system

Abstract

The system according to the embodiment aims to reduce the workload of teachers and maintain consistency in the evaluation of school report marks. [Solution] The system according to the embodiment includes a support unit, an analysis unit, an evaluation unit, and a monitoring unit. The support unit supports teachers in their work. The analysis unit analyzes students' academic performance data based on the work supported by the support unit. The evaluation unit calculates school report scores based on the data analyzed by the analysis unit. The monitoring unit continuously monitors the school report scores calculated by the evaluation unit and reviews them based on specific conditions.

Description

[Technical Field]

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

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

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

[0004] Conventional technology places a heavy workload on teachers and can lead to variations in the assessment of school reports.

[0005] The system according to the embodiment aims to reduce the workload of teachers and maintain consistency in the evaluation of school report marks. [Means for solving the problem]

[0006] The system according to the embodiment includes a support unit, an analysis unit, an evaluation unit, and a monitoring unit. The support unit supports teachers in their work. The analysis unit analyzes students' academic performance data based on the work supported by the support unit. The evaluation unit calculates school report scores based on the data analyzed by the analysis unit. The monitoring unit continuously monitors the school report scores calculated by the evaluation unit and reviews them based on specific conditions. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the workload of teachers and maintain consistency in the evaluation of school report marks. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) An educational support system according to an embodiment of the present invention utilizes AI to reduce teachers' workloads and eliminate variance in school report scores. In this educational support system, AI supports teachers while they are busy with classes and other tasks. Next, AI analyzes students' grade data and fairly evaluates their grade scores. This system reduces teachers' workloads and eliminates variance in school report scores. For example, AI supports teachers while they are busy with classes and other tasks. For example, AI can assist with lesson preparation and the creation of teaching materials, thereby reducing teachers' workload. Furthermore, AI can manage students' attendance and assignment submission status, thereby reducing the time teachers spend on these tasks. Next, AI analyzes students' grade data and fairly evaluates their grade scores. Specifically, AI calculates grade scores based on students' test results and assignment evaluations using a machine learning algorithm. This eliminates variance in evaluations among teachers and achieves fair evaluations. For example, AI can analyze students' test results and calculate grade scores based on each student's score. AI can also evaluate students' assignments and reflect the results in their school report scores. Furthermore, AI can continuously monitor students' academic performance data and revise their school report scores as necessary. For example, AI can detect fluctuations in students' grades and reevaluate their school report scores, enabling fair evaluations based on the most recent grades. This system reduces teachers' workload and eliminates inconsistencies in school report scores. Teachers can focus on their classes and other tasks, improving the quality of their instruction. Students also receive fair evaluations, which increases their motivation to learn. For example, AI can assist with lesson preparation, allowing teachers more time to concentrate on lessons and improving the quality of their instruction. Furthermore, AI's fair evaluation of school report scores allows students to feel that their efforts are being fairly evaluated, increasing their motivation to learn. In this way, the educational support system can reduce teachers' workload and eliminate inconsistencies in school report scores.

[0029] An education support system according to an embodiment includes a support unit, an analysis unit, an evaluation unit, and a monitoring unit. The support unit supports teachers in their work. Teachers' work includes, but is not limited to, lesson preparation, grade evaluation, and assignment management. For example, the support unit supports lesson preparation and the creation of teaching materials. The support unit can use AI to collect materials necessary for lesson preparation and create teaching materials. For example, the support unit can collect information from the internet and provide materials appropriate for the lesson. The support unit can also analyze past lesson data and propose optimal lesson plans. Furthermore, the support unit manages students' attendance and assignment submission status. The support unit can use AI to monitor students' attendance in real time and automatically update attendance records. For example, the support unit can capture students' attendance with a camera and confirm attendance using facial recognition technology. The support unit can also manage students' assignment submission status and send reminders for assignments that are past their deadline. The analysis unit analyzes students' grade data based on the work supported by the support unit. The academic performance data includes, but is not limited to, test scores, assignment evaluations, and attendance. The analysis unit uses AI to analyze the student's academic performance data and provide basic data for calculating the school report score. For example, the analysis unit analyzes the student's test results and calculates the score distribution for each student. The analysis unit can also evaluate the student's assignments and reflect the results in the academic performance data. The evaluation unit calculates the school report score based on the data analyzed by the analysis unit. The evaluation unit uses a machine learning algorithm to fairly calculate the school report score. For example, the evaluation unit applies an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation unit can also continuously monitor the student's academic performance data and revise the school report score as necessary. The monitoring unit continuously monitors the school report score calculated by the evaluation unit and revise it based on specific conditions. The specific conditions include, but are not limited to, changes in grades and attendance. The monitoring unit can use AI to monitor the student's academic performance data in real time and issue an alert if a review of the school report score is necessary.For example, the monitoring unit reevaluates the student's school report score if there is a sudden change in the student's grades. The monitoring unit can also review the student's school report score if there is a change in the student's attendance. This allows the education support system according to the embodiment to reduce the burden on teachers and eliminate variations in school report scores.

