer based on past question data. Some or all of the above processing in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input past question data into a generating AI and have the generating AI generate the best answer.

[0053] The question-answering unit can add supplementary explanations according to the student's level of understanding. For example, the question-answering unit can use AI to analyze the student's level of understanding and provide appropriate supplementary explanations. For example, if the student's level of understanding is low, the question-answering unit can provide detailed supplementary explanations. Conversely, if the student's level of understanding is high, the question-answering unit can also provide concise supplementary explanations. The question-answering unit can also provide appropriate supplementary explanations according to the student's level of understanding. This enables the question-answering unit to add supplementary explanations according to the student's level of understanding. Some or all of the above processing in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input student understanding data into a generating AI and have the generating AI perform the addition of supplementary explanations.

[0054] The question-answering unit can suggest relevant questions by referring to the question history of other students. For example, the question-answering unit can use AI to analyze the question history of other students and suggest relevant questions. For example, the question-answering unit can suggest relevant questions based on the topics that other students frequently ask. The question-answering unit can also suggest past questions related to the content of a student's question. The question-answering unit can also analyze the question history of other students and suggest relevant questions. This enables the question-answering unit to suggest relevant questions based on the question history of other students. Some or all of the above processing in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input other students' question history data into a generating AI and have the generating AI suggest relevant questions.

[0055] The question-answering unit can provide visually easy-to-understand answers using videos and diagrams. For example, the question-answering unit can use AI to create videos and diagrams and provide visually easy-to-understand answers. The question-answering unit can, for example, use videos to provide visually easy-to-understand answers. Furthermore, the question-answering unit can use diagrams to explain complex content in an easy-to-understand way. The question-answering unit can also combine videos and diagrams to provide effective answers. This enables the question-answering unit to provide visually easy-to-understand answers by using videos and diagrams. Some or all of the above-described processes in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input video and diagram data into a generating AI and have the generating AI create visually easy-to-understand answers.

[0056] The event management department can create an optimal schedule by referring to past event data. For example, the event management department can use AI to analyze past event data and create an optimal schedule. For example, the event management department can prioritize scheduling events that had high participant satisfaction based on past event data. The event management department can also analyze past event data to determine the optimal timing for holding an event. The event management department can also create an effective schedule by referring to past event data. This enables the event management department to create an optimal schedule based on past event data. Some or all of the above processes in the event management department may be performed using AI or not. For example, the event management department can input past event data into a generating AI and have the generating AI create an optimal schedule.

[0057] The event management department can collect participant feedback and incorporate it into future events. For example, the event management department can use AI to analyze participant feedback and incorporate it into future events. For example, the event management department can collect participant feedback after an event has ended and incorporate it into future events. Furthermore, the event management department can improve the content of events based on participant feedback. The event management department can also analyze feedback and use it to plan future events. This allows the event management department to improve future events based on participant feedback. Some or all of the above processes in the event management department may be performed using AI or not. For example, the event management department can input participant feedback data into a generating AI and have the generating AI implement the changes for future events.

[0058] The event management department can adjust schedules by referring to event information from other schools and regions. For example, the event management department can use AI to analyze event information from other schools and regions and adjust schedules. For example, the event management department can adjust schedules to avoid overlaps based on event information from other schools. The event management department can also refer to regional event information to determine the optimal timing for holding events. The event management department can also plan effective schedules by referring to event information from other schools and regions. This enables the event management department to adjust schedules based on event information from other schools and regions. Some or all of the above processes in the event management department may be performed using AI or not. For example, the event management department can input event information data from other schools and regions into a generating AI and have the generating AI perform schedule adjustments.

[0059] The event management department can streamline participant management and communication by utilizing digital tools. For example, the event management department can use AI to streamline participant management and communication by utilizing digital tools. For example, the event management department can use digital tools to register and manage participants. Furthermore, the event management department can also streamline communication with participants by utilizing digital tools. The event management department can also use digital tools to manage event schedules. In this way, the event management department can streamline participant management and communication by utilizing digital tools. Some or all of the above processes in the event management department may be performed using AI or not. For example, the event management department can input data from digital tools into a generating AI and have the generating AI perform the streamlining of participant management and communication.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The AI ​​assistant teacher system can also include a health management department that monitors students' health status. This department, for example, uses AI to collect student health data and monitor their health status in real time. It can monitor students' body temperature and heart rate, for instance, and issue alerts if abnormalities are detected. Furthermore, the health management department can create health status reports based on student health data. For example, it can periodically create health status reports and provide them to parents and teachers. This allows the health management department to monitor students' health status in real time and take early action.

[0062] The AI ​​assistant teacher system can also include a Creative Support Department to further cultivate students' creativity. The Creative Support Department, for example, uses AI to assist students' creative activities. For instance, the Creative Support Department can use AI to support students in generating ideas and help develop them. Furthermore, the Creative Support Department can evaluate students' work and provide feedback. For example, the Creative Support Department can use AI to evaluate students' paintings and essays and provide feedback indicating areas for improvement. This allows the Creative Support Department to foster students' creativity and promote creative activities.

[0063] The AI ​​assistant homeroom system can also include a social skills support department to further cultivate students' social skills. This department could, for example, use AI to analyze students' social skills and provide appropriate support. It could also offer training programs to improve students' communication skills. Furthermore, it could propose activities to foster students' cooperation and leadership skills. For instance, it could provide opportunities for group work and discussions to cultivate students' social skills. This allows the social skills support department to improve students' social skills and foster their social development.

[0064] The AI ​​assistant teacher system can further provide individualized learning plans tailored to each student's learning style. For example, it can use AI to analyze a student's learning style and create an appropriate learning plan. For students with a visual learning style, it can provide materials that make extensive use of diagrams and videos, while for students with an auditory learning style, it can provide materials that include audio explanations. Furthermore, for students with a tactile learning style, it can provide materials that include practical exercises. In this way, the AI ​​assistant teacher system can provide individualized learning plans tailored to each student's learning style, maximizing learning effectiveness.

[0065] The AI ​​assistant teacher system can further predict learning progress based on students' learning history. For example, it can use AI to analyze students' past learning data and predict their future learning progress. Based on past learning data, it can identify students' strengths and weaknesses and create individualized learning plans. It can also provide early follow-up according to learning progress. As a result, the AI ​​assistant teacher system can predict learning progress based on students' learning history, enabling effective learning support.

