system

Abstract

The system according to this embodiment aims to reduce the burden on teachers and staff in educational settings and improve the quality of education. [Solution] The system according to the embodiment comprises a planning support unit, a scoring unit, a learning plan provision unit, an administrative automation unit, and a mental health support unit. The planning support unit creates lesson plans. The scoring unit automatically scores exams and homework based on the lesson plans created by the planning support unit. The learning plan provision unit provides individual learning plans based on the scoring results obtained by the scoring unit. The administrative automation unit automates administrative tasks such as attendance management and grade management. The mental health support unit evaluates and supports the mental health of teachers and students.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, due to a shortage of teaching staff and overwork, the quality of education may decline, and there may be problems such as teachers' health problems and an increase in the turnover rate.

[0005] The system according to the embodiment aims to reduce the burden on teaching staff in the educational field and improve the quality of education.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a planning support unit, a scoring unit, a learning plan provision unit, an administrative automation unit, and a mental health support unit. The planning support unit creates lesson plans. The scoring unit automatically scores exams and homework based on the lesson plans created by the planning support unit. The learning plan provision unit provides individual learning plans based on the scoring results obtained by the scoring unit. The administrative automation unit automates administrative tasks such as attendance management and grade management. The mental health support unit evaluates and supports the mental health of teachers and students. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the burden on teachers and staff in educational settings 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is a mechanism that introduces an AI system to alleviate the shortage and excessive workload of teachers and staff in educational settings. This educational support system assists in the creation of lesson plans and reduces teachers' preparation time. Next, the educational support system automatically grades exams and homework, reducing the workload of evaluation. Furthermore, the educational support system provides individualized learning plans optimized for each student, improving the efficiency of learning. In addition, the educational support system automates administrative tasks such as attendance management and grade management, reducing the burden on teachers. Furthermore, the educational support system evaluates and supports the mental health of teachers and students, contributing to the prevention of health problems. Finally, the educational support system assists in communication with parents and automates inquiry handling and feedback. As a result, the quality of education improves, the turnover rate of teachers and staff decreases, and the vicious cycle of the educational environment is broken. For example, the educational support system assists in the creation of lesson plans. In this process, curriculum creation is automated, reducing teachers' preparation time. For example, the educational support system automatically creates lesson plans for each semester, and teachers can proceed with lessons according to that plan. This allows teachers to significantly reduce the time spent on lesson preparation. Next, the educational support system automates the grading of exams and homework. For example, the system scans students' answer sheets and grades them automatically. This reduces the time teachers spend on evaluation, allowing them to focus on other teaching activities. Furthermore, the educational support system provides personalized learning plans optimized for each student. For example, the system analyzes students' learning history and comprehension levels and creates individualized learning plans based on the results. This allows students to learn at their own pace, improving learning effectiveness. The educational support system also automates administrative tasks such as attendance and grade management. For example, the system records student attendance in real time and automatically compiles grades. This reduces the time teachers spend on administrative tasks, allowing them to focus on teaching activities. In addition, the educational support system assesses and supports the mental health of teachers and students. For example, the system monitors the stress levels of teachers and students and provides mental health care advice as needed.This contributes to the prevention of health problems and the improvement of the educational environment. Finally, the educational support system assists communication with parents. For example, the educational support system automatically responds to inquiries from parents and provides feedback. This reduces the time teachers spend communicating with parents and allows them to concentrate on educational activities. This improves the quality of education, reduces teacher turnover, and breaks the vicious cycle in the educational environment. In this way, the educational support system can alleviate teacher shortages and overwork in educational settings and improve the quality of education.

[0029] The educational support system according to this embodiment comprises a planning support unit, a grading unit, a learning plan provision unit, an administrative automation unit, and a mental health support unit. The planning support unit creates lesson plans. The planning support unit automates curriculum creation using, for example, AI. For example, the planning support unit automatically creates lesson plans for each semester, allowing teachers to conduct lessons according to those plans. For example, the planning support unit uses AI to create a curriculum based on lesson objectives, content, progress schedule, etc. The grading unit automatically grades exams and homework based on the lesson plans created by the planning support unit. For example, the grading unit uses AI to scan students' answer sheets and grade them automatically. For example, the grading unit uses AI to analyze the content of the answer sheets and assign scores based on evaluation criteria. For example, the grading unit uses AI to evaluate the accuracy of the answer sheets using a grading algorithm. The learning plan provision unit provides individualized learning plans based on the grading results obtained by the grading unit. The Learning Plan Provision Department, for example, uses AI to analyze students' learning history and comprehension levels and creates individualized learning plans based on the results. For example, the Learning Plan Provision Department uses AI to create learning plans based on students' learning goals, learning content, and progress schedules. For example, the Learning Plan Provision Department uses AI to provide plans that maximize students' learning effectiveness. The Administrative Automation Department automates administrative tasks such as attendance management and grade management. For example, the Administrative Automation Department uses AI to record students' attendance in real time and automatically compile grades. For example, the Administrative Automation Department uses AI to manage attendance based on attendance recording methods and attendance rate calculation methods. For example, the Administrative Automation Department uses AI to manage grades based on grade recording methods and grade evaluation criteria. The Mental Health Support Department evaluates and supports the mental health of teachers and students. For example, the Mental Health Support Department uses AI to monitor the stress levels of teachers and students and provides mental health care advice as needed. For example, the Mental Health Support Department uses AI to evaluate stress levels based on stress measurement methods and evaluation criteria. The Mental Health Support Department, for example, uses AI to provide advice on mental health care, such as counseling and relaxation techniques.As a result, the educational support system according to this embodiment can alleviate the shortage and excessive workload of teachers and staff in educational settings and improve the quality of education.

[0030] The Planning Support Department creates lesson plans. For example, it automates curriculum creation using AI. Specifically, the AI ​​analyzes past lesson data, student learning history, and educational objectives to generate optimal lesson plans. For instance, the AI ​​sets learning objectives for each subject and determines the lesson content and schedule based on those objectives. Furthermore, the AI ​​can monitor students' understanding and progress in real time and adjust the lesson plan as needed. This allows teachers to provide lessons tailored to the individual needs of each student. The Planning Support Department also provides an interface for teachers to review and revise lesson plans. Based on the AI-generated plan, teachers can create optimal lesson plans that reflect their own experience and knowledge. Additionally, the Planning Support Department visualizes the progress of lesson plans and provides tools to make it easier for teachers to manage the progress of their lessons. This allows the Planning Support Department to reduce the burden on teachers and support efficient lesson management.

[0031] The grading department automatically grades exams and homework based on lesson plans created by the planning support department. For example, the grading department uses AI to scan students' answer sheets and grade them automatically. Specifically, the AI ​​uses optical character recognition (OCR) technology to digitize the answer sheets and analyze their content. Next, the AI ​​grades the answer sheets and assigns points based on pre-set evaluation criteria. For example, the AI ​​evaluates the accuracy, logic, and expression of the answer sheets and calculates an overall score. Furthermore, the grading department automatically generates and provides feedback on the answer sheets to students. This allows students to understand their weaknesses and areas for improvement and use this information in their next learning. The grading department also stores the grading results in a database and provides tools to make it easier for teachers to manage grades. As a result, the grading department can significantly reduce the workload of teachers and achieve quick and accurate grade evaluation.

[0032] The Learning Plan Provision Department provides individualized learning plans based on the scoring results obtained by the Scoring Department. For example, the Learning Plan Provision Department uses AI to analyze students' learning history and comprehension levels, and creates individualized learning plans based on the results. Specifically, the AI ​​comprehensively evaluates students' past performance, learning patterns, and comprehension levels to generate an optimal learning plan. For instance, the AI ​​identifies students' weaknesses and provides corresponding supplementary materials and practice problems. The AI ​​can also monitor students' learning progress in real time and adjust the learning plan as needed. This allows students to learn efficiently at their own pace. Furthermore, the Learning Plan Provision Department provides tools to help teachers easily understand students' learning progress. Teachers can then provide appropriate guidance to individual students based on the learning plans created by the AI. This enables the Learning Plan Provision Department to maximize student learning effectiveness and improve the quality of individualized instruction.

[0033] The Administrative Automation Department automates administrative tasks such as attendance management and grade management. For example, it uses AI to record student attendance in real time and automatically compile grades. Specifically, the AI ​​uses cameras and sensors in classrooms to check student attendance and automatically records attendance data. Furthermore, the AI ​​compiles student grade data and automatically generates report cards. For example, the AI ​​comprehensively evaluates grades in each subject, attendance rates, assignment submission status, etc., to create report cards. The Administrative Automation Department also provides an interface to make it easier for teachers to manage grade data. Teachers can check and correct grades based on the data compiled by the AI. In addition, the Administrative Automation Department automatically generates attendance and grade management reports and provides them to administrators of educational institutions. In this way, the Administrative Automation Department can improve the efficiency of administrative tasks and reduce the burden on teachers and administrators.

[0034] The Mental Health Support Department assesses and supports the mental health of teachers and students. For example, it uses AI to monitor the stress levels of teachers and students and provides mental health care advice as needed. Specifically, the AI ​​uses biosensors and questionnaire data to assess stress levels and create individualized care plans. For instance, the AI ​​analyzes biometric data such as heart rate, skin electrical activity, and sleep patterns to detect signs of stress. The AI ​​also conducts regular questionnaires to assess psychological states. This allows the AI ​​to identify the causes and effects of stress and propose appropriate countermeasures. Furthermore, the Mental Health Support Department provides advice on mental health care, including counseling and relaxation techniques. For example, the AI ​​provides guidance on breathing exercises and meditation for relaxation to help reduce stress. It can also collaborate with professional counselors to arrange individual counseling sessions as needed. Through these efforts, the Mental Health Support Department comprehensively supports the mental health of teachers and students, maintaining a healthy environment in the educational setting.

[0035] The Planning Support Department can automate curriculum creation. For example, the Planning Support Department can automate curriculum creation using AI. For example, the Planning Support Department can automatically create lesson plans for each semester, allowing teachers to conduct lessons according to those plans. For example, the Planning Support Department can use AI to create a curriculum based on lesson objectives, content, and progress schedules. This can reduce the time teachers spend preparing for lessons. Curriculum creation includes, but is not limited to, educational objectives, content, and progress schedules. Some or all of the above-described processes in the Planning Support Department may be performed using, for example, generative AI, or not using generative AI. For example, the Planning Support Department can create a curriculum using a generative AI model that takes lesson objectives, content, and progress schedules as input and outputs a curriculum.