[0030] The support unit can support lesson preparation or teaching material creation. For example, the support unit collects materials necessary for lesson preparation and creates teaching materials. The support unit uses AI to collect information on the internet and provide materials appropriate for the lesson. For example, the support unit analyzes past lesson data and proposes optimal lesson plans. The support unit can also customize the content of teaching materials according to the progress of the lesson. For example, the support unit monitors the progress of the lesson in real time and modifies the content of the teaching materials as needed. This reduces the burden on teachers in lesson preparation and teaching material creation. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without AI. For example, the support unit can use AI to search information on the internet to collect materials necessary for lesson preparation and provide relevant materials. In addition, the support unit can use AI to analyze past lesson data and propose optimal lesson plans when creating teaching materials. Furthermore, the support unit can use AI to customize the content of the teaching materials according to the progress of the lesson. For example, the support unit can use AI to monitor the progress of the lesson in real time and modify the content of the teaching materials as needed. This allows the support department to reduce the burden on teachers in preparing lessons and creating teaching materials.

[0031] The support unit can manage students' attendance or assignment submission status. For example, the support unit monitors students' attendance in real time and automatically updates the attendance record. The support unit uses AI to capture students' attendance with a camera and confirm attendance using facial recognition technology. For example, the support unit captures students' attendance with a camera and confirms attendance using facial recognition technology. The support unit can also manage students' assignment submission status and send reminders for assignments that are past their deadline. For example, the support unit manages students' assignment submission status and sends reminders for assignments that are past their deadline. This reduces the workload of teachers and enables efficient management. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without AI. For example, the support unit can use AI to monitor students' attendance in real time and automatically update the attendance record. The support unit can also use AI to manage students' assignment submission status and send reminders for assignments that are past their deadline. This will enable the support department to reduce the workload of teachers and enable efficient management.

[0032] The analysis unit can analyze the grade data based on the students' test results or assignment evaluations. The analysis unit, for example, analyzes the students' test results and calculates the score distribution for each student. The analysis unit can analyze the students' test results and calculate the score distribution for each student using AI. For example, the analysis unit can analyze the students' test results and calculate the score distribution for each student. The analysis unit can also evaluate the students' assignments and reflect the results in the grade data. For example, the analysis unit can evaluate the students' assignments and reflect the results in the grade data. This allows the student grade data to be accurately analyzed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze the students' test results and calculate the score distribution for each student. The analysis unit can also use AI to evaluate the students' assignments and reflect the results in the grade data. This allows the analysis unit to accurately analyze the student grade data.

[0033] The evaluation unit can calculate the school report score using a machine learning algorithm. For example, the evaluation unit applies an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation unit uses AI to apply an algorithm to calculate the school report score based on the student's test results and assignment evaluations. For example, the evaluation unit applies an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation unit can also continuously monitor the student's academic performance data and revise the school report score as necessary. For example, the evaluation unit can continuously monitor the student's academic performance data and revise the school report score as necessary. This allows the calculation of the school report score to be fair and accurate. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without AI. For example, the evaluation unit can use AI to apply an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation unit can also use AI to continuously monitor the student's academic performance data and revise the school report score as necessary. This allows the evaluation unit to calculate the school report score to be fair and accurate.

[0034] The monitoring unit can continuously monitor student performance data and revise school report scores based on specific conditions. For example, the monitoring unit can monitor student performance data in real time and issue an alert when a review of school report scores is necessary. The monitoring unit can use AI to monitor student performance data in real time and issue an alert when a review of school report scores is necessary. For example, the monitoring unit can reevaluate school report scores when a student's performance suddenly changes. The monitoring unit can also revise school report scores when a student's attendance status changes. This ensures that school report scores are reviewed in a timely manner and fair evaluations are maintained. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI or without AI. For example, the monitoring unit can use AI to monitor student performance data in real time and issue an alert when a review of school report scores is necessary. The monitoring unit can also use AI to reevaluate school report scores when a student's performance suddenly changes. Furthermore, the monitoring department can use AI to review grades if a student's attendance status changes, ensuring that grades are reviewed in a timely manner and maintaining fair evaluation.