[0066] The AI ​​assistant teacher system can further monitor students' learning environments and provide appropriate learning environments. For example, it can use AI to monitor students' learning environments and provide appropriate learning environments. It can analyze students' learning environments and provide environments that enhance concentration. It can also identify areas for improvement in the learning environment and take appropriate measures. In this way, the AI ​​assistant teacher system can maximize learning effectiveness by monitoring students' learning environments and providing appropriate learning environments.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The Grade Management Department manages and grades. For example, it grades tests and homework, and digitally records and manages grades. Using AI, it automatically grades test papers and saves grades as digital data. It can also analyze grade data to understand trends in performance. Based on past grade data, it analyzes fluctuations in grades and identifies the causes of improvement or decline. Based on the grade data, it creates individual grade reports for each student, analyzes students' strengths and weaknesses, and develops individualized instruction plans. Step 2: The Lesson Preparation Department prepares for lessons and creates teaching materials. For example, they search for and create teaching materials, plan lessons, and create presentation materials. They use AI to automatically search for teaching materials and create the necessary materials. They plan lessons and support the progress of the lessons. They plan lessons according to the students' proficiency levels and provide individualized support. They create presentation materials to visually convey the content of the lessons. They use AI to automatically create presentation materials and effectively convey the content of the lessons. Step 3: The progress monitoring department monitors students' progress. For example, it collects and analyzes learning data to understand each student's progress and creates reports. It uses AI to automatically collect learning data and understand progress in real time. Based on the progress data, it creates progress reports for each student. Based on the progress data, it analyzes students' proficiency levels and develops individualized instruction plans. Based on the progress data, it identifies students' strengths and weaknesses and provides early support. Based on the progress data, it understands students' learning progress in real time and provides necessary support. Step 4: The question-answering department provides question-answering and supplementary instruction. For example, it answers students' questions in real time and provides supplementary explanations. It uses AI to automatically answer students' questions and provide supplementary explanations. It analyzes the content of students' questions and provides appropriate answers. It searches for relevant information based on the content of students' questions and provides appropriate answers. It records answers to students' questions so that they can be referenced later. It saves students' questions and answers in a database so that they can be searched later. Step 5: The Event Management Department manages events and activities. For example, they handle tasks such as scheduling events, confirming attendance, and sending notifications. They use AI to automatically manage event schedules and confirm attendance. They automatically send notifications about events. They send notifications to participants based on the event schedule. They record event attendance data so that it can be referenced later. They save event attendance data in a database so that it can be searched later.

[0069] (Example of form 2) The AI ​​assistant teacher system according to an embodiment of the present invention is a solution for improving the efficiency of teachers' work. This AI assistant teacher system uses AI to automate five tasks: grade management and grading, lesson preparation and material creation, student progress monitoring, question answering and supplementary instruction, and event management. For example, the AI ​​assistant teacher system includes a grade management unit that performs grade management and grading, with AI grading tests and homework and digitally recording and managing grades. This saves time on grading, reduces human error, and enables consistent evaluation in real time. It also includes a lesson preparation unit that performs lesson preparation and material creation, with AI searching for and creating materials, planning lessons, and creating presentation materials. This enables the rapid provision of materials, individualized support according to students' proficiency levels, and the provision of high-quality materials. Furthermore, it includes a progress monitoring unit that monitors students' progress, collecting and analyzing learning data, understanding each student's progress, and creating reports. This allows for real-time understanding of each student's proficiency level, enabling early follow-up and improving the quality of data-driven instruction. The system includes a question-answering department that provides real-time answers to student questions and supplementary guidance, with AI providing explanations and supplementary information. This enables 24 / 7 support, enhances individualized attention, and improves learning support outside of class. It also includes an event management department that handles event scheduling, attendance confirmation, and notifications, all performed by AI. This reduces administrative burden, streamlines schedule management, and minimizes communication errors and omissions. By automating these tasks with AI, teachers' working hours are reduced, allowing them to dedicate more time to lesson preparation and student guidance. Furthermore, consistent evaluation and the provision of high-quality teaching materials become possible, improving the quality of education. The total market size is expected to be approximately 6 trillion yen, bringing positive impacts to the education field and society as a whole. In summary, the AI ​​assistant teacher system can improve the efficiency of teachers' work and enhance the quality of education.

[0070] The AI ​​assistant teacher system according to this embodiment comprises a grade management unit, a lesson preparation unit, a progress monitoring unit, a question answering unit, and an event management unit. The grade management unit performs grade management and grading. For example, the grade management unit grades tests and homework and digitally records and manages grades. For example, the grade management unit uses AI to automatically grade test papers and saves grades as digital data. The grade management unit can also analyze grade data and understand trends in grades. For example, the grade management unit analyzes fluctuations in grades based on past grade data and identifies the causes of improvement or decline in grades. The grade management unit can also create individual student grade reports based on grade data. For example, the grade management unit analyzes students' strengths and weaknesses based on grade data and develops individualized instruction plans. The lesson preparation unit performs lesson preparation and material creation. For example, the lesson preparation unit searches for and creates teaching materials, plans lessons, and creates presentation materials. For example, the lesson preparation unit uses AI to automatically search for teaching materials and create the necessary materials. The Lesson Preparation Department can also plan lessons and support the progress of those lessons. For example, the Lesson Preparation Department can create lesson plans tailored to each student's proficiency level, enabling individualized instruction. The Lesson Preparation Department can also create presentation materials to visually communicate the lesson content. For example, the Lesson Preparation Department can use AI to automatically create presentation materials and effectively communicate the lesson content. The Progress Monitoring Department monitors students' progress. For example, the Progress Monitoring Department can collect and analyze learning data to understand each student's progress and create reports. For example, the Progress Monitoring Department can use AI to automatically collect learning data and understand progress in real time. The Progress Monitoring Department can also create individual student progress reports based on the progress data. For example, the Progress Monitoring Department can analyze students' proficiency levels based on the progress data and create individualized instruction plans. The Progress Monitoring Department can also identify students' strengths and weaknesses based on the progress data and provide early support. For example, the Progress Monitoring Department can understand students' learning progress in real time based on the progress data and provide necessary support. The question-and-answer section provides question-and-answer and supplementary instruction. For example, the question-and-answer section answers students' questions in real time and provides supplementary explanations.The question-answering unit can, for example, use AI to automatically answer students' questions and provide supplementary explanations. The question-answering unit can also analyze the content of students' questions and provide appropriate answers. For example, it can search for relevant information based on the content of students' questions and provide appropriate answers. The question-answering unit can also record answers to students' questions for later reference. For example, it can store students' questions and answers in a database for later retrieval. The event management unit manages events and activities. For example, the event management unit handles tasks such as event scheduling, attendance confirmation, and notifications. For example, the event management unit can automatically manage event schedules and confirm attendance using AI. The event management unit can also automatically send notifications about events. For example, it can send notifications to participants based on the event schedule. The event management unit can also record event attendance data for later reference. For example, it can store event attendance data in a database for later retrieval. As a result, the AI ​​assistant teacher system according to this embodiment can improve the efficiency of teachers' work and enhance the quality of education.