[0036] The grading unit can scan answer sheets and perform automatic grading. For example, the grading unit can use AI to scan students' answer sheets and grade them automatically. For example, the grading unit can use AI to analyze the content of the answer sheets and assign scores based on evaluation criteria. For example, the grading unit can use AI to evaluate the accuracy of the answer sheets using a grading algorithm. This can reduce the workload of teachers in evaluation. The scanning of answer sheets includes, but is not limited to, the scanning resolution and the method of saving the scanned data. Some or all of the above processes in the grading unit may be performed using, for example, generative AI, or without generative AI. For example, the grading unit can input data obtained by scanning students' answer sheets into a generative AI and have the generative AI perform the analysis of the content of the answer sheets and grading.

[0037] The learning plan provision unit can create individualized learning plans by analyzing learning history and comprehension levels. For example, the learning plan provision unit can use AI to analyze a student's learning history and comprehension levels and create an individualized learning plan based on the results. For example, the learning plan provision unit can use AI to create a learning plan based on the student's learning goals, learning content, progress schedule, etc. For example, the learning plan provision unit can use AI to provide a plan that maximizes the student's learning effectiveness. This makes it possible to provide a learning plan optimized for each student. Learning history includes, but is not limited to, learning content, learning time, and learning outcomes. Comprehension levels include, but are not limited to, test results and assignment completion. Some or all of the above processing in the learning plan provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning plan provision unit can input data on a student's learning history and comprehension level into a generative AI and have the generative AI create an individualized learning plan.

[0038] The administrative automation department can automate attendance management and grade management. For example, the administrative automation department can use AI to record student attendance in real time and automatically compile grades. For example, the administrative automation department can use AI to manage attendance based on methods for recording attendance and methods for calculating attendance rates. For example, the administrative automation department can use AI to manage grades based on methods for recording grades and criteria for evaluating grades. This can reduce the administrative work of teachers. Attendance management includes, but is not limited to, methods for recording attendance and methods for calculating attendance rates. Grade management includes, but is not limited to, methods for recording grades and criteria for evaluating grades. Some or all of the above processes in the administrative automation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the administrative automation department can input student attendance and grade data into a generative AI and have the generative AI perform the automation of attendance management and grade management.

[0039] The Mental Health Support Department can provide stress level monitoring and mental health care advice. For example, the Mental Health Support Department can use AI to monitor the stress levels of teachers and students and provide mental health care advice as needed. For example, the Mental Health Support Department can use AI to evaluate stress levels based on stress measurement methods, evaluation criteria, etc. The Mental Health Support Department can also use AI to provide mental health care advice, such as counseling and relaxation methods. This can contribute to the prevention of health problems among teachers and students. Stress level monitoring includes, but is not limited to, stress measurement methods and evaluation criteria. Mental health care advice includes, but is not limited to, counseling and relaxation methods. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the Mental Health Support Department can input teacher and student stress level data into a generative AI and have the generative AI perform stress level evaluation and provide mental health care advice.

[0040] The Communication Support Department can automatically respond to inquiries from parents and provide feedback. For example, the Communication Support Department can use AI to automatically respond to inquiries from parents and provide feedback. For example, the Communication Support Department's AI can analyze the content of the parent's inquiry and provide an appropriate answer. For example, the Communication Support Department's AI can automatically generate feedback in response to the parent's inquiry. This can reduce the time teachers spend communicating with parents. Parent inquiries include, but are not limited to, the type of inquiry and the response procedure. Some or all of the above processing in the Communication Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Communication Support Department can input data from parent inquiries into a generative AI and have the generative AI perform the analysis of the inquiry content and generate feedback.

[0041] The Planning Support Department can analyze past lesson plan data and select the optimal curriculum creation method. For example, the Planning Support Department can use AI to analyze past lesson plan data and select the optimal curriculum creation method. For example, the Planning Support Department can use AI to propose the most effective curriculum creation method based on past lesson plan data. For example, the Planning Support Department can analyze past lesson plan data and have AI select a curriculum creation method that suits the teacher's preferences. For example, the Planning Support Department can refer to past lesson plan data and have AI select a curriculum creation method that maximizes student learning effectiveness. This makes it possible to create an optimal curriculum based on past data. Past lesson plan data includes, but is not limited to, data types and analysis methods. Some or all of the above processing in the Planning Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Planning Support Department can input past lesson plan data into generative AI and have the generative AI select the optimal curriculum creation method.

[0042] The planning support department can adjust lesson plans when creating them, taking into account students' learning progress. For example, the planning support department can use AI to monitor students' learning progress in real time and adjust lesson plans accordingly. For example, the planning support department can analyze students' learning progress data, and the AI ​​can create lesson plans tailored to individual learning needs. For example, the planning support department can use AI to adjust the pace of the lesson plan, taking into account students' learning progress. This makes it possible to adjust lesson plans according to students' learning progress. Students' learning progress includes, but is not limited to, test results and assignment completion rates. Some or all of the above processes in the planning support department may be performed using, for example, generative AI, or not using generative AI. For example, the planning support department can input student learning progress data into generative AI and have the generative AI perform the adjustment of the lesson plan.

[0043] The planning support department can adjust lesson plans while taking into account the school's annual event schedule. For example, the planning support department can use AI to refer to the school's annual event schedule and adjust the lesson plans. For example, the planning support department can use AI to adjust the progress of the lesson plans in accordance with the school's event schedule. For example, the planning support department can use AI to adjust the content of the lesson plans while taking the school's event schedule into consideration. This makes it possible to adjust lesson plans in accordance with the school's annual event schedule. The school's annual event schedule includes, but is not limited to, the types of events and how the schedule is adjusted. Some or all of the above processing in the planning support department may be performed using, for example, a generating AI, or without using a generating AI. For example, the planning support department can input data on the school's annual event schedule into a generating AI and have the generating AI perform the adjustment of the lesson plans.

[0044] The planning support department can create lesson plans by referring to local educational policies and curriculum guidelines. For example, the planning support department can use AI to refer to local educational policies and create lesson plans. For example, the planning support department can have the AI ​​adjust the lesson plans based on curriculum guidelines. For example, the planning support department can have the AI ​​create the optimal lesson plan by considering local educational policies and guidelines. This makes it possible to create lesson plans based on local educational policies and curriculum guidelines. Local educational policies include, but are not limited to, educational objectives and curriculum guidelines. Some or all of the above processes in the planning support department may be performed using, for example, generative AI, or not using generative AI. For example, the planning support department can input data on local educational policies and curriculum guidelines into generative AI and have the generative AI create lesson plans.

[0045] The scoring unit can optimize its scoring algorithm by referring to past scoring data during the scoring process. For example, the scoring unit can use AI to analyze past scoring data and optimize the scoring algorithm. For example, the scoring unit can use AI to adjust the scoring criteria based on past scoring data. For example, the scoring unit can use AI to maintain scoring consistency by referring to past scoring data. This makes it possible to optimize the scoring algorithm based on past data. Past scoring data includes, but is not limited to, data types and analysis methods. Some or all of the above processes in the scoring unit may be performed using, for example, generative AI, or without generative AI. For example, the scoring unit can input past scoring data into generative AI and have the generative AI perform the optimization of the scoring algorithm.

[0046] The grading department can improve the accuracy of grading by analyzing students' response patterns during the grading process. For example, the grading department can use AI to analyze students' response patterns and improve grading accuracy. For example, the grading department can use AI to adjust grading criteria based on students' response patterns. For example, the grading department can use AI to maintain grading consistency by referring to students' response patterns. This makes it possible to improve grading accuracy based on students' response patterns. Examples of students' response patterns include, but are not limited to, response tendencies and patterns of incorrect answers. Some or all of the above processes in the grading department may be performed using, for example, generative AI, or not using generative AI. For example, the grading department can input student response pattern data into generative AI and have the generative AI perform the grading accuracy improvement.

[0047] The grading unit can adjust the grading criteria when grading, taking into account the student's learning history. For example, the grading unit may use AI to refer to the student's learning history and adjust the grading criteria. For example, the grading unit may use AI to set individual grading criteria based on the student's learning history. For example, the grading unit may use AI to maintain grading consistency, taking into account the student's learning history. This makes it possible to adjust the grading criteria based on the student's learning history. The student's learning history includes, but is not limited to, learning content, learning time, and learning outcomes. Some or all of the above processes in the grading unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the grading unit may input student learning history data into a generative AI and have the generative AI perform the adjustment of the grading criteria.

[0048] The grading unit can maintain grading consistency by referring to the grading criteria of other teachers during the grading process. For example, the grading unit can use AI to refer to the grading criteria of other teachers and maintain grading consistency. For example, the grading unit can use AI to adjust its grading criteria based on the grading criteria of other teachers. For example, the grading unit can use AI to improve the accuracy of grading by considering the grading criteria of other teachers. This makes it possible to maintain grading consistency based on the grading criteria of other teachers. The grading criteria of other teachers include, but are not limited to, evaluation criteria and grading methods. Some or all of the above processes in the grading unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the grading unit can input data on the grading criteria of other teachers into a generative AI and have the generative AI perform the task of maintaining grading consistency.

[0049] The learning plan provision unit can create an optimal plan by analyzing a student's past learning data when providing a learning plan. For example, the learning plan provision unit can use AI to analyze a student's past learning data and create an optimal learning plan. For example, the learning plan provision unit can use AI to provide an individualized learning plan based on the student's learning history. For example, the learning plan provision unit can refer to the student's learning data and have the AI ​​adjust the content of the learning plan. This makes it possible to create an optimal learning plan based on the student's past learning data. Past learning data includes, but is not limited to, learning content, learning outcomes, and learning time. Some or all of the above processing in the learning plan provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning plan provision unit can input a student's past learning data into a generative AI and have the generative AI create an optimal learning plan.

[0050] The learning plan provision unit can customize learning plans according to the student's learning style when providing them. For example, the learning plan provision unit can use AI to analyze the student's learning style and provide the optimal learning plan. For example, the learning plan provision unit can use AI to adjust the content of the learning plan to match the student's learning style. For example, the learning plan provision unit can use AI to create individual learning plans, taking into account the student's learning style. This makes it possible to provide customized learning plans that are tailored to the student's learning style. Learning styles include, but are not limited to, visual, auditory, and experiential learning. Some or all of the above processing in the learning plan provision unit may be performed using, for example, generative AI, or without generative AI. For example, the learning plan provision unit can input data on the student's learning style into generative AI and have the generative AI perform the plan customization.