[0035] The support unit can analyze a teacher's past lesson preparation history and suggest an appropriate teaching material creation method. For example, the support unit can analyze the effectiveness of teaching materials used by the teacher in the past and suggest the most effective teaching material creation method. The support unit can use AI to analyze a teacher's past lesson preparation history and suggest an appropriate teaching material creation method. For example, the support unit can analyze the effectiveness of teaching materials used by the teacher in the past and suggest the most effective teaching material creation method. The support unit can also suggest areas for improvement based on feedback from the teacher's past lessons. Furthermore, the support unit can analyze the types and formats of teaching materials used by the teacher in the past and suggest the optimal combination. This can reduce the teacher's burden by suggesting the optimal teaching material creation method based on the teacher's past lesson preparation history. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without AI. For example, the support unit can use AI to analyze a teacher's past lesson preparation history and suggest an appropriate teaching material creation method. The support unit can also use AI to analyze the effectiveness of teaching materials used by the teacher in the past and suggest the most effective teaching material creation method. The support unit can also use AI to suggest areas for improvement based on feedback from the teacher's past lessons. This allows the support department to reduce the burden on teachers by suggesting the most appropriate way to create teaching materials based on past lesson preparation history.

[0036] The support unit can customize the content of teaching materials based on the teacher's current lesson progress when preparing for a lesson. For example, the support unit customizes the teaching materials to be used in the next lesson based on the content of the lesson the teacher is currently teaching. The support unit uses AI to customize the content of teaching materials based on the teacher's current lesson progress when preparing for a lesson. For example, the support unit customizes the teaching materials to be used in the next lesson based on the content of the lesson the teacher is currently teaching. The support unit can also suggest related teaching materials based on topics covered by the teacher in the lesson. Furthermore, the support unit can analyze the effectiveness of the teaching materials used by the teacher in the lesson and suggest areas for improvement in the next lesson. This makes it possible to customize teaching materials according to the lesson progress. Some or all of the above-mentioned processing in the support unit may be performed using AI or without AI. For example, the support unit can use AI to customize the content of teaching materials based on the teacher's current lesson progress when preparing for a lesson. Furthermore, the support unit can use AI to customize the content of teaching materials based on the teacher's current lesson progress. Furthermore, the support unit can use AI to customize the teaching materials to be used in the next lesson based on the content of the lesson the teacher is currently teaching. Furthermore, the support unit can use AI to suggest related teaching materials based on topics covered by the teacher in the lesson. This allows the support department to customize teaching materials according to the progress of the class.

[0037] The support unit can create region-specific teaching materials based on the geographical location information of the teacher when preparing for a lesson. For example, if a teacher is teaching a lesson in a specific region, the support unit suggests region-specific teaching materials. The support unit uses AI to create region-specific teaching materials based on the geographical location information of the teacher when preparing for a lesson. For example, if a teacher is teaching a lesson in a specific region, the support unit suggests region-specific teaching materials. The support unit can also use AI to provide relevant information when a teacher is creating teaching materials based on the culture or history of the region. Furthermore, the support unit can also use AI to suggest optimal materials when a teacher is creating teaching materials that take the characteristics of the region into consideration. This makes it possible to create region-specific teaching materials. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can use AI to create region-specific teaching materials based on the geographical location information of the teacher when preparing for a lesson. Furthermore, if a teacher is teaching a lesson in a specific region, the support unit can use AI to suggest region-specific teaching materials. Furthermore, the support unit can use AI to provide relevant information when a teacher is creating teaching materials based on the culture or history of the region. This will enable the support department to create educational materials that are specific to the region.