[0071] The Grade Management Department is responsible for managing and grading grades. For example, it grades tests and homework, and digitally records and manages grades. Specifically, the Grade Management Department uses AI to automatically grade test papers and save the grades as digital data. The AI ​​uses natural language processing technology to analyze the content of the answers and grade them accurately. For example, even for written response questions, the AI ​​can understand the context and provide appropriate evaluations. The Grade Management Department can also analyze grade data to understand trends in performance. For example, based on past grade data, the Department can analyze fluctuations in grades and identify the causes of improvements or declines. This allows teachers to understand students' learning situations in detail and provide appropriate guidance. Furthermore, the Grade Management Department can create individual student performance reports based on the performance data. For example, based on the performance data, the Department can analyze students' strengths and weaknesses and develop individualized instruction plans. This allows teachers to provide instruction tailored to each student. The Grade Management Department also stores performance data in the cloud, making it accessible anytime, anywhere. This allows teachers to check grade data and take necessary actions even outside of school. Furthermore, the grade management department can automatically generate and send grade reports to parents. This allows parents to understand their child's learning progress and provide support at home. The grade management department also takes security into consideration for grade data, preventing data leaks through encryption and access restrictions. As a result, the grade management department can manage grades safely and efficiently, reducing the workload of teachers.

[0072] The Lesson Preparation Department is responsible for lesson preparation and material creation. For example, it searches for and creates teaching materials, develops lesson plans, and creates presentation materials. Specifically, the Lesson Preparation Department uses AI to automatically search for teaching materials and create the necessary materials. The AI ​​selects materials suitable for the lesson content from a vast amount of information on the internet and provides them to teachers. For example, the AI ​​can search for the latest research papers and educational materials on a specific topic and present them to teachers. The Lesson Preparation Department can also develop lesson plans and support the progress of lessons. For example, it can develop lesson plans tailored to students' proficiency levels, enabling individualized instruction. The AI ​​can analyze students' past learning data and suggest the most suitable lesson content for each student. This allows teachers to conduct lessons tailored to each individual student. Furthermore, the Lesson Preparation Department can create presentation materials to visually communicate the lesson content. For example, the Lesson Preparation Department uses AI to automatically create presentation materials and effectively communicate the lesson content. Based on the lesson content, the AI ​​can select appropriate images and graphs to create visually easy-to-understand presentation materials. Furthermore, the lesson preparation department can monitor the progress of lessons in real time and revise the lesson plan as needed. This allows the department to prepare lessons efficiently and effectively, reducing the workload of teachers. In addition, the department can collect feedback on lesson content and incorporate it into subsequent lessons. This enables the department to consistently provide lessons based on the latest information, improving the quality of education.

[0073] The Progress Monitoring Department monitors students' progress. For example, it collects and analyzes learning data, understands each student's progress, and creates reports. Specifically, the Progress Monitoring Department uses AI to automatically collect learning data and understand progress in real time. The AI ​​analyzes students' learning history and test results, allowing for a detailed understanding of each student's progress. For example, the AI ​​can identify which subjects a student is struggling with and provide the necessary support. The Progress Monitoring Department can also create individual student progress reports based on the progress data. For example, it can analyze students' proficiency levels based on the progress data and develop individualized instruction plans. This allows teachers to provide instruction tailored to each student. Furthermore, the Progress Monitoring Department can identify students' strengths and weaknesses based on the progress data and provide early support. For example, it can understand students' learning progress in real time based on the progress data and provide necessary support. This allows the Progress Monitoring Department to understand students' learning situations in detail and provide appropriate instruction. Furthermore, the progress monitoring unit stores progress data in the cloud, making it accessible anytime, anywhere. This allows teachers to check progress data and take necessary actions even when outside of school. In addition, the progress monitoring unit can automatically create and send progress reports to parents. This allows parents to understand their child's learning progress and provide support at home. The progress monitoring unit also prioritizes the security of progress data, preventing data leaks through encryption and access restrictions. As a result, the progress monitoring unit can monitor progress safely and efficiently, reducing the workload of teachers.

[0074] The question-answering unit provides question-answering and supplementary instruction. For example, the question-answering unit answers students' questions in real time and provides supplementary explanations. Specifically, the question-answering unit uses AI to automatically answer students' questions and provide supplementary explanations. The AI ​​uses natural language processing technology to analyze the content of students' questions and generate appropriate answers. For example, the AI ​​can understand the questions submitted by students, search for relevant information, and provide answers. The question-answering unit can also analyze the content of students' questions and provide appropriate answers. For example, the question-answering unit searches for relevant information based on the content of students' questions and provides appropriate answers. This allows students to resolve their doubts and proceed with their learning. Furthermore, the question-answering unit can record answers to students' questions for later reference. For example, the question-answering unit saves students' questions and answers in a database for later retrieval. This allows students to refer to past questions and answers and deepen their learning. The question-answering unit can also provide supplementary instruction based on the content of students' questions. For example, the question-answering unit can identify parts that students find difficult to understand and provide additional materials or explanations. This allows students to deepen their understanding and advance their learning. Furthermore, the question-answering unit can analyze trends in question content and identify common points of confusion. This enables teachers to review their lesson content and provide more effective instruction. The question-answering unit also takes security into consideration for question data, preventing data leakage through encryption and access restrictions. As a result, the question-answering unit can answer questions safely and efficiently, reducing the workload of teachers.

[0075] The Event Management Department is responsible for managing events and activities. For example, the Event Management Department handles tasks such as scheduling events, confirming attendance, and sending notifications. Specifically, the Event Management Department uses AI to automatically manage event schedules and confirm attendance. The AI ​​uses a calendar function to centrally manage event schedules and send notifications to participants. For example, the AI ​​can send reminders to participants based on the event schedule and confirm attendance. The Event Management Department can also automatically send notifications for events. For example, the Event Management Department sends notifications to participants based on the event schedule. This allows participants to understand the event schedule and take appropriate action. Furthermore, the Event Management Department can record event attendance data for later reference. For example, the Event Management Department saves event attendance data to a database for later searching. This allows teachers to understand the attendance status of events and take appropriate action. Furthermore, the Event Management Department can collect feedback on events and incorporate it into future events. This allows the Event Management Department to always provide events based on the latest information, improving participant satisfaction. In addition, the Event Management Department stores event schedules in the cloud, making them accessible anytime, anywhere. This allows participants to check event schedules and take appropriate action even when outside of school. The Event Management Department also takes security into consideration for schedule data, preventing data leaks by encrypting data and restricting access. As a result, the Event Management Department can manage events safely and efficiently, reducing the workload of teachers.

[0076] The grade management department can grade tests and homework and digitally record and manage grades. For example, the grade management department can scan test and homework answer sheets and automatically grade them using AI. For example, the grade management department can use AI to analyze the content of answer sheets and understand the correct answer rate and the trend of incorrect answers. The grade management department can also digitally record and manage grade data. For example, the grade management department can save grade data as digital data and search and refer to it as needed. The grade management department can also create individual student grade reports based on the grade data. For example, the grade management department can analyze students' strengths and weaknesses based on the grade data and develop individualized instruction plans. This allows the grade management department to save time on grading and reduce human error. Some or all of the above processes in the grade management department may be performed using AI or not. For example, the grade management department can scan test and homework answers, input the resulting image data into a generating AI, and have the AI ​​generate text data from the image data.