[0051] The learning plan provision unit can adjust the learning plan when providing it, taking into account the student's home environment and living situation. For example, the learning plan provision unit can use AI to refer to the student's home environment and adjust the learning plan. For example, the learning plan provision unit can use AI to adjust the content of the learning plan based on the student's living situation. For example, the learning plan provision unit can use AI to provide the optimal learning plan, taking into account the student's home environment and living situation. This makes it possible to adjust the learning plan according to the student's home environment and living situation. Home environment and living situation include, but are not limited to, the family's economic situation and daily routine. Some or all of the above processing in the learning plan provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning plan provision unit can input data on the student's home environment and living situation into a generative AI and have the generative AI perform the plan adjustment.

[0052] The learning plan provision unit can create learning plans based on students' interests and preferences when providing them. For example, the learning plan provision unit can use AI to analyze students' interests and provide the optimal learning plan. For example, the learning plan provision unit can use AI to adjust the content of the learning plan based on students' interests. For example, the learning plan provision unit can use AI to create individualized learning plans, taking into account students' interests and preferences. This makes it possible to create learning plans based on students' interests and preferences. Students' interests and preferences include, but are not limited to, hobbies and future goals. Some or all of the above-described processes in the learning plan provision unit may be performed using, for example, generative AI, or without generative AI. For example, the learning plan provision unit can input data on students' interests and preferences into generative AI and have the generative AI create the plan.

[0053] The Office Automation Department can analyze past office data to select the optimal automation method when automating office tasks. For example, the Office Automation Department can use AI to analyze past office data and select the optimal automation method. For example, the Office Automation Department can use AI to improve the efficiency of office tasks based on past office data. For example, the Office Automation Department can refer to past office data and use AI to optimize the automation of office tasks. This makes it possible to select the optimal automation method based on past office data. Past office data includes, but is not limited to, data types and analysis methods. Some or all of the above processing in the Office Automation Department may be performed using, for example, a generating AI, or without a generating AI. For example, the Office Automation Department can input past office data into a generating AI and have the generating AI select the optimal automation method.

[0054] The administrative automation department can automate administrative tasks while taking into account the school's operational policies and regulations. For example, the administrative automation department can use AI to refer to the school's operational policies and automate administrative tasks. For example, the administrative automation department can use AI to adjust the automation of administrative tasks based on the school's regulations. For example, the administrative automation department can use AI to perform optimal automation of administrative tasks while taking into account the school's operational policies and regulations. This makes it possible to automate administrative tasks based on the school's operational policies and regulations. School operational policies and regulations include, but are not limited to, operational goals and detailed regulations. Some or all of the above processes in the administrative automation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the administrative automation department can input data on the school's operational policies and regulations into a generative AI and have the generative AI perform the automation.

[0055] The Office Automation Department can improve the accuracy of automation by referencing office work data from other schools when automating office tasks. For example, the Office Automation Department can use AI to reference office work data from other schools to improve the accuracy of automation. For example, the Office Automation Department can use AI to improve the efficiency of office work based on office work data from other schools. For example, the Office Automation Department can analyze office work data from other schools and have AI select the optimal automation method. This makes it possible to improve the accuracy of automation based on office work data from other schools. Office work data from other schools includes, but is not limited to, data types and analysis methods. Some or all of the above processes in the Office Automation Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Office Automation Department can input office work data from other schools into a generative AI and have the generative AI perform the task of improving the accuracy of automation.

[0056] The administrative automation unit can perform automation of administrative tasks while taking into account the policies of the local education administration. For example, the administrative automation unit can use AI to refer to the policies of the local education administration and automate administrative tasks. For example, the administrative automation unit can use AI to adjust the automation of administrative tasks based on the policies of the local education administration. For example, the administrative automation unit can use AI to perform the most optimal automation of administrative tasks while taking into account the policies of the local education administration. This makes it possible to automate administrative tasks based on the policies of the local education administration. The policies of the local education administration include, but are not limited to, educational goals and operational policies. Some or all of the above processing in the administrative automation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the administrative automation unit can input data on the policies of the local education administration into a generative AI and have the generative AI perform the automation.

[0057] The Mental Health Support Department can analyze past mental health data to select the optimal support method when providing mental health support. For example, the Mental Health Support Department can use AI to analyze past mental health data and select the optimal support method. For example, the Mental Health Support Department can use AI to provide individualized support methods based on past mental health data. For example, the Mental Health Support Department can refer to past mental health data and have AI evaluate the effectiveness of support methods. This makes it possible to select the optimal support method based on past mental health data. Past mental health data includes, but is not limited to, data types and analysis methods. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Mental Health Support Department can input past mental health data into a generative AI and have the generative AI select support methods.

[0058] The Mental Health Support Department can identify individual stressors and take countermeasures when providing mental health support. For example, the Mental Health Support Department can use AI to identify stressors for teachers and students and propose countermeasures. For example, the Mental Health Support Department can analyze individual stressors and have the AI ​​provide appropriate support methods. For example, the Mental Health Support Department can monitor stressors and have the AI ​​take early countermeasures. This makes it possible to provide countermeasures based on individual stressors. Stressors include, but are not limited to, the causes of stress and countermeasures. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Mental Health Support Department can input data on the stressors of teachers and students into a generative AI and have the generative AI provide countermeasures.

[0059] The Mental Health Support Department can adjust its support methods when providing mental health support, taking into account the home environment and living situation. For example, the Mental Health Support Department can use AI to refer to the home environment of teachers and students and adjust support methods. For example, the Mental Health Support Department can use AI to adjust the content of support methods based on the living situation of teachers and students. For example, the Mental Health Support Department can use AI to provide the optimal support method, taking into account the home environment and living situation of teachers and students. This makes it possible to adjust support methods according to the home environment and living situation. The home environment and living situation include, but are not limited to, the family's economic situation and daily routine. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Mental Health Support Department can input data on the home environment and living situation of teachers and students into a generative AI and have the generative AI perform the adjustment of support methods.

[0060] The Mental Health Support Department can utilize local mental health resources when providing mental health support. For example, the Mental Health Support Department can use AI to refer to local mental health resources and provide support methods. For example, the Mental Health Support Department can use AI to adjust the content of support methods based on local mental health resources. For example, the Mental Health Support Department can use local mental health resources and have AI provide the optimal support method. This makes it possible to provide support that utilizes local mental health resources. Local mental health resources include, but are not limited to, counseling services and support organizations. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Mental Health Support Department can input data on local mental health resources into generative AI and have the generative AI provide support methods.

[0061] The Communication Support Department can analyze past inquiry data to select the optimal response method when providing communication support. For example, the Communication Support Department can use AI to analyze past inquiry data and select the optimal response method. For example, the Communication Support Department can use AI to adjust the response method based on past inquiry data. For example, the Communication Support Department can refer to past inquiry data and use AI to maintain consistency in responses. This makes it possible to select the optimal response method based on past inquiry data. Past inquiry data includes, but is not limited to, data types and analysis methods. Some or all of the above processing in the Communication Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Communication Support Department can input past inquiry data into a generative AI and have the generative AI select a response method.

[0062] The Communication Support Department can take into account parents' concerns and requests when providing communication support. For example, the Communication Support Department can use AI to analyze parents' concerns and provide the most appropriate response. For example, the Communication Support Department can use AI to adjust the content of the response based on parents' requests. For example, the Communication Support Department can use AI to provide individualized response methods, taking into account parents' concerns and requests. This makes it possible to respond in accordance with parents' concerns and requests. Parents' concerns and requests include, but are not limited to, educational policies and details of requests. Some or all of the above processing in the Communication Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Communication Support Department can input data on parents' concerns and requests into a generative AI and have the generative AI adjust the response method.

[0063] The Communication Support Department can adjust its response methods when providing communication support, taking into account the parents' living situation and home environment. For example, the Communication Support Department may use AI to refer to the parents' living situation and adjust its response methods. For example, the Communication Support Department may use AI to adjust the content of its response methods based on the parents' home environment. For example, the Communication Support Department may use AI to provide the optimal response method, taking into account the parents' living situation and home environment. This makes it possible to adjust response methods according to the parents' living situation and home environment. The parents' living situation and home environment include, but are not limited to, the family's economic situation and daily routine. Some or all of the above processing in the Communication Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Communication Support Department may input data on the parents' living situation and home environment into a generative AI and have the generative AI perform the adjustment of the response method.

[0064] The Communication Support Department can refer to local educational policies and guidelines when providing communication support. For example, the Communication Support Department can use AI to refer to local educational policies and provide response methods. For example, the Communication Support Department can use AI to adjust the content of response methods based on local guidelines. For example, the Communication Support Department can consider local educational policies and guidelines and use AI to provide the optimal response method. This makes it possible to respond in accordance with local educational policies and guidelines. Local educational policies and guidelines include, but are not limited to, educational goals and curriculum guidelines. Some or all of the above processing in the Communication Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Communication Support Department can input data on local educational policies and guidelines into generative AI and have the generative AI provide response methods.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The educational support system can also include a performance monitoring unit that monitors teachers' performance in real time and provides feedback. For example, the performance monitoring unit analyzes the teacher's tone of voice, speaking speed, gestures, etc., and evaluates the progress of the lesson and student reactions. This allows teachers to identify areas for improvement in real time during the lesson and improve the quality of their teaching. The performance monitoring unit also provides detailed feedback after the lesson, allowing teachers to specifically identify areas for improvement for the next lesson. Furthermore, the performance monitoring unit can analyze teachers' teaching styles and student reactions over the long term, supporting teacher growth.

[0067] Educational support systems can also incorporate gamification features to further enhance students' motivation to learn. For example, gamification features can provide a system where students earn points or badges each time they achieve a learning goal. This makes it easier for students to maintain their motivation to learn. Gamification features can also present learning content in a game format, allowing students to learn while having fun. Furthermore, gamification features can promote competition and cooperation among students, allowing them to share the enjoyment of learning.

[0068] The educational support system can also include a predictive learning plan provider that forecasts future learning plans based on students' learning history. For example, the predictive learning plan provider analyzes students' past learning history and proposes future learning plans. This allows students to understand a learning plan that suits them in advance and proceed with their studies systematically. Furthermore, the predictive learning plan provider can forecast learning progress based on students' learning history and adjust the learning plan at the appropriate time. In addition, the predictive learning plan provider can provide plans to maximize learning effectiveness based on students' learning history.

[0069] The educational support system can also include a lesson recording unit that automatically records teachers' lesson content for later reference. For example, the lesson recording unit can record teachers' lesson content in audio and video format, making it available for teachers and students to refer to later. This can be used to review and improve lessons. The lesson recording unit can also convert lesson content into text and provide it as a searchable database. Furthermore, the lesson recording unit can analyze lesson content and suggest areas for improvement to teachers.