[0038] The support unit can analyze the social media activity of the teacher when preparing for a lesson and suggest relevant teaching materials. For example, the support unit can suggest relevant teaching materials based on information shared by the teacher on social media. The support unit can use AI to analyze the social media activity of the teacher when preparing for a lesson and suggest relevant teaching materials. For example, the support unit can suggest relevant teaching materials based on information shared by the teacher on social media. The support unit can also customize teaching materials based on information about accounts the teacher follows on social media. The support unit can also suggest areas for improving the teaching materials based on feedback the teacher receives on social media. This makes it possible to suggest teaching materials based on social media activity. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can use AI to analyze the social media activity of the teacher when preparing for a lesson and suggest relevant teaching materials. The support unit can also use AI to customize teaching materials based on information about accounts the teacher follows on social media. The support unit can also use AI to suggest areas for improving the teaching materials based on feedback the teacher receives on social media. This makes it possible to suggest teaching materials based on social media activity.

[0039] The analysis unit can analyze the student's past academic performance data and set appropriate evaluation criteria. The analysis unit can, for example, set optimal evaluation criteria based on the student's past academic performance data. The analysis unit can use AI to analyze the student's past academic performance data and set appropriate evaluation criteria. For example, the analysis unit can set optimal evaluation criteria based on the student's past academic performance data. The analysis unit can also analyze the student's academic performance trends and adjust the evaluation criteria. Furthermore, the analysis unit can set individual evaluation criteria based on the student's past academic performance data. This makes it possible to set optimal evaluation criteria based on the past academic performance data. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without using AI. For example, the analysis unit can use AI to analyze the student's past academic performance data and set appropriate evaluation criteria. The analysis unit can also use AI to analyze the student's academic performance trends and adjust the evaluation criteria. Furthermore, the analysis unit can use AI to set individual evaluation criteria based on the student's past academic performance data. This makes it possible for the analysis unit to set optimal evaluation criteria based on the past academic performance data.

[0040] The analysis unit can weight the data based on the student's current learning situation when analyzing the grade data. The analysis unit, for example, weights the grade data based on the student's current learning situation. The analysis unit uses AI to weight the data based on the student's current learning situation when analyzing the grade data. For example, the analysis unit weights the grade data based on the student's current learning situation. The analysis unit can also adjust the weighting of the grade data taking into account the student's learning progress. Furthermore, the analysis unit can weight the grade data by reflecting the student's current learning situation in real time. This makes it possible to weight the grade data according to the current learning situation. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to weight the data based on the student's current learning situation when analyzing the grade data. The analysis unit can also use AI to weight the grade data based on the student's current learning situation. The analysis unit can also use AI to adjust the weighting of the grade data taking into account the student's learning progress. This allows the analysis unit to weight the grade data according to the current learning situation.

[0041] The analysis unit can perform region-specific evaluations based on the student's geographical location information when analyzing the academic performance data. For example, if a student lives in a specific region, the analysis unit performs evaluations taking into account the characteristics of the region. The analysis unit uses AI to perform region-specific evaluations based on the student's geographical location information when analyzing the academic performance data. For example, if a student lives in a specific region, the analysis unit performs evaluations taking into account the characteristics of the region. The analysis unit can also set region-specific evaluation criteria based on the student's geographical location information. Furthermore, the analysis unit can evaluate the academic performance data taking into account the educational environment of the student's region. This enables region-specific evaluations. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can use AI to perform region-specific evaluations based on the student's geographical location information when analyzing the academic performance data. Furthermore, if a student lives in a specific region, the analysis unit can use AI to perform evaluations taking into account the characteristics of the region. Furthermore, the analysis unit can use AI to set region-specific evaluation criteria based on the student's geographical location information. This enables region-specific evaluations.

[0042] The analysis unit can analyze students' social media activities when analyzing the academic performance data and complement the relevant data. The analysis unit, for example, complements the academic performance data based on students' social media activities. The analysis unit can use AI to analyze students' social media activities when analyzing the academic performance data and complement the relevant data. For example, the analysis unit complements the academic performance data based on students' social media activities. The analysis unit can also complement the academic performance data based on information shared by students on social media. The analysis unit can also analyze students' social media activities and reflect them in the evaluation of the academic performance data. This makes it possible to complement the academic performance data based on social media activities. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze students' social media activities when analyzing the academic performance data and complement the relevant data. The analysis unit can also use AI to complement the academic performance data based on students' social media activities. The analysis unit can also use AI to complement the academic performance data based on information shared by students on social media. This makes it possible for the analysis unit to complement the academic performance data based on social media activities.