[0077] The lesson preparation department can search for and create teaching materials, plan lessons, and create presentation materials. For example, the lesson preparation department can use AI to automatically search for teaching materials and create the necessary materials. For example, the lesson preparation department can use AI to search for and download relevant teaching materials from online teaching material databases. The lesson preparation department can also plan lessons and support the progress of the lessons. For example, the lesson preparation department can use AI to plan lessons according to the students' proficiency levels and provide individualized support. The lesson preparation department can also create presentation materials to visually convey the content of the lessons. For example, the lesson preparation department can use AI to automatically create presentation materials and effectively convey the content of the lessons. This enables the lesson preparation department to provide teaching materials quickly and to provide individualized support according to the students' proficiency levels. Some or all of the above processes in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input teaching material data obtained from online teaching material databases into a generating AI and have the generating AI perform the searching and creation of teaching materials.

[0078] The progress monitoring unit can collect and analyze learning data, understand the progress of each student, and create reports. For example, the progress monitoring unit can use AI to automatically collect learning data and understand the progress in real time. For example, the progress monitoring unit can use AI to analyze students' learning data and understand their progress. Furthermore, the progress monitoring unit can create individual student progress reports based on the progress data. For example, the progress monitoring unit can use AI to analyze progress data and evaluate students' proficiency levels. Based on the progress data, the progress monitoring unit can identify students' strengths and weaknesses and provide early support. For example, the progress monitoring unit can use AI to analyze progress data, understand students' learning progress in real time, and provide necessary support. This allows the progress monitoring unit to understand each student's proficiency level in real time and enable early support. Some or all of the above processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input students' learning data into a generating AI and have the generating AI perform progress analysis and report creation.

[0079] The question-answering unit can answer students' questions in real time and provide supplementary explanations. For example, the question-answering unit can use AI to automatically answer students' questions and provide supplementary explanations. For example, the question-answering unit can use AI to analyze the content of students' questions and generate appropriate answers. The question-answering unit can also search for relevant information based on the content of students' questions and provide appropriate answers. For example, the question-answering unit can use AI to search for information on the internet and generate answers to students' questions. The question-answering unit can also record answers to students' questions for later reference. For example, the question-answering unit can use AI to save students' questions and answers in a database for later retrieval. This enables the question-answering unit to be available 24 hours a day and to provide more personalized support. Some or all of the above processes in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input the content of students' questions into a generating AI and have the generating AI generate appropriate answers and supplementary explanations.

[0080] The Event Management Department can perform tasks such as scheduling events, confirming attendance, and sending notifications. For example, the Event Management Department can use AI to automatically manage event schedules and confirm attendance. For example, the Event Management Department can use AI to create event schedules and send notifications to participants. The Event Management Department can also record event attendance data for later reference. For example, the Event Management Department can use AI to save event attendance data to a database for later retrieval. This reduces the administrative burden on the Event Management Department and streamlines schedule management. Some or all of the above processes performed by the Event Management Department may be performed using AI or not. For example, the Event Management Department can input event schedule data into a generating AI and have the generating AI create schedules and send notifications.

[0081] The performance management department can estimate students' emotions and adjust the method of providing performance feedback based on those estimated emotions. For example, the performance management department can use AI to estimate students' emotions and adjust the content of the feedback based on those emotions. For example, if a student is feeling down, the performance management department can provide feedback that includes words of encouragement. If a student is feeling confident, the performance management department can provide feedback that sets the next goal. If a student is feeling anxious, the performance management department can provide feedback that indicates specific areas for improvement. This allows the performance management department to provide more appropriate guidance by providing feedback that is tailored to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the performance management department may be performed using AI or not. For example, the performance management department can input student emotion data into a generative AI and have the generative AI execute the content of the feedback.

[0082] The performance management department can analyze performance trends by referring to past performance data and develop individualized instruction plans. For example, the performance management department can use AI to analyze past performance data and understand performance trends. For example, the performance management department can identify a student's strengths and weaknesses from past performance data and develop instruction plans. The performance management department can also analyze performance trends and set individual assignments according to the student's learning progress. The performance management department can also create instruction plans tailored to the student's learning pace based on past performance data. This enables the performance management department to develop instruction plans based on past performance data. Some or all of the above processes in the performance management department may be performed using AI or not. For example, the performance management department can input past performance data into a generating AI and have the generating AI perform performance trend analysis and instruction plan development.

[0083] The performance management department can analyze the difficulty level and question trends of tests and reflect this in the creation of future tests. For example, the performance management department can use AI to analyze past test data and create tests with adjusted difficulty levels. For example, the performance management department can use AI to analyze question trends and create tests to measure the level of understanding of the learning material. Furthermore, the performance management department can create tests tailored to individual learning needs based on students' performance data. In this way, the performance management department can use the analysis of test difficulty and question trends to help create future tests. Some or all of the above processes in the performance management department may be performed using AI or not. For example, the performance management department can input past test data into a generating AI and have the generating AI perform an analysis of difficulty level and question trends.

[0084] The performance management unit can estimate students' emotions and adjust the display order of grades based on those emotions. For example, the performance management unit can use AI to estimate students' emotions and adjust the display order of grades based on those emotions. For example, if a student is feeling anxious, the performance management unit can display grades in order from best to worst. If a student is confident, the performance management unit can display grades in order from those that need improvement. If a student is relaxed, the performance management unit can display grades in a random order. This allows the performance management unit to provide more appropriate feedback by adjusting the display order of grades according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the performance management unit may be performed using AI or not. For example, the performance management unit can input student emotion data into a generative AI and have the generative AI adjust the display order of grades.

[0085] The performance management department can conduct comprehensive evaluations based on student attendance and extracurricular activity data. For example, the performance management department can use AI to analyze student attendance and extracurricular activity data and conduct comprehensive evaluations. For example, the performance management department can consider attendance and add points to students with high attendance rates. The performance management department can also evaluate students who are active in extracurricular activities based on their data. The performance management department can comprehensively evaluate attendance and extracurricular activity data and reflect this in grades. This enables the performance management department to conduct comprehensive evaluations that take attendance and extracurricular activity data into account. Some or all of the above processes in the performance management department may be performed using AI or not. For example, the performance management department can input student attendance and extracurricular activity data into a generating AI and have the generating AI perform the comprehensive evaluation.

[0086] The performance management department can customize notification methods to parents and share grades at the appropriate time. For example, the performance management department can use AI to customize notification methods to parents and share grades. For example, the performance management department can share grades via email or app notifications, depending on the parents' preferences. The performance management department can also immediately notify parents if there are significant changes in grades. The performance management department can also strengthen communication with parents by providing regular grade reports. This allows the performance management department to share grades at the appropriate time by customizing notification methods to parents. Some or all of the above processes in the performance management department may be performed using AI or not. For example, the performance management department can input notification methods to parents into a generating AI and have the generating AI customize and send notifications.

[0087] The lesson preparation department can estimate students' emotions and adjust the difficulty level and content of the teaching materials based on those estimated emotions. For example, the lesson preparation department can use AI to estimate students' emotions and adjust the difficulty level and content of the teaching materials based on those estimated emotions. For example, if a student is feeling stressed, the lesson preparation department can provide teaching materials that contain many easy problems. Conversely, if a student is relaxed, the lesson preparation department can provide teaching materials that contain challenging problems. If a student is excited, the lesson preparation department can provide teaching materials that contain engaging content. In this way, the lesson preparation department can provide more appropriate teaching materials by adjusting the difficulty level and content of the teaching materials according to 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 lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input student emotional data into a generating AI and have the AI ​​adjust the difficulty level and content of the teaching materials.