[0070] The educational support system can also include a learning style adaptation unit that customizes learning content according to the student's learning style. For example, if a student is a visual learner, the learning style adaptation unit can provide learning materials that heavily utilize visual content. This allows students to learn in a way that suits them. Furthermore, if a student is an auditory learner, the learning style adaptation unit can provide learning materials that heavily utilize audio content. Additionally, if a student is an experiential learner, the learning style adaptation unit can provide practical assignments and projects.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The Planning Support Department creates the lesson plan. For example, they use AI to automate curriculum creation and automatically generate lesson plans for each semester. The Planning Support Department creates the curriculum based on the lesson objectives, content, and progress schedule. Step 2: The grading department automatically grades exams and homework based on the lesson plans created by the planning support department. For example, it uses AI to scan students' answer sheets and grade them automatically. The grading department analyzes the content of the answer sheets and assigns points based on evaluation criteria. It uses a grading algorithm to evaluate the accuracy of the answer sheets. Step 3: The learning plan provision department provides individualized learning plans based on the scoring results obtained by the scoring department. For example, it may use AI to analyze students' learning history and level of understanding and create individualized learning plans based on the results. The learning plan provision department creates learning plans based on students' learning goals, learning content, and progress schedule, and provides plans to maximize learning effectiveness. Step 4: The Administrative Automation Department automates administrative tasks such as attendance management and grade management. For example, it uses AI to record student attendance in real time and automatically compile grades. The Administrative Automation Department manages attendance based on methods for recording attendance and calculating attendance rates, and manages grades based on methods for recording grades and evaluation criteria. Step 5: The Mental Health Support Department assesses and supports the mental health of teachers and students. For example, it uses AI to monitor the stress levels of teachers and students and provides mental health care advice as needed. The Mental Health Support Department assesses stress levels based on stress measurement methods and evaluation criteria, and provides mental health care advice such as counseling and relaxation methods.

[0073] (Example of form 2) The educational support system according to an embodiment of the present invention is a mechanism that introduces an AI system to alleviate the shortage and excessive workload of teachers and staff in educational settings. This educational support system assists in the creation of lesson plans and reduces teachers' preparation time. Next, the educational support system automatically grades exams and homework, reducing the workload of evaluation. Furthermore, the educational support system provides individualized learning plans optimized for each student, improving the efficiency of learning. In addition, the educational support system automates administrative tasks such as attendance management and grade management, reducing the burden on teachers. Furthermore, the educational support system evaluates and supports the mental health of teachers and students, contributing to the prevention of health problems. Finally, the educational support system assists in communication with parents and automates inquiry handling and feedback. As a result, the quality of education improves, the turnover rate of teachers and staff decreases, and the vicious cycle of the educational environment is broken. For example, the educational support system assists in the creation of lesson plans. In this process, curriculum creation is automated, reducing teachers' preparation time. For example, the educational support system automatically creates lesson plans for each semester, and teachers can proceed with lessons according to that plan. This allows teachers to significantly reduce the time spent on lesson preparation. Next, the educational support system automates the grading of exams and homework. For example, the system scans students' answer sheets and grades them automatically. This reduces the time teachers spend on evaluation, allowing them to focus on other teaching activities. Furthermore, the educational support system provides personalized learning plans optimized for each student. For example, the system analyzes students' learning history and comprehension levels and creates individualized learning plans based on the results. This allows students to learn at their own pace, improving learning effectiveness. The educational support system also automates administrative tasks such as attendance and grade management. For example, the system records student attendance in real time and automatically compiles grades. This reduces the time teachers spend on administrative tasks, allowing them to focus on teaching activities. In addition, the educational support system assesses and supports the mental health of teachers and students. For example, the system monitors the stress levels of teachers and students and provides mental health care advice as needed.This contributes to the prevention of health problems and the improvement of the educational environment. Finally, the educational support system assists communication with parents. For example, the educational support system automatically responds to inquiries from parents and provides feedback. This reduces the time teachers spend communicating with parents and allows them to concentrate on educational activities. This improves the quality of education, reduces teacher turnover, and breaks the vicious cycle in the educational environment. In this way, the educational support system can alleviate teacher shortages and overwork in educational settings and improve the quality of education.

[0074] The educational support system according to this embodiment comprises a planning support unit, a grading unit, a learning plan provision unit, an administrative automation unit, and a mental health support unit. The planning support unit creates lesson plans. The planning support unit automates curriculum creation using, for example, AI. For example, the planning support unit automatically creates lesson plans for each semester, allowing teachers to conduct lessons according to those plans. For example, the planning support unit uses AI to create a curriculum based on lesson objectives, content, progress schedule, etc. The grading unit automatically grades exams and homework based on the lesson plans created by the planning support unit. For example, the grading unit uses AI to scan students' answer sheets and grade them automatically. For example, the grading unit uses AI to analyze the content of the answer sheets and assign scores based on evaluation criteria. For example, the grading unit uses AI to evaluate the accuracy of the answer sheets using a grading algorithm. The learning plan provision unit provides individualized learning plans based on the grading results obtained by the grading unit. The Learning Plan Provision Department, for example, uses AI to analyze students' learning history and comprehension levels and creates individualized learning plans based on the results. For example, the Learning Plan Provision Department uses AI to create learning plans based on students' learning goals, learning content, and progress schedules. For example, the Learning Plan Provision Department uses AI to provide plans that maximize students' learning effectiveness. The Administrative Automation Department automates administrative tasks such as attendance management and grade management. For example, the Administrative Automation Department uses AI to record students' attendance in real time and automatically compile grades. For example, the Administrative Automation Department uses AI to manage attendance based on attendance recording methods and attendance rate calculation methods. For example, the Administrative Automation Department uses AI to manage grades based on grade recording methods and grade evaluation criteria. The Mental Health Support Department evaluates and supports the mental health of teachers and students. For example, the Mental Health Support Department uses AI to monitor the stress levels of teachers and students and provides mental health care advice as needed. For example, the Mental Health Support Department uses AI to evaluate stress levels based on stress measurement methods and evaluation criteria. The Mental Health Support Department, for example, uses AI to provide advice on mental health care, such as counseling and relaxation techniques.As a result, the educational support system according to this embodiment can alleviate the shortage and excessive workload of teachers and staff in educational settings and improve the quality of education.

[0075] The Planning Support Department creates lesson plans. For example, it automates curriculum creation using AI. Specifically, the AI ​​analyzes past lesson data, student learning history, and educational objectives to generate optimal lesson plans. For instance, the AI ​​sets learning objectives for each subject and determines the lesson content and schedule based on those objectives. Furthermore, the AI ​​can monitor students' understanding and progress in real time and adjust the lesson plan as needed. This allows teachers to provide lessons tailored to the individual needs of each student. The Planning Support Department also provides an interface for teachers to review and revise lesson plans. Based on the AI-generated plan, teachers can create optimal lesson plans that reflect their own experience and knowledge. Additionally, the Planning Support Department visualizes the progress of lesson plans and provides tools to make it easier for teachers to manage the progress of their lessons. This allows the Planning Support Department to reduce the burden on teachers and support efficient lesson management.

[0076] The grading department automatically grades exams and homework based on lesson plans created by the planning support department. For example, the grading department uses AI to scan students' answer sheets and grade them automatically. Specifically, the AI ​​uses optical character recognition (OCR) technology to digitize the answer sheets and analyze their content. Next, the AI ​​grades the answer sheets and assigns points based on pre-set evaluation criteria. For example, the AI ​​evaluates the accuracy, logic, and expression of the answer sheets and calculates an overall score. Furthermore, the grading department automatically generates and provides feedback on the answer sheets to students. This allows students to understand their weaknesses and areas for improvement and use this information in their next learning. The grading department also stores the grading results in a database and provides tools to make it easier for teachers to manage grades. As a result, the grading department can significantly reduce the workload of teachers and achieve quick and accurate grade evaluation.

[0077] The Learning Plan Provision Department provides individualized learning plans based on the scoring results obtained by the Scoring Department. For example, the Learning Plan Provision Department uses AI to analyze students' learning history and comprehension levels, and creates individualized learning plans based on the results. Specifically, the AI ​​comprehensively evaluates students' past performance, learning patterns, and comprehension levels to generate an optimal learning plan. For instance, the AI ​​identifies students' weaknesses and provides corresponding supplementary materials and practice problems. The AI ​​can also monitor students' learning progress in real time and adjust the learning plan as needed. This allows students to learn efficiently at their own pace. Furthermore, the Learning Plan Provision Department provides tools to help teachers easily understand students' learning progress. Teachers can then provide appropriate guidance to individual students based on the learning plans created by the AI. This enables the Learning Plan Provision Department to maximize student learning effectiveness and improve the quality of individualized instruction.

[0078] The Administrative Automation Department automates administrative tasks such as attendance management and grade management. For example, it uses AI to record student attendance in real time and automatically compile grades. Specifically, the AI ​​uses cameras and sensors in classrooms to check student attendance and automatically records attendance data. Furthermore, the AI ​​compiles student grade data and automatically generates report cards. For example, the AI ​​comprehensively evaluates grades in each subject, attendance rates, assignment submission status, etc., to create report cards. The Administrative Automation Department also provides an interface to make it easier for teachers to manage grade data. Teachers can check and correct grades based on the data compiled by the AI. In addition, the Administrative Automation Department automatically generates attendance and grade management reports and provides them to administrators of educational institutions. In this way, the Administrative Automation Department can improve the efficiency of administrative tasks and reduce the burden on teachers and administrators.

[0079] The Mental Health Support Department assesses and supports the mental health of teachers and students. For example, it uses AI to monitor the stress levels of teachers and students and provides mental health care advice as needed. Specifically, the AI ​​uses biosensors and questionnaire data to assess stress levels and create individualized care plans. For instance, the AI ​​analyzes biometric data such as heart rate, skin electrical activity, and sleep patterns to detect signs of stress. The AI ​​also conducts regular questionnaires to assess psychological states. This allows the AI ​​to identify the causes and effects of stress and propose appropriate countermeasures. Furthermore, the Mental Health Support Department provides advice on mental health care, including counseling and relaxation techniques. For example, the AI ​​provides guidance on breathing exercises and meditation for relaxation to help reduce stress. It can also collaborate with professional counselors to arrange individual counseling sessions as needed. Through these efforts, the Mental Health Support Department comprehensively supports the mental health of teachers and students, maintaining a healthy environment in the educational setting.