[0043] The evaluation unit can apply an appropriate evaluation algorithm by referring to the student's past academic performance data when calculating the internal assessment score. The evaluation unit, for example, applies an optimal evaluation algorithm based on the student's past academic performance data. The evaluation unit uses AI to apply an appropriate evaluation algorithm by referring to the student's past academic performance data when calculating the internal assessment score. For example, the evaluation unit can apply an optimal evaluation algorithm based on the student's past academic performance data. The evaluation unit can also analyze trends in the student's academic performance and adjust the evaluation algorithm. The evaluation unit can also apply an individual evaluation algorithm based on the student's past academic performance data. This makes it possible to apply an optimal evaluation algorithm based on the past academic performance data. Some or all of the above-described processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can use AI to apply an appropriate evaluation algorithm by referring to the student's past academic performance data when calculating the internal assessment score. The evaluation unit can also use AI to analyze trends in the student's academic performance and adjust the evaluation algorithm. The evaluation unit can also use AI to apply an individual evaluation algorithm based on the student's past academic performance data. This allows the evaluation unit to apply an optimal evaluation algorithm based on past performance data.

[0044] The evaluation unit can customize the evaluation criteria based on the student's current learning situation when calculating the internal assessment score. The evaluation unit customizes the evaluation criteria based on, for example, the student's current learning situation. The evaluation unit uses AI to customize the evaluation criteria based on the student's current learning situation when calculating the internal assessment score. For example, the evaluation unit customizes the evaluation criteria based on the student's current learning situation. The evaluation unit can also adjust the evaluation criteria taking into account the student's learning progress. Furthermore, the evaluation unit can customize the evaluation criteria by reflecting the student's current learning situation in real time. This makes it possible to customize the evaluation criteria according to the current learning situation. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can use AI to customize the evaluation criteria based on the student's current learning situation when calculating the internal assessment score. Furthermore, the evaluation unit can use AI to customize the evaluation criteria based on the student's current learning situation. Furthermore, the evaluation unit can use AI to adjust the evaluation criteria taking into account the student's learning progress. This allows the evaluation unit to customize the evaluation criteria according to the current learning situation.

[0045] The evaluation unit can perform a region-specific evaluation based on the student's geographical location information when calculating the school report score. For example, if the student lives in a specific region, the evaluation unit performs the evaluation taking into account the characteristics of the region. The evaluation unit uses AI to perform a region-specific evaluation based on the student's geographical location information when calculating the school report score. For example, if the student lives in a specific region, the evaluation unit performs the evaluation taking into account the characteristics of the region. The evaluation unit can also set region-specific evaluation criteria based on the student's geographical location information. Furthermore, the evaluation unit can evaluate the school report score taking into account the educational environment of the student's region. This enables a region-specific evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without AI. For example, the evaluation unit can use AI to perform a region-specific evaluation based on the student's geographical location information when calculating the school report score. Furthermore, if the student lives in a specific region, the evaluation unit can use AI to perform the evaluation taking into account the characteristics of the region. Furthermore, the evaluation unit can use AI to set region-specific evaluation criteria based on the student's geographical location information. This allows the evaluation unit to perform region-specific evaluations.

[0046] The evaluation unit can analyze a student's social media activity and supplement relevant data when calculating the school report score. The evaluation unit, for example, supplements the school report score based on the student's social media activity. The evaluation unit can use AI to analyze a student's social media activity and supplement relevant data when calculating the school report score. For example, the evaluation unit supplements the school report score based on the student's social media activity. The evaluation unit can also supplement the school report score based on information shared by the student on social media. Furthermore, the evaluation unit can analyze the student's social media activity and reflect it in the evaluation of the school report score. This makes it possible to supplement the school report score based on social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can use AI to analyze a student's social media activity and supplement relevant data when calculating the school report score. The evaluation unit can also use AI to supplement the school report score based on the student's social media activity. Furthermore, the evaluation unit can use AI to supplement the school report score based on information shared by the student on social media. This will allow the evaluation department to supplement school grades based on social media activity.

[0047] The monitoring unit can set an appropriate monitoring method by referring to the student's past academic performance data during monitoring. The monitoring unit, for example, sets an optimal monitoring method based on the student's past academic performance data. The monitoring unit uses AI to set an appropriate monitoring method by referring to the student's past academic performance data during monitoring. For example, the monitoring unit sets an optimal monitoring method based on the student's past academic performance data. The monitoring unit can also analyze trends in the student's academic performance and adjust the monitoring method. The monitoring unit can also set an individual monitoring method based on the student's past academic performance data. This makes it possible to set an optimal monitoring method based on the past academic performance data