[0088] The lesson preparation department can optimize lesson plans by referring to past lesson data. For example, the lesson preparation department can use AI to analyze past lesson data and optimize lesson plans. For example, the lesson preparation department can formulate effective lesson plans based on past lesson data. The lesson preparation department can also adjust lesson content considering students' level of understanding. The lesson preparation department can also analyze past lesson data and optimize the pace of the lesson. This enables the lesson preparation department to optimize lesson plans based on past lesson data. Some or all of the above processes in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input past lesson data into a generating AI and have the generating AI perform the optimization of lesson plans.

[0089] The lesson preparation department can provide different teaching material formats depending on the students' learning styles. For example, the lesson preparation department can use AI to analyze students' learning styles and provide appropriate teaching material formats. For example, the lesson preparation department can provide teaching materials that heavily utilize diagrams and videos for students with a visual learning style. It can also provide teaching materials that include audio explanations for students with an auditory learning style. It can also provide teaching materials that include practical exercises for students with a tactile learning style. This enables the lesson preparation department to provide teaching material formats tailored to students' learning styles. Some or all of the above processing in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input student learning style data into a generating AI and have the generating AI provide the teaching material formats.

[0090] The lesson preparation unit can estimate students' emotions and adjust the pace of the lesson based on those estimated emotions. For example, the lesson preparation unit can use AI to estimate students' emotions and adjust the pace of the lesson based on those estimated emotions. For example, if students are nervous, the lesson preparation unit can conduct the lesson at a slow pace. If students are relaxed, the lesson preparation unit can conduct the lesson at a normal pace. If students are excited, the lesson preparation unit can conduct the lesson at a fast pace. In this way, the lesson preparation unit can conduct a more appropriate lesson by adjusting the pace of the lesson according to 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 lesson preparation unit may be performed using AI or not. For example, the lesson preparation unit can input student emotion data into a generative AI and have the generative AI adjust the pace of the lesson.

[0091] The lesson preparation department can incorporate best practices by referring to other teachers' lesson plans. For example, the lesson preparation department can use AI to analyze other teachers' lesson plans and adopt effective methods. For example, the lesson preparation department can develop lesson plans by referring to other teachers' success stories. The lesson preparation department can also analyze other teachers' lesson plans and adopt effective methods. The lesson preparation department can also create its own lesson plans based on other teachers' lesson plans. This enables the lesson preparation department to develop effective lesson plans by referring to other teachers' lesson plans. Some or all of the above processes in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input other teachers' lesson plan data into a generating AI and have the generating AI implement the incorporation of best practices.

[0092] The lesson preparation department can customize teaching materials based on the local educational curriculum. For example, the lesson preparation department can use AI to analyze the local educational curriculum and adjust the content of the teaching materials. The lesson preparation department can adjust the content of the teaching materials based on the local educational curriculum. The lesson preparation department can also plan lessons by referring to the local educational curriculum. The lesson preparation department can also provide teaching materials that are in line with the local educational curriculum. This enables the lesson preparation department to customize teaching materials based on the local educational curriculum. Some or all of the above processes in the lesson preparation department may be performed using AI or not. For example, the lesson preparation department can input local educational curriculum data into a generating AI and have the generating AI perform the customization of teaching materials.

[0093] The progress monitoring unit can estimate students' emotions and adjust the content of the progress report based on the estimated emotions. For example, the progress monitoring unit can use AI to estimate students' emotions and adjust the content of the progress report based on the estimated emotions. For example, if a student is feeling down, the progress monitoring unit can provide a report that includes a lot of positive feedback. Also, if a student is feeling confident, the progress monitoring unit can provide a report that sets the next goal. If a student is feeling anxious, the progress monitoring unit can provide a report that shows specific areas for improvement. In this way, the progress monitoring unit can provide more appropriate feedback by adjusting the content of the progress report according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the progress monitoring unit may be performed using AI or not using AI. For example, the progress monitoring unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the content of the progress report.

[0094] The progress monitoring unit can predict learning progress by referring to past learning data. For example, the progress monitoring unit can use AI to analyze past learning data and predict learning progress. For example, the progress monitoring unit can predict future learning progress based on past learning data. The progress monitoring unit can also analyze learning data and formulate instructional plans according to learning progress. The progress monitoring unit can also predict learning progress by referring to past learning data and provide early follow-up. This enables the progress monitoring unit to predict learning progress based on past learning data. Some or all of the above processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input past learning data into a generating AI and have the generating AI perform learning progress prediction.

[0095] The progress monitoring unit can provide individualized support based on students' learning environments and home circumstances. For example, the progress monitoring unit can use AI to analyze students' learning environments and home circumstances and provide appropriate support. For example, the progress monitoring unit can consider students' learning environments and provide appropriate learning support. Furthermore, the progress monitoring unit can consider students' home circumstances and develop individualized learning plans. The progress monitoring unit can also provide support tailored to students' learning environments and home circumstances, thereby assisting their learning progress. This enables the progress monitoring unit to provide individualized support tailored to students' learning environments and home circumstances. Some or all of the above-described processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input student learning environment and home circumstances data into a generating AI and have the generating AI perform the provision of individualized support.

[0096] The progress monitoring unit can estimate students' emotions and adjust the display method of the progress report based on the estimated emotions. For example, the progress monitoring unit can use AI to estimate students' emotions and adjust the display method of the progress report based on the estimated emotions. For example, if a student is feeling anxious, the progress monitoring unit can provide a display method that emphasizes positive elements. Also, if a student is feeling confident, the progress monitoring unit can provide a display method that sets the next goal. If a student is relaxed, the progress monitoring unit can provide a display method that includes detailed information. This allows the progress monitoring unit to provide more appropriate feedback by adjusting the display method of the progress report according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input student emotion data into the generative AI and have the generative AI adjust the display method of the progress report.

[0097] The progress monitoring unit can improve motivation by providing comparative data with other students. For example, the progress monitoring unit can use AI to analyze comparative data with other students and improve motivation. For example, the progress monitoring unit can compare performance with other students and improve motivation. Furthermore, the progress monitoring unit can set goals by referring to the learning progress of other students. The progress monitoring unit can also evaluate learning progress based on comparative data with other students. Thus, the progress monitoring unit can improve student motivation by providing comparative data with other students. Some or all of the above processing in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input comparative data with other students into a generating AI and have the generating AI perform analysis for motivation improvement.