[0080] The Planning Support Department can automate curriculum creation. For example, the Planning Support Department can automate curriculum creation using AI. For example, the Planning Support Department can automatically create lesson plans for each semester, allowing teachers to conduct lessons according to those plans. For example, the Planning Support Department can use AI to create a curriculum based on lesson objectives, content, and progress schedules. This can reduce the time teachers spend preparing for lessons. Curriculum creation includes, but is not limited to, educational objectives, content, and progress schedules. Some or all of the above-described processes in the Planning Support Department may be performed using, for example, generative AI, or not using generative AI. For example, the Planning Support Department can create a curriculum using a generative AI model that takes lesson objectives, content, and progress schedules as input and outputs a curriculum.

[0081] The grading unit can scan answer sheets and perform automatic grading. For example, the grading unit can use AI to scan students' answer sheets and grade them automatically. For example, the grading unit can use AI to analyze the content of the answer sheets and assign scores based on evaluation criteria. For example, the grading unit can use AI to evaluate the accuracy of the answer sheets using a grading algorithm. This can reduce the workload of teachers in evaluation. The scanning of answer sheets includes, but is not limited to, the scanning resolution and the method of saving the scanned data. Some or all of the above processes in the grading unit may be performed using, for example, generative AI, or without generative AI. For example, the grading unit can input data obtained by scanning students' answer sheets into a generative AI and have the generative AI perform the analysis of the content of the answer sheets and grading.

[0082] The learning plan provision unit can create individualized learning plans by analyzing learning history and comprehension levels. For example, the learning plan provision unit can use AI to analyze a student's learning history and comprehension levels and create an individualized learning plan based on the results. For example, the learning plan provision unit can use AI to create a learning plan based on the student's learning goals, learning content, progress schedule, etc. For example, the learning plan provision unit can use AI to provide a plan that maximizes the student's learning effectiveness. This makes it possible to provide a learning plan optimized for each student. Learning history includes, but is not limited to, learning content, learning time, and learning outcomes. Comprehension levels include, but are not limited to, test results and assignment completion. Some or all of the above processing in the learning plan provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning plan provision unit can input data on a student's learning history and comprehension level into a generative AI and have the generative AI create an individualized learning plan.

[0083] The administrative automation department can automate attendance management and grade management. For example, the administrative automation department can use AI to record student attendance in real time and automatically compile grades. For example, the administrative automation department can use AI to manage attendance based on methods for recording attendance and methods for calculating attendance rates. For example, the administrative automation department can use AI to manage grades based on methods for recording grades and criteria for evaluating grades. This can reduce the administrative work of teachers. Attendance management includes, but is not limited to, methods for recording attendance and methods for calculating attendance rates. Grade management includes, but is not limited to, methods for recording grades and criteria for evaluating grades. Some or all of the above processes in the administrative automation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the administrative automation department can input student attendance and grade data into a generative AI and have the generative AI perform the automation of attendance management and grade management.

[0084] The Mental Health Support Department can provide stress level monitoring and mental health care advice. For example, the Mental Health Support Department can use AI to monitor the stress levels of teachers and students and provide mental health care advice as needed. For example, the Mental Health Support Department can use AI to evaluate stress levels based on stress measurement methods, evaluation criteria, etc. The Mental Health Support Department can also use AI to provide mental health care advice, such as counseling and relaxation methods. This can contribute to the prevention of health problems among teachers and students. Stress level monitoring includes, but is not limited to, stress measurement methods and evaluation criteria. Mental health care advice includes, but is not limited to, counseling and relaxation methods. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the Mental Health Support Department can input teacher and student stress level data into a generative AI and have the generative AI perform stress level evaluation and provide mental health care advice.

[0085] The Communication Support Department can automatically respond to inquiries from parents and provide feedback. For example, the Communication Support Department can use AI to automatically respond to inquiries from parents and provide feedback. For example, the Communication Support Department's AI can analyze the content of the parent's inquiry and provide an appropriate answer. For example, the Communication Support Department's AI can automatically generate feedback in response to the parent's inquiry. This can reduce the time teachers spend communicating with parents. Parent inquiries include, but are not limited to, the type of inquiry and the response procedure. Some or all of the above processing in the Communication Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Communication Support Department can input data from parent inquiries into a generative AI and have the generative AI perform the analysis of the inquiry content and generate feedback.

[0086] The planning support unit can estimate a teacher's emotions and adjust the timing of lesson plan creation based on the estimated emotions. For example, the planning support unit can use AI to estimate a teacher's emotions and adjust the timing of lesson plan creation based on the estimated emotions. For example, if a teacher is stressed, the planning support unit can have the AI ​​delay lesson plan creation to give the teacher time to relax. For example, if a teacher is relaxed, the planning support unit can have the AI ​​speed up lesson plan creation to proceed efficiently. For example, if a teacher is tired, the planning support unit can have the AI ​​divide the lesson plan creation so that it can be completed in a shorter time. This makes it possible to create lesson plans that are in line with the teacher's emotions. Teacher emotions include, but are not limited to, emotion recognition technology and evaluation criteria. Some or all of the above processing in the planning support unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the planning support unit can input teacher emotion data into a generative AI and have the generative AI adjust the timing of lesson plan creation.

[0087] The Planning Support Department can analyze past lesson plan data and select the optimal curriculum creation method. For example, the Planning Support Department can use AI to analyze past lesson plan data and select the optimal curriculum creation method. For example, the Planning Support Department can use AI to propose the most effective curriculum creation method based on past lesson plan data. For example, the Planning Support Department can analyze past lesson plan data and have AI select a curriculum creation method that suits the teacher's preferences. For example, the Planning Support Department can refer to past lesson plan data and have AI select a curriculum creation method that maximizes student learning effectiveness. This makes it possible to create an optimal curriculum based on past data. Past lesson plan data includes, but is not limited to, data types and analysis methods. Some or all of the above processing in the Planning Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Planning Support Department can input past lesson plan data into generative AI and have the generative AI select the optimal curriculum creation method.

[0088] The planning support department can adjust lesson plans when creating them, taking into account students' learning progress. For example, the planning support department can use AI to monitor students' learning progress in real time and adjust lesson plans accordingly. For example, the planning support department can analyze students' learning progress data, and the AI ​​can create lesson plans tailored to individual learning needs. For example, the planning support department can use AI to adjust the pace of the lesson plan, taking into account students' learning progress. This makes it possible to adjust lesson plans according to students' learning progress. Students' learning progress includes, but is not limited to, test results and assignment completion rates. Some or all of the above processes in the planning support department may be performed using, for example, generative AI, or not using generative AI. For example, the planning support department can input student learning progress data into generative AI and have the generative AI perform the adjustment of the lesson plan.

[0089] The planning support unit can estimate a teacher's emotions and determine the priority of lesson plans based on the estimated emotions. For example, the planning support unit can use AI to estimate a teacher's emotions and determine the priority of lesson plans based on the estimated emotions. For example, if a teacher is stressed, the planning support unit's AI will postpone less important lesson plans. For example, if a teacher is relaxed, the planning support unit's AI will prioritize creating more important lesson plans. For example, if a teacher is tired, the planning support unit's AI will prioritize creating simpler lesson plans. This makes it possible to determine the priority of lesson plans in accordance with the teacher's emotions. The priority of lesson plans includes, but is not limited to, importance and urgency. Some or all of the above processing in the planning support unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the planning support unit can input teacher emotion data into a generative AI and have the generative AI perform the determination of lesson plan priorities.

[0090] The planning support department can adjust lesson plans while taking into account the school's annual event schedule. For example, the planning support department can use AI to refer to the school's annual event schedule and adjust the lesson plans. For example, the planning support department can use AI to adjust the progress of the lesson plans in accordance with the school's event schedule. For example, the planning support department can use AI to adjust the content of the lesson plans while taking the school's event schedule into consideration. This makes it possible to adjust lesson plans in accordance with the school's annual event schedule. The school's annual event schedule includes, but is not limited to, the types of events and how the schedule is adjusted. Some or all of the above processing in the planning support department may be performed using, for example, a generating AI, or without using a generating AI. For example, the planning support department can input data on the school's annual event schedule into a generating AI and have the generating AI perform the adjustment of the lesson plans.

[0091] The planning support department can create lesson plans by referring to local educational policies and curriculum guidelines. For example, the planning support department can use AI to refer to local educational policies and create lesson plans. For example, the planning support department can have the AI ​​adjust the lesson plans based on curriculum guidelines. For example, the planning support department can have the AI ​​create the optimal lesson plan by considering local educational policies and guidelines. This makes it possible to create lesson plans based on local educational policies and curriculum guidelines. Local educational policies include, but are not limited to, educational objectives and curriculum guidelines. Some or all of the above processes in the planning support department may be performed using, for example, generative AI, or not using generative AI. For example, the planning support department can input data on local educational policies and curriculum guidelines into generative AI and have the generative AI create lesson plans.

[0092] The scoring unit can estimate students' emotions and adjust the feedback method for scoring results based on the estimated emotions. For example, the scoring unit can use AI to estimate students' emotions and adjust the feedback method for scoring results based on the estimated emotions. For example, if a student is stressed, the scoring unit can have the AI ​​provide feedback in gentle language. For example, if a student is relaxed, the scoring unit can have the AI ​​provide detailed feedback. For example, if a student is excited, the scoring unit can have the AI ​​provide concise feedback. This makes it possible to adjust the feedback method according to the student's emotions. Student emotions include, but are not limited to, emotion recognition technology and evaluation criteria. Some or all of the above processing in the scoring unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the scoring unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the feedback method.

[0093] The scoring unit can optimize its scoring algorithm by referring to past scoring data during the scoring process. For example, the scoring unit can use AI to analyze past scoring data and optimize the scoring algorithm. For example, the scoring unit can use AI to adjust the scoring criteria based on past scoring data. For example, the scoring unit can use AI to maintain scoring consistency by referring to past scoring data. This makes it possible to optimize the scoring algorithm based on past data. Past scoring data includes, but is not limited to, data types and analysis methods. Some or all of the above processes in the scoring unit may be performed using, for example, generative AI, or without generative AI. For example, the scoring unit can input past scoring data into generative AI and have the generative AI perform the optimization of the scoring algorithm.

[0094] The grading department can improve the accuracy of grading by analyzing students' response patterns during the grading process. For example, the grading department can use AI to analyze students' response patterns and improve grading accuracy. For example, the grading department can use AI to adjust grading criteria based on students' response patterns. For example, the grading department can use AI to maintain grading consistency by referring to students' response patterns. This makes it possible to improve grading accuracy based on students' response patterns. Examples of students' response patterns include, but are not limited to, response tendencies and patterns of incorrect answers. Some or all of the above processes in the grading department may be performed using, for example, generative AI, or not using generative AI. For example, the grading department can input student response pattern data into generative AI and have the generative AI perform the grading accuracy improvement.