[0098] The progress monitoring unit can strengthen collaboration with parents and counselors to provide comprehensive support. For example, the progress monitoring unit can use AI to strengthen collaboration with parents and counselors and provide comprehensive support. For example, the progress monitoring unit can strengthen collaboration with parents and share information about students' learning progress. Furthermore, the progress monitoring unit can strengthen collaboration with counselors and provide learning support to students. Based on collaboration with parents and counselors, the progress monitoring unit can also provide comprehensive learning support. This enables the progress monitoring unit to provide comprehensive learning support by strengthening collaboration with parents and counselors. Some or all of the above-described processes in the progress monitoring unit may be performed using AI or not. For example, the progress monitoring unit can input collaboration data with parents and counselors into a generating AI and have the generating AI perform the provision of comprehensive support.

[0099] The question-answering unit can estimate a student's emotions and adjust the way it expresses its answers based on those emotions. For example, the question-answering unit can use AI to estimate a student's emotions and adjust the way it expresses its answers based on those emotions. For example, if a student is nervous, the question-answering unit can provide answers in gentle language. If a student is relaxed, the question-answering unit can provide answers that include detailed explanations. If a student is excited, the question-answering unit can provide concise and clear answers. This allows the question-answering unit to provide more appropriate answers by adjusting the way it expresses its responses according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input student emotion data into a generative AI and have the generative AI adjust the way it expresses its answers.

[0100] The question-answering unit can provide the best answer by referring to past question data. The question-answering unit can, for example, use AI to analyze past question data and provide the best answer. The question-answering unit can, for example, provide the best answer to common questions based on past question data. The question-answering unit can also refer to a student's past question history and provide relevant answers. The question-answering unit can also analyze past question data and provide the most effective answer. This enables the question-answering unit to provide the best answer based on past question data. Some or all of the above processing in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input past question data into a generating AI and have the generating AI generate the best answer.

[0101] The question-answering unit can add supplementary explanations according to the student's level of understanding. For example, the question-answering unit can use AI to analyze the student's level of understanding and provide appropriate supplementary explanations. For example, if the student's level of understanding is low, the question-answering unit can provide detailed supplementary explanations. Conversely, if the student's level of understanding is high, the question-answering unit can also provide concise supplementary explanations. The question-answering unit can also provide appropriate supplementary explanations according to the student's level of understanding. This enables the question-answering unit to add supplementary explanations according to the student's level of understanding. Some or all of the above processing in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input student understanding data into a generating AI and have the generating AI perform the addition of supplementary explanations.

[0102] The question-answering unit can estimate a student's emotions and determine the priority of answers based on the estimated emotions. For example, the question-answering unit can use AI to estimate a student's emotions and determine the priority of answers based on the estimated emotions. For example, if a student is feeling anxious, the question-answering unit can provide answers quickly. If a student is relaxed, the question-answering unit can also provide answers with normal priority. If a student is agitated, the question-answering unit can also prioritize answering important questions. This allows the question-answering unit to provide more appropriate answers by determining the priority of answers according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input student emotion data into a generative AI and have the generative AI determine the priority of answers.

[0103] The question-answering unit can suggest relevant questions by referring to the question history of other students. For example, the question-answering unit can use AI to analyze the question history of other students and suggest relevant questions. For example, the question-answering unit can suggest relevant questions based on the topics that other students frequently ask. The question-answering unit can also suggest past questions related to the content of a student's question. The question-answering unit can also analyze the question history of other students and suggest relevant questions. This enables the question-answering unit to suggest relevant questions based on the question history of other students. Some or all of the above processing in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input other students' question history data into a generating AI and have the generating AI suggest relevant questions.

[0104] The question-answering unit can provide visually easy-to-understand answers using videos and diagrams. For example, the question-answering unit can use AI to create videos and diagrams and provide visually easy-to-understand answers. The question-answering unit can, for example, use videos to provide visually easy-to-understand answers. Furthermore, the question-answering unit can use diagrams to explain complex content in an easy-to-understand way. The question-answering unit can also combine videos and diagrams to provide effective answers. This enables the question-answering unit to provide visually easy-to-understand answers by using videos and diagrams. Some or all of the above-described processes in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input video and diagram data into a generating AI and have the generating AI create visually easy-to-understand answers.

[0105] The event management department can estimate students' emotions and adjust event schedules based on those estimates. For example, the event management department can use AI to estimate students' emotions and adjust event schedules based on those estimates. For instance, if a student is stressed, the event management department can prioritize scheduling relaxing events. Similarly, if a student is excited, the event management department can prioritize scheduling active events. If a student is tired, the event management department can prioritize scheduling events that allow for rest. This allows the event management department to adjust event schedules according to 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 event management department may be performed using AI or not. For example, the event management department can input student emotion data into a generative AI and have the generative AI perform event scheduling.

[0106] The event management department can create an optimal schedule by referring to past event data. For example, the event management department can use AI to analyze past event data and create an optimal schedule. For example, the event management department can prioritize scheduling events that had high participant satisfaction based on past event data. The event management department can also analyze past event data to determine the optimal timing for holding an event. The event management department can also create an effective schedule by referring to past event data. This enables the event management department to create an optimal schedule based on past event data. Some or all of the above processes in the event management department may be performed using AI or not. For example, the event management department can input past event data into a generating AI and have the generating AI create an optimal schedule.

[0107] The event management department can collect participant feedback and incorporate it into future events. For example, the event management department can use AI to analyze participant feedback and incorporate it into future events. For example, the event management department can collect participant feedback after an event has ended and incorporate it into future events. Furthermore, the event management department can improve the content of events based on participant feedback. The event management department can also analyze feedback and use it to plan future events. This allows the event management department to improve future events based on participant feedback. Some or all of the above processes in the event management department may be performed using AI or not. For example, the event management department can input participant feedback data into a generating AI and have the generating AI implement the changes for future events.

[0108] The event management department can estimate students' emotions and adjust event notification methods based on those estimated emotions. For example, the event management department can use AI to estimate students' emotions and adjust event notification methods based on those estimated emotions. For example, if a student is nervous, the event management department can notify them of the event using gentle language. If a student is relaxed, the event management department can also notify them of the event using the normal notification method. If a student is excited, the event management department can also notify them of the event using cheerful language. This allows the event management department to adjust event notification methods according to 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 event management department may be performed using AI or not. For example, the event management department can input student emotion data into a generative AI and have the generative AI adjust the event notification method.

[0109] The event management department can adjust schedules by referring to event information from other schools and regions. For example, the event management department can use AI to analyze event information from other schools and regions and adjust schedules. For example, the event management department can adjust schedules to avoid overlaps based on event information from other schools. The event management department can also refer to regional event information to determine the optimal timing for holding events. The event management department can also plan effective schedules by referring to event information from other schools and regions. This enables the event management department to adjust schedules based on event information from other schools and regions. Some or all of the above processes in the event management department may be performed using AI or not. For example, the event management department can input event information data from other schools and regions into a generating AI and have the generating AI perform schedule adjustments.

[0110] The event management department can streamline participant management and communication by utilizing digital tools. For example, the event management department can use AI to streamline participant management and communication by utilizing digital tools. For example, the event management department can use digital tools to register and manage participants. Furthermore, the event management department can also streamline communication with participants by utilizing digital tools. The event management department can also use digital tools to manage event schedules. In this way, the event management department can streamline participant management and communication by utilizing digital tools. Some or all of the above processes in the event management department may be performed using AI or not. For example, the event management department can input data from digital tools into a generating AI and have the generating AI perform the streamlining of participant management and communication.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The AI ​​assistant teacher system can also include a health management department that monitors students' health status. This department, for example, uses AI to collect student health data and monitor their health status in real time. It can monitor students' body temperature and heart rate, for instance, and issue alerts if abnormalities are detected. Furthermore, the health management department can create health status reports based on student health data. For example, it can periodically create health status reports and provide them to parents and teachers. This allows the health management department to monitor students' health status in real time and take early action.

[0113] The AI ​​assistant teacher system can also be equipped with a motivation management unit to further enhance students' motivation to learn. This unit can, for example, use AI to analyze students' motivation and provide appropriate motivation-boosting measures. It can also create individualized motivation improvement plans based on students' learning data. Furthermore, the motivation management unit can implement a reward system to increase student motivation. For example, it could award points to students when they achieve their goals, allowing them to receive rewards by accumulating points. This enables the motivation management unit to improve students' motivation and enhance their learning outcomes.

[0114] The AI ​​assistant teacher system can also include a Creative Support Department to further cultivate students' creativity. The Creative Support Department, for example, uses AI to assist students' creative activities. For instance, the Creative Support Department can use AI to support students in generating ideas and help develop them. Furthermore, the Creative Support Department can evaluate students' work and provide feedback. For example, the Creative Support Department can use AI to evaluate students' paintings and essays and provide feedback indicating areas for improvement. This allows the Creative Support Department to foster students' creativity and promote creative activities.

[0115] The AI ​​assistant homeroom system can also include a social skills support department to further cultivate students' social skills. This department could, for example, use AI to analyze students' social skills and provide appropriate support. It could also offer training programs to improve students' communication skills. Furthermore, it could propose activities to foster students' cooperation and leadership skills. For instance, it could provide opportunities for group work and discussions to cultivate students' social skills. This allows the social skills support department to improve students' social skills and foster their social development.

[0116] The AI ​​assistant teacher system can further estimate students' emotions and adjust the learning environment based on those estimates. For example, it can use AI to estimate students' emotions and adjust classroom lighting and music accordingly. If a student is feeling stressed, the lighting can be softened and calming music played to create a relaxing environment. Conversely, if a student is concentrating, a quiet environment can be provided to maintain their focus. This allows the AI ​​assistant teacher system to adjust the learning environment according to students' emotions, leading to more effective learning.

[0117] The AI ​​assistant teacher system can further provide individualized learning plans tailored to each student's learning style. For example, it can use AI to analyze a student's learning style and create an appropriate learning plan. For students with a visual learning style, it can provide materials that make extensive use of diagrams and videos, while for students with an auditory learning style, it can provide materials that include audio explanations. Furthermore, for students with a tactile learning style, it can provide materials that include practical exercises. In this way, the AI ​​assistant teacher system can provide individualized learning plans tailored to each student's learning style, maximizing learning effectiveness.

[0118] The AI ​​assistant teacher system can further estimate students' emotions and adjust the pace of learning based on those emotions. For example, it can use AI to estimate students' emotions and adjust the pace of the lesson accordingly. If students are nervous, the lesson can be conducted at a slower pace. Conversely, if students are relaxed, the lesson can be conducted at a normal pace. This allows the AI ​​assistant teacher system to adjust the pace of learning according to students' emotions, leading to more effective learning.

[0119] The AI ​​assistant teacher system can further predict learning progress based on students' learning history. For example, it can use AI to analyze students' past learning data and predict their future learning progress. Based on past learning data, it can identify students' strengths and weaknesses and create individualized learning plans. It can also provide early follow-up according to learning progress. As a result, the AI ​​assistant teacher system can predict learning progress based on students' learning history, enabling effective learning support.

[0120] The AI ​​assistant teacher system can further estimate students' emotions and adjust the content of feedback based on those emotions. For example, it can use AI to estimate students' emotions and adjust the content of feedback accordingly. If a student is feeling down, it can provide feedback that includes words of encouragement. If a student is confident, it can provide feedback that sets the next goal. This allows the AI ​​assistant teacher system to adjust the content of feedback according to the student's emotions, enabling more appropriate guidance.