[0095] The scoring unit can estimate a student's emotions and adjust the display method of the scoring results based on the estimated emotions. For example, the scoring unit can use AI to estimate a student's emotions and adjust the display method of the scoring results based on the estimated emotions. For example, if a student is stressed, the scoring unit can use the AI ​​to provide a simple display method. For example, if a student is relaxed, the scoring unit can use the AI ​​to provide a detailed display method. For example, if a student is excited, the scoring unit can use the AI ​​to provide a visually stimulating display method. This makes it possible to adjust the display method of the scoring results according to the student's emotions. The display method of the scoring results includes, but is not limited to, display format and feedback content. Some or all of the above processing in the scoring unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the scoring unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0096] The grading unit can adjust the grading criteria when grading, taking into account the student's learning history. For example, the grading unit may use AI to refer to the student's learning history and adjust the grading criteria. For example, the grading unit may use AI to set individual grading criteria based on the student's learning history. For example, the grading unit may use AI to maintain grading consistency, taking into account the student's learning history. This makes it possible to adjust the grading criteria based on the student's learning history. The student's learning history includes, but is not limited to, learning content, learning time, and learning outcomes. Some or all of the above processes in the grading unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the grading unit may input student learning history data into a generative AI and have the generative AI perform the adjustment of the grading criteria.

[0097] The grading unit can maintain grading consistency by referring to the grading criteria of other teachers during the grading process. For example, the grading unit can use AI to refer to the grading criteria of other teachers and maintain grading consistency. For example, the grading unit can use AI to adjust its grading criteria based on the grading criteria of other teachers. For example, the grading unit can use AI to improve the accuracy of grading by considering the grading criteria of other teachers. This makes it possible to maintain grading consistency based on the grading criteria of other teachers. The grading criteria of other teachers include, but are not limited to, evaluation criteria and grading methods. Some or all of the above processes in the grading unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the grading unit can input data on the grading criteria of other teachers into a generative AI and have the generative AI perform the task of maintaining grading consistency.

[0098] The learning plan delivery unit can estimate a student's emotions and adjust the timing of the learning plan delivery based on the estimated emotions. For example, the learning plan delivery unit can use AI to estimate a student's emotions and adjust the timing of the learning plan delivery based on the estimated emotions. For example, if a student is stressed, the AI ​​can delay the delivery of the learning plan. For example, if a student is relaxed, the AI ​​can speed up the delivery of the learning plan. For example, if a student is excited, the AI ​​can split the delivery of the learning plan. This makes it possible to adjust the timing of the learning plan delivery according to the student's emotions. The timing of the learning plan delivery includes, but is not limited to, learning progress and emotional state. Some or all of the above processing in the learning plan delivery unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the learning plan delivery unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the delivery timing.

[0099] The learning plan provision unit can create an optimal plan by analyzing a student's past learning data when providing a learning plan. For example, the learning plan provision unit can use AI to analyze a student's past learning data and create an optimal learning plan. For example, the learning plan provision unit can use AI to provide an individualized learning plan based on the student's learning history. For example, the learning plan provision unit can refer to the student's learning data and have the AI ​​adjust the content of the learning plan. This makes it possible to create an optimal learning plan based on the student's past learning data. Past learning data includes, but is not limited to, learning content, learning outcomes, and learning time. Some or all of the above processing in the learning plan provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning plan provision unit can input a student's past learning data into a generative AI and have the generative AI create an optimal learning plan.

[0100] The learning plan provision unit can customize learning plans according to the student's learning style when providing them. For example, the learning plan provision unit can use AI to analyze the student's learning style and provide the optimal learning plan. For example, the learning plan provision unit can use AI to adjust the content of the learning plan to match the student's learning style. For example, the learning plan provision unit can use AI to create individual learning plans, taking into account the student's learning style. This makes it possible to provide customized learning plans that are tailored to the student's learning style. Learning styles include, but are not limited to, visual, auditory, and experiential learning. Some or all of the above processing in the learning plan provision unit may be performed using, for example, generative AI, or without generative AI. For example, the learning plan provision unit can input data on the student's learning style into generative AI and have the generative AI perform the plan customization.

[0101] The learning plan provider can estimate a student's emotions and determine the priority of learning plans based on those emotions. For example, the learning plan provider can use AI to estimate a student's emotions and determine the priority of learning plans based on those emotions. For example, if a student is stressed, the AI ​​will postpone less important learning plans. For example, if a student is relaxed, the AI ​​will prioritize providing more important learning plans. For example, if a student is excited, the AI ​​will prioritize providing simpler learning plans. This makes it possible to determine the priority of learning plans in accordance with the student's emotions. Prioritization of learning plans includes, but is not limited to, importance and urgency. Some or all of the above processing in the learning plan provider may be performed using, for example, an emotion engine or generative AI, or without using such an emotion engine or generative AI. For example, the learning plan provider can input student emotion data into a generative AI and have the generative AI perform the priority determination.

[0102] The learning plan provision unit can adjust the learning plan when providing it, taking into account the student's home environment and living situation. For example, the learning plan provision unit can use AI to refer to the student's home environment and adjust the learning plan. For example, the learning plan provision unit can use AI to adjust the content of the learning plan based on the student's living situation. For example, the learning plan provision unit can use AI to provide the optimal learning plan, taking into account the student's home environment and living situation. This makes it possible to adjust the learning plan according to the student's home environment and living situation. Home environment and living situation include, but are not limited to, the family's economic situation and daily routine. Some or all of the above processing in the learning plan provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning plan provision unit can input data on the student's home environment and living situation into a generative AI and have the generative AI perform the plan adjustment.

[0103] The learning plan provision unit can create learning plans based on students' interests and preferences when providing them. For example, the learning plan provision unit can use AI to analyze students' interests and provide the optimal learning plan. For example, the learning plan provision unit can use AI to adjust the content of the learning plan based on students' interests. For example, the learning plan provision unit can use AI to create individualized learning plans, taking into account students' interests and preferences. This makes it possible to create learning plans based on students' interests and preferences. Students' interests and preferences include, but are not limited to, hobbies and future goals. Some or all of the above-described processes in the learning plan provision unit may be performed using, for example, generative AI, or without generative AI. For example, the learning plan provision unit can input data on students' interests and preferences into generative AI and have the generative AI create the plan.

[0104] The administrative automation unit can estimate a teacher's emotions and adjust the timing of administrative task automation based on the estimated emotions. For example, the administrative automation unit can use AI to estimate a teacher's emotions and adjust the timing of administrative task automation based on the estimated emotions. For example, if the teacher is stressed, the administrative automation unit can delay the automation of administrative tasks. For example, if the teacher is relaxed, the administrative automation unit can speed up the automation of administrative tasks. For example, if the teacher is tired, the administrative automation unit can divide the automation of administrative tasks. This makes it possible to adjust the timing of administrative task automation according to the teacher's emotions. The timing of administrative task automation includes, but is not limited to, the importance and urgency of the task. Some or all of the above processing in the administrative automation unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the administrative automation unit can input teacher emotion data into a generative AI and have the generative AI perform the adjustment of automation timing.

[0105] The Office Automation Department can analyze past office data to select the optimal automation method when automating office tasks. For example, the Office Automation Department can use AI to analyze past office data and select the optimal automation method. For example, the Office Automation Department can use AI to improve the efficiency of office tasks based on past office data. For example, the Office Automation Department can refer to past office data and use AI to optimize the automation of office tasks. This makes it possible to select the optimal automation method based on past office data. Past office data includes, but is not limited to, data types and analysis methods. Some or all of the above processing in the Office Automation Department may be performed using, for example, a generating AI, or without a generating AI. For example, the Office Automation Department can input past office data into a generating AI and have the generating AI select the optimal automation method.

[0106] The administrative automation department can automate administrative tasks while taking into account the school's operational policies and regulations. For example, the administrative automation department can use AI to refer to the school's operational policies and automate administrative tasks. For example, the administrative automation department can use AI to adjust the automation of administrative tasks based on the school's regulations. For example, the administrative automation department can use AI to perform optimal automation of administrative tasks while taking into account the school's operational policies and regulations. This makes it possible to automate administrative tasks based on the school's operational policies and regulations. School operational policies and regulations include, but are not limited to, operational goals and detailed regulations. Some or all of the above processes in the administrative automation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the administrative automation department can input data on the school's operational policies and regulations into a generative AI and have the generative AI perform the automation.

[0107] The administrative automation unit can estimate a teacher's emotions and determine the priority of administrative tasks based on the estimated emotions. For example, the administrative automation unit can use AI to estimate a teacher's emotions and determine the priority of administrative tasks based on the estimated emotions. For example, if a teacher is stressed, the administrative automation unit's AI will postpone less important administrative tasks. For example, if a teacher is relaxed, the administrative automation unit's AI will prioritize and automate more important administrative tasks. For example, if a teacher is tired, the administrative automation unit's AI will prioritize and automate simpler administrative tasks. This makes it possible to determine the priority of administrative tasks in accordance with the teacher's emotions. The priority of administrative tasks includes, but is not limited to, importance and urgency. Some or all of the above processing in the administrative automation unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the administrative automation unit can input teacher emotion data into a generative AI and have the generative AI perform the priority determination.

[0108] The Office Automation Department can improve the accuracy of automation by referencing office work data from other schools when automating office tasks. For example, the Office Automation Department can use AI to reference office work data from other schools to improve the accuracy of automation. For example, the Office Automation Department can use AI to improve the efficiency of office work based on office work data from other schools. For example, the Office Automation Department can analyze office work data from other schools and have AI select the optimal automation method. This makes it possible to improve the accuracy of automation based on office work data from other schools. Office work data from other schools includes, but is not limited to, data types and analysis methods. Some or all of the above processes in the Office Automation Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Office Automation Department can input office work data from other schools into a generative AI and have the generative AI perform the task of improving the accuracy of automation.

[0109] The administrative automation unit can perform automation of administrative tasks while taking into account the policies of the local education administration. For example, the administrative automation unit can use AI to refer to the policies of the local education administration and automate administrative tasks. For example, the administrative automation unit can use AI to adjust the automation of administrative tasks based on the policies of the local education administration. For example, the administrative automation unit can use AI to perform the most optimal automation of administrative tasks while taking into account the policies of the local education administration. This makes it possible to automate administrative tasks based on the policies of the local education administration. The policies of the local education administration include, but are not limited to, educational goals and operational policies. Some or all of the above processing in the administrative automation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the administrative automation unit can input data on the policies of the local education administration into a generative AI and have the generative AI perform the automation.

[0110] The Mental Health Support Department can estimate the emotions of teachers and students and provide mental health care advice based on the estimated emotions. For example, the Mental Health Support Department can use AI to estimate the emotions of teachers and students and provide mental health care advice based on the estimated emotions. For example, if a teacher is feeling stressed, the AI ​​can suggest ways to relax. For example, if a student is feeling anxious, the AI ​​can offer counseling opportunities. For example, the Mental Health Support Department can monitor the emotions of teachers and students and have the AI ​​provide appropriate mental health care advice. This makes it possible to provide mental health care advice that is tailored to the emotions of teachers and students. Emotions include, but are not limited to, emotion recognition technology and evaluation criteria. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the Mental Health Support Department can input teacher and student emotion data into a generative AI and have the generative AI provide advice.

[0111] The Mental Health Support Department can analyze past mental health data to select the optimal support method when providing mental health support. For example, the Mental Health Support Department can use AI to analyze past mental health data and select the optimal support method. For example, the Mental Health Support Department can use AI to provide individualized support methods based on past mental health data. For example, the Mental Health Support Department can refer to past mental health data and have AI evaluate the effectiveness of support methods. This makes it possible to select the optimal support method based on past mental health data. Past mental health data includes, but is not limited to, data types and analysis methods. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Mental Health Support Department can input past mental health data into a generative AI and have the generative AI select support methods.

[0112] The Mental Health Support Department can identify individual stressors and take countermeasures when providing mental health support. For example, the Mental Health Support Department can use AI to identify stressors for teachers and students and propose countermeasures. For example, the Mental Health Support Department can analyze individual stressors and have the AI ​​provide appropriate support methods. For example, the Mental Health Support Department can monitor stressors and have the AI ​​take early countermeasures. This makes it possible to provide countermeasures based on individual stressors. Stressors include, but are not limited to, the causes of stress and countermeasures. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Mental Health Support Department can input data on the stressors of teachers and students into a generative AI and have the generative AI provide countermeasures.

[0113] The Mental Health Support Department can estimate the emotions of teachers and students and determine the priority of mental health support based on the estimated emotions. For example, the Mental Health Support Department can use AI to estimate the emotions of teachers and students and determine the priority of mental health support based on the estimated emotions. For example, if a teacher is feeling stressed, the Mental Health Support Department's AI will prioritize providing support. For example, if a student is feeling anxious, the Mental Health Support Department's AI will prioritize providing counseling. For example, the Mental Health Support Department can monitor the emotions of teachers and students and have the AI ​​determine the priority of support. This makes it possible to determine the priority of mental health support in accordance with the emotions of teachers and students. The priority of mental health support includes, but is not limited to, importance and urgency. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the Mental Health Support Department can input teacher and student emotion data into a generative AI and have the generative AI perform the priority determination.

[0114] The Mental Health Support Department can adjust its support methods when providing mental health support, taking into account the home environment and living situation. For example, the Mental Health Support Department can use AI to refer to the home environment of teachers and students and adjust support methods. For example, the Mental Health Support Department can use AI to adjust the content of support methods based on the living situation of teachers and students. For example, the Mental Health Support Department can use AI to provide the optimal support method, taking into account the home environment and living situation of teachers and students. This makes it possible to adjust support methods according to the home environment and living situation. The home environment and living situation include, but are not limited to, the family's economic situation and daily routine. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Mental Health Support Department can input data on the home environment and living situation of teachers and students into a generative AI and have the generative AI perform the adjustment of support methods.

[0115] The Mental Health Support Department can utilize local mental health resources when providing mental health support. For example, the Mental Health Support Department can use AI to refer to local mental health resources and provide support methods. For example, the Mental Health Support Department can use AI to adjust the content of support methods based on local mental health resources. For example, the Mental Health Support Department can use local mental health resources and have AI provide the optimal support method. This makes it possible to provide support that utilizes local mental health resources. Local mental health resources include, but are not limited to, counseling services and support organizations. Some or all of the above processing in the Mental Health Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Mental Health Support Department can input data on local mental health resources into generative AI and have the generative AI provide support methods.

[0116] The Communication Support Department can estimate the parent's emotions and adjust its response method based on the estimated emotions. For example, the Communication Support Department can use AI to estimate the parent's emotions and adjust its response method based on the estimated emotions. For example, if the parent is stressed, the AI ​​will respond using gentle language. For example, if the parent is relaxed, the AI ​​will provide detailed information. For example, if the parent is agitated, the AI ​​will provide a concise response. This makes it possible to adjust the response method to the parent's emotions. Parent's emotions include, but are not limited to, emotion recognition technology and evaluation criteria. Some or all of the above processing in the Communication Support Department may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the Communication Support Department can input parent's emotion data into a generative AI and have the generative AI adjust the response method to the inquiry.

[0117] The Communication Support Department can analyze past inquiry data to select the optimal response method when providing communication support. For example, the Communication Support Department can use AI to analyze past inquiry data and select the optimal response method. For example, the Communication Support Department can use AI to adjust the response method based on past inquiry data. For example, the Communication Support Department can refer to past inquiry data and use AI to maintain consistency in responses. This makes it possible to select the optimal response method based on past inquiry data. Past inquiry data includes, but is not limited to, data types and analysis methods. Some or all of the above processing in the Communication Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Communication Support Department can input past inquiry data into a generative AI and have the generative AI select a response method.

[0118] The Communication Support Department can take into account parents' concerns and requests when providing communication support. For example, the Communication Support Department can use AI to analyze parents' concerns and provide the most appropriate response. For example, the Communication Support Department can use AI to adjust the content of the response based on parents' requests. For example, the Communication Support Department can use AI to provide individualized response methods, taking into account parents' concerns and requests. This makes it possible to respond in accordance with parents' concerns and requests. Parents' concerns and requests include, but are not limited to, educational policies and details of requests. Some or all of the above processing in the Communication Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Communication Support Department can input data on parents' concerns and requests into a generative AI and have the generative AI adjust the response method.

[0119] The communication support department can estimate the parent's emotions and determine the priority of responding to inquiries based on the estimated emotions. For example, the communication support department can use AI to estimate the parent's emotions and determine the priority of responding to inquiries based on the estimated emotions. For example, if the parent is stressed, the communication support department will have the AI ​​prioritize responding. For example, if the parent is relaxed, the communication support department will have the AI ​​provide detailed information. For example, if the parent is agitated, the communication support department will have the AI ​​provide a concise response. This makes it possible to determine the priority of responding to inquiries according to the parent's emotions. The priority of responding to inquiries includes, but is not limited to, importance and urgency. Some or all of the above processing in the communication support department may be performed using, for example, an emotion engine or a generative AI, or it may be performed without using an emotion engine or a generative AI. For example, the communication support department can input parent emotion data into a generative AI and have the generative AI perform the priority determination.

[0120] The Communication Support Department can adjust its response methods when providing communication support, taking into account the parents' living situation and home environment. For example, the Communication Support Department may use AI to refer to the parents' living situation and adjust its response methods. For example, the Communication Support Department may use AI to adjust the content of its response methods based on the parents' home environment. For example, the Communication Support Department may use AI to provide the optimal response method, taking into account the parents' living situation and home environment. This makes it possible to adjust response methods according to the parents' living situation and home environment. The parents' living situation and home environment include, but are not limited to, the family's economic situation and daily routine. Some or all of the above processing in the Communication Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Communication Support Department may input data on the parents' living situation and home environment into a generative AI and have the generative AI perform the adjustment of the response method.

[0121] The Communication Support Department can refer to local educational policies and guidelines when providing communication support. For example, the Communication Support Department can use AI to refer to local educational policies and provide response methods. For example, the Communication Support Department can use AI to adjust the content of response methods based on local guidelines. For example, the Communication Support Department can consider local educational policies and guidelines and use AI to provide the optimal response method. This makes it possible to respond in accordance with local educational policies and guidelines. Local educational policies and guidelines include, but are not limited to, educational goals and curriculum guidelines. Some or all of the above processing in the Communication Support Department may be performed using, for example, generative AI, or without generative AI. For example, the Communication Support Department can input data on local educational policies and guidelines into generative AI and have the generative AI provide response methods.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The educational support system can also include a performance monitoring unit that monitors teachers' performance in real time and provides feedback. For example, the performance monitoring unit analyzes the teacher's tone of voice, speaking speed, gestures, etc., and evaluates the progress of the lesson and student reactions. This allows teachers to identify areas for improvement in real time during the lesson and improve the quality of their teaching. The performance monitoring unit also provides detailed feedback after the lesson, allowing teachers to specifically identify areas for improvement for the next lesson. Furthermore, the performance monitoring unit can analyze teachers' teaching styles and student reactions over the long term, supporting teacher growth.

[0124] Educational support systems can also incorporate gamification features to further enhance students' motivation to learn. For example, gamification features can provide a system where students earn points or badges each time they achieve a learning goal. This makes it easier for students to maintain their motivation to learn. Gamification features can also present learning content in a game format, allowing students to learn while having fun. Furthermore, gamification features can promote competition and cooperation among students, allowing them to share the enjoyment of learning.

[0125] The educational support system can also include an emotion-adaptive learning unit that estimates students' emotions and adjusts learning content based on those emotions. For example, if a student is feeling stressed, the emotion-adaptive learning unit can reduce the difficulty of the learning content and provide relaxing material. This allows students to continue learning without difficulty. Furthermore, if a student is agitated, the emotion-adaptive learning unit can provide more challenging tasks to increase their motivation. Additionally, if a student is relaxed, the emotion-adaptive learning unit can provide standard learning content, enabling them to learn efficiently.

[0126] The educational support system can also include an emotion-adaptive lesson support unit that estimates the teacher's emotions and supports the progress of the lesson based on those estimated emotions. For example, if the emotion-adaptive lesson support unit is feeling stressed, it can slow down the pace of the lesson and provide time for the teacher to relax. This allows the teacher to continue teaching without difficulty. Conversely, if the teacher is relaxed, the emotion-adaptive lesson support unit can speed up the pace of the lesson and conduct the lesson efficiently. Furthermore, if the teacher is tired, the emotion-adaptive lesson support unit can divide the lesson content into shorter segments so that it can be completed in a shorter time.

[0127] The educational support system can also include a predictive learning plan provider that forecasts future learning plans based on students' learning history. For example, the predictive learning plan provider analyzes students' past learning history and proposes future learning plans. This allows students to understand a learning plan that suits them in advance and proceed with their studies systematically. Furthermore, the predictive learning plan provider can forecast learning progress based on students' learning history and adjust the learning plan at the appropriate time. In addition, the predictive learning plan provider can provide plans to maximize learning effectiveness based on students' learning history.