[0121] The AI ​​assistant teacher system can further monitor students' learning environments and provide appropriate learning environments. For example, it can use AI to monitor students' learning environments and provide appropriate learning environments. It can analyze students' learning environments and provide environments that enhance concentration. It can also identify areas for improvement in the learning environment and take appropriate measures. In this way, the AI ​​assistant teacher system can maximize learning effectiveness by monitoring students' learning environments and providing appropriate learning environments.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The Grade Management Department manages and grades. For example, it grades tests and homework, and digitally records and manages grades. Using AI, it automatically grades test papers and saves grades as digital data. It can also analyze grade data to understand trends in performance. Based on past grade data, it analyzes fluctuations in grades and identifies the causes of improvement or decline. Based on the grade data, it creates individual grade reports for each student, analyzes students' strengths and weaknesses, and develops individualized instruction plans. Step 2: The Lesson Preparation Department prepares for lessons and creates teaching materials. For example, they search for and create teaching materials, plan lessons, and create presentation materials. They use AI to automatically search for teaching materials and create the necessary materials. They plan lessons and support the progress of the lessons. They plan lessons according to the students' proficiency levels and provide individualized support. They create presentation materials to visually convey the content of the lessons. They use AI to automatically create presentation materials and effectively convey the content of the lessons. Step 3: The progress monitoring department monitors students' progress. For example, it collects and analyzes learning data to understand each student's progress and creates reports. It uses AI to automatically collect learning data and understand progress in real time. Based on the progress data, it creates progress reports for each student. Based on the progress data, it analyzes students' proficiency levels and develops individualized instruction plans. Based on the progress data, it identifies students' strengths and weaknesses and provides early support. Based on the progress data, it understands students' learning progress in real time and provides necessary support. Step 4: The question-answering department provides question-answering and supplementary instruction. For example, it answers students' questions in real time and provides supplementary explanations. It uses AI to automatically answer students' questions and provide supplementary explanations. It analyzes the content of students' questions and provides appropriate answers. It searches for relevant information based on the content of students' questions and provides appropriate answers. It records answers to students' questions so that they can be referenced later. It saves students' questions and answers in a database so that they can be searched later. Step 5: The Event Management Department manages events and activities. For example, they handle tasks such as scheduling events, confirming attendance, and sending notifications. They use AI to automatically manage event schedules and confirm attendance. They automatically send notifications about events. They send notifications to participants based on the event schedule. They record event attendance data so that it can be referenced later. They save event attendance data in a database so that it can be searched later.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements mentioned above, including the grade management unit, lesson preparation unit, progress monitoring unit, question answering unit, and event management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the grade management unit is implemented by the control unit 46A of the smart device 14, which grades tests and homework and digitally records and manages grades. The lesson preparation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which searches for and creates teaching materials, plans lessons, and creates presentation materials. The progress monitoring unit is implemented by, for example, the control unit 46A of the smart device 14, which collects and analyzes learning data, grasps the progress of each student, and creates reports. The question answering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which answers students' questions in real time and provides supplementary explanations. The event management unit is implemented by, for example, the control unit 46A of the smart device 14, which performs tasks such as scheduling events and activities, confirming attendance, and sending notifications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements mentioned above, including the grade management unit, lesson preparation unit, progress monitoring unit, question answering unit, and event management unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the grade management unit is implemented by the control unit 46A of the smart glasses 214, which grades tests and homework and digitally records and manages grades. The lesson preparation unit is implemented by the specific processing unit 290 of the data processing unit 12, which searches for and creates teaching materials, plans lessons, and creates presentation materials. The progress monitoring unit is implemented by the control unit 46A of the smart glasses 214, which collects and analyzes learning data, grasps the progress of each student, and creates reports. The question answering unit is implemented by the specific processing unit 290 of the data processing unit 12, which answers students' questions in real time and provides supplementary explanations. The event management unit is implemented by the control unit 46A of the smart glasses 214, which performs tasks such as scheduling events and activities, confirming attendance, and sending notifications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements mentioned above, including the grade management unit, lesson preparation unit, progress monitoring unit, question answering unit, and event management unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the grade management unit is implemented by the control unit 46A of the headset terminal 314, which grades tests and homework and digitally records and manages grades. The lesson preparation unit is implemented by the specific processing unit 290 of the data processing unit 12, which searches for and creates teaching materials, plans lessons, and creates presentation materials. The progress monitoring unit is implemented by the control unit 46A of the headset terminal 314, which collects and analyzes learning data, grasps the progress of each student, and creates reports. The question answering unit is implemented by the specific processing unit 290 of the data processing unit 12, which answers students' questions in real time and provides supplementary explanations. The event management unit is implemented by the control unit 46A of the headset terminal 314, which performs tasks such as scheduling events and activities, confirming attendance, and sending notifications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements mentioned above, including the grade management unit, lesson preparation unit, progress monitoring unit, question answering unit, and event management unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the grade management unit is implemented by the control unit 46A of the robot 414, which grades tests and homework and digitally records and manages grades. The lesson preparation unit is implemented by the specific processing unit 290 of the data processing unit 12, which searches for and creates teaching materials, plans lessons, and creates presentation materials. The progress monitoring unit is implemented by the control unit 46A of the robot 414, which collects and analyzes learning data, grasps the progress of each student, and creates reports. The question answering unit is implemented by the specific processing unit 290 of the data processing unit 12, which answers students' questions in real time and provides supplementary explanations. The event management unit is implemented by the control unit 46A of the robot 414, which performs tasks such as scheduling events and activities, checking attendance, and sending notifications. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The Department of Grade Management, which is responsible for managing and grading grades, The Lesson Preparation Department is responsible for lesson preparation and material creation, The progress monitoring department is responsible for monitoring the progress of students, The question-answering section provides question-answering and supplementary guidance, It includes an Event Management Department that manages events and activities. A system characterized by the following features. (Note 2) The aforementioned performance management department, Grade tests and homework, and digitally record and manage grades. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned class preparation department, Searching for and creating teaching materials, developing lesson plans, and creating presentation materials. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned progress monitoring unit, Collect and analyze learning data, understand each student's progress, and create reports. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned question answering unit is Answering students' questions in real time and providing supplementary explanations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned event management department, This role involves tasks such as scheduling events and activities, confirming attendance, and sending notifications. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned performance management department, The system estimates students' emotions and adjusts the feedback method for their performance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned performance management department, Analyze past performance data to understand academic trends and develop individualized instruction plans. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned performance management department, We will analyze the difficulty level and question trends of the test and reflect this in the creation of the next test. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned performance management department, The system estimates students' emotions and adjusts the display order of grades based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned performance management department, A comprehensive evaluation will be conducted based on student attendance records and extracurricular activity data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned performance management department, Customize how you notify parents and share their child's grades at the right time. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned class preparation department, The system estimates students' emotions and adjusts the difficulty level and content of the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned class preparation department, Optimize lesson plans by referring to past lesson data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned class preparation department, We provide different teaching material formats to suit each student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned class preparation department, The system estimates students' emotions and adjusts the pace of the lesson based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned class preparation department, Refer to other teachers' lesson plans and adopt best practices. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned class preparation department, Customize teaching materials based on the local educational curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress monitoring unit, The system estimates students' emotions and adjusts the content of progress reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned progress monitoring unit, Predicting learning progress by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned progress monitoring unit, We provide individualized support based on each student's learning environment and home circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned progress monitoring unit, The system estimates students' emotions and adjusts how progress reports are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned progress monitoring unit, Providing comparative data with other students can improve motivation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress monitoring unit, We will strengthen collaboration with parents and counselors to provide comprehensive support. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned question answering unit is The system estimates the students' emotions and adjusts the way they express their responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned question answering unit is We provide the best answer by referring to past question data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned question answering unit is Add supplementary explanations according to the students' level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned question answering unit is The system estimates students' emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned question answering unit is Refer to other students' question history and suggest related questions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned question answering unit is We provide answers that are easy to understand visually using videos and diagrams. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned event management department, The system estimates students' emotions and adjusts event schedules based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned event management department, We will create an optimal schedule by referring to past event data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned event management department, We will collect participant feedback and use it to improve future events. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned event management department, The system estimates students' emotions and adjusts how events are notified based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned event management department, Refer to event information from other schools and communities to adjust your schedule. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned event management department, Use digital tools to streamline participant management and communication. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 Department of Grade Management, which is responsible for managing and grading grades, The Lesson Preparation Department is responsible for lesson preparation and material creation, The progress monitoring department is responsible for monitoring the progress of students, The question and answer section provides question-answering and supplementary guidance, It includes an Event Management Department that manages events and activities. A system characterized by the following features.

2. The aforementioned performance management department, Grade tests and homework, and digitally record and manage grades. The system according to feature 1.

3. The aforementioned class preparation department, Searching for and creating teaching materials, developing lesson plans, and creating presentation materials. The system according to feature 1.

4. The aforementioned progress monitoring unit, Collect and analyze learning data, understand each student's progress, and create reports. The system according to feature 1.

5. The aforementioned question answering unit is Answering students' questions in real time and providing supplementary explanations. The system according to feature 1.

6. The aforementioned event management department, This role involves tasks such as scheduling events and activities, confirming attendance, and sending notifications. The system according to feature 1.

7. The aforementioned performance management department, The system estimates students' emotions and adjusts the feedback method for their performance based on those estimated emotions. The system according to feature 1.

8. The aforementioned performance management department, Analyze past performance data to understand academic trends and develop individualized instruction plans. The system according to feature 1.

9. The aforementioned performance management department, We will analyze the difficulty level and question trends of the test and reflect this in the creation of the next test. The system according to feature 1.

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