[0128] The educational support system may also include an emotion monitoring unit that estimates students' emotions and monitors their learning progress based on those emotions. For example, if a student is feeling stressed, the emotion monitoring unit can slow down their learning progress and provide time for them to relax. This allows students to continue learning without undue pressure. Conversely, if a student is excited, the emotion monitoring unit can accelerate their learning progress to enable more efficient learning. Furthermore, if a student is relaxed, the emotion monitoring unit can maintain their normal learning progress, enabling them to learn effectively.

[0129] The educational support system can also include a lesson recording unit that automatically records teachers' lesson content for later reference. For example, the lesson recording unit can record teachers' lesson content in audio and video format, making it available for teachers and students to refer to later. This can be used to review and improve lessons. The lesson recording unit can also convert lesson content into text and provide it as a searchable database. Furthermore, the lesson recording unit can analyze lesson content and suggest areas for improvement to teachers.

[0130] The educational support system may also include an emotionally adaptive feedback unit that estimates students' emotions and provides learning feedback based on those estimated emotions. For example, if a student is feeling stressed, the emotionally adaptive feedback unit provides feedback in gentle language, making it easier for the student to accept the feedback. If a student is relaxed, the emotionally adaptive feedback unit provides detailed feedback, allowing them to specifically identify areas for improvement in their learning. Furthermore, if a student is agitated, the emotionally adaptive feedback unit can provide concise feedback, thereby increasing their motivation to learn.

[0131] The educational support system can also include a learning style adaptation unit that customizes learning content according to the student's learning style. For example, if a student is a visual learner, the learning style adaptation unit can provide learning materials that heavily utilize visual content. This allows students to learn in a way that suits them. Furthermore, if a student is an auditory learner, the learning style adaptation unit can provide learning materials that heavily utilize audio content. Additionally, if a student is an experiential learner, the learning style adaptation unit can provide practical assignments and projects.

[0132] The educational support system may also include an emotion-adaptive lesson feedback unit that estimates the teacher's emotions and provides lesson feedback based on those estimated emotions. For example, if the emotion-adaptive lesson feedback unit is feeling stressed, it will provide feedback in gentle language, making it easier for the teacher to accept the feedback. If the teacher is relaxed, the emotion-adaptive lesson feedback unit will provide detailed feedback, allowing for a concrete understanding of areas for improvement in the lesson. Furthermore, if the teacher is agitated, the emotion-adaptive lesson feedback unit will provide concise feedback, improving the quality of the lesson.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The Planning Support Department creates the lesson plan. For example, they use AI to automate curriculum creation and automatically generate lesson plans for each semester. The Planning Support Department creates the curriculum based on the lesson objectives, content, and progress schedule. Step 2: The grading department automatically grades exams and homework based on the lesson plans created by the planning support department. For example, it uses AI to scan students' answer sheets and grade them automatically. The grading department analyzes the content of the answer sheets and assigns points based on evaluation criteria. It uses a grading algorithm to evaluate the accuracy of the answer sheets. Step 3: The learning plan provision department provides individualized learning plans based on the scoring results obtained by the scoring department. For example, it may use AI to analyze students' learning history and level of understanding and create individualized learning plans based on the results. The learning plan provision department creates learning plans based on students' learning goals, learning content, and progress schedule, and provides plans to maximize learning effectiveness. Step 4: The Administrative Automation Department automates administrative tasks such as attendance management and grade management. For example, it uses AI to record student attendance in real time and automatically compile grades. The Administrative Automation Department manages attendance based on methods for recording attendance and calculating attendance rates, and manages grades based on methods for recording grades and evaluation criteria. Step 5: The Mental Health Support Department assesses and supports the mental health of teachers and students. For example, it uses AI to monitor the stress levels of teachers and students and provides mental health care advice as needed. The Mental Health Support Department assesses stress levels based on stress measurement methods and evaluation criteria, and provides mental health care advice such as counseling and relaxation methods.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the planning support unit, grading unit, learning plan provision unit, administrative automation unit, and mental health support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the planning support unit is implemented by the control unit 46A of the smart device 14 and assists in the creation of lesson plans. The grading unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs automatic grading of exams and homework. The learning plan provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides individualized learning plans optimized for each student. The administrative automation unit is implemented by, for example, the control unit 46A of the smart device 14 and automates administrative tasks such as attendance management and grade management. The mental health support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates and supports the mental health of teachers and students. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the planning support unit, grading unit, learning plan provision unit, administrative automation unit, and mental health support unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the planning support unit is implemented by the control unit 46A of the smart glasses 214 and assists in the creation of lesson plans. The grading unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs automatic grading of exams and homework. The learning plan provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides individualized learning plans optimized for each student. The administrative automation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and automates administrative tasks such as attendance management and grade management. The mental health support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates and supports the mental health of teachers and students. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the planning support unit, grading unit, learning plan provision unit, administrative automation unit, and mental health support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the planning support unit is implemented by the control unit 46A of the headset terminal 314 and assists in the creation of lesson plans. The grading unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs automatic grading of exams and homework. The learning plan provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides individualized learning plans optimized for each student. The administrative automation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automates administrative tasks such as attendance management and grade management. The mental health support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates and supports the mental health of teachers and students. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the planning support unit, grading unit, learning plan provision unit, administrative automation unit, and mental health support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the planning support unit is implemented by the control unit 46A of the robot 414 and assists in the creation of lesson plans. The grading unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs automatic grading of exams and homework. The learning plan provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides individualized learning plans optimized for each student. The administrative automation unit is implemented by, for example, the control unit 46A of the robot 414 and automates administrative tasks such as attendance management and grade management. The mental health support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates and supports the mental health of teachers and students. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) The Planning Support Department, which creates lesson plans, A scoring unit that automatically grades exams and homework based on the lesson plans created by the aforementioned planning support unit, A learning plan provision unit provides an individual learning plan based on the scoring results obtained by the scoring unit, The Administrative Automation Department automates administrative tasks such as attendance management and grade management, It includes a Mental Health Support Department that assesses and supports the mental health of teachers and students. A system characterized by the following features. (Note 2) The aforementioned planning support department, Automate curriculum creation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The scoring unit is, Scan and automatically grade your answers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning plan provision unit, We analyze learning history and comprehension levels to create individualized learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned business automation unit, Automate attendance management and grade management. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Mental Health Support Department We provide stress level monitoring and mental health care advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) It has a communication support department that automatically responds to inquiries from parents and provides feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned planning support department, The system estimates the teacher's emotions and adjusts the timing of lesson planning based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned planning support department, We analyze past lesson plan data to select the optimal method for creating the curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned planning support department, When creating lesson plans, adjust them to take into account the students' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned planning support department, The system estimates the teacher's emotions and prioritizes lesson plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned planning support department, When creating lesson plans, adjust them to take into account the school's annual event schedule. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned planning support department, When creating lesson plans, refer to local educational policies and curriculum guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 14) The scoring unit is, The system estimates students' emotions and adjusts the feedback method for grading results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The scoring unit is, During the scoring process, the scoring algorithm is optimized by referring to past scoring data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The scoring unit is, During grading, we analyze students' response patterns to improve grading accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The scoring unit is, The system estimates students' emotions and adjusts how the scoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The scoring unit is, When grading, adjust the grading criteria to take into account the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The scoring unit is, When grading, refer to the grading criteria of other teachers to maintain grading consistency. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning plan provision unit, The system estimates students' emotions and adjusts the timing of providing learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning plan provision unit, When providing a learning plan, we analyze the student's past learning data to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning plan provision unit, When providing a learning plan, customize the plan according to the student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning plan provision unit, The system estimates students' emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning plan provision unit, When providing a study plan, we adjust it to take into account the student's home environment and living situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning plan provision unit, When providing a learning plan, we create the plan based on the student's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned business automation unit, It estimates the teacher's emotions and adjusts the timing of automating administrative tasks based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned business automation unit, When automating administrative tasks, past administrative data is analyzed to select the optimal automation method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned business automation unit, When automating administrative tasks, the school's operating policies and regulations should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned business automation unit, The system estimates the teacher's emotions and prioritizes administrative tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned business automation unit, When automating administrative tasks, we improve the accuracy of the automation by referencing administrative data from other schools. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned business automation unit, When automating administrative tasks, the local education administration's policies should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Mental Health Support Department It estimates the emotions of teachers and students and provides mental health care advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned Mental Health Support Department When providing mental health support, we analyze past mental health data to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned Mental Health Support Department When providing mental health support, identify individual stressors and take appropriate measures. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned Mental Health Support Department The system estimates the emotions of teachers and students and prioritizes mental health support based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned Mental Health Support Department When providing mental health support, we adjust the support methods while taking into account the family environment and living situation. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned Mental Health Support Department When providing mental health support, utilize local mental health resources. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned Communication Support Department We estimate the parent's emotions and adjust the way we respond to inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned Communication Support Department When providing communication support, we analyze past inquiry data to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned Communication Support Department When providing communication support, we take into consideration the parents' interests and requests. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned Communication Support Department The system estimates the parents' emotions and determines the priority of responding to inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned Communication Support Department When providing communication support, we adjust our approach considering the parents' living situation and home environment. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned Communication Support Department When providing communication support, we will refer to local educational policies and guidelines. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 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 Planning Support Department, which creates lesson plans, A scoring unit that automatically grades exams and homework based on the lesson plans created by the aforementioned planning support unit, A learning plan provision unit provides an individual learning plan based on the scoring results obtained by the scoring unit, The Administrative Automation Department automates administrative tasks such as attendance management and grade management, It includes a Mental Health Support Department that assesses and supports the mental health of teachers and students. A system characterized by the following features.

2. The aforementioned planning support department, Automate curriculum creation. The system according to feature 1.

3. The scoring unit is, Scan and automatically grade your answers. The system according to feature 1.

4. The aforementioned learning plan provision unit, We analyze learning history and comprehension levels to create individualized learning plans. The system according to feature 1.

5. The aforementioned business automation unit, Automate attendance management and grade management. The system according to feature 1.

6. The aforementioned Mental Health Support Department We provide stress level monitoring and mental health care advice. The system according to feature 1.

7. It has a communication support department that automatically responds to inquiries from parents and provides feedback. The system according to feature 1.

8. The aforementioned planning support department, The system estimates the teacher's emotions and adjusts the timing of lesson planning based on those estimated emotions. The system according to feature 1.

9. The aforementioned planning support department, We analyze past lesson plan data to select the optimal method for creating the curriculum. The system according to feature 1.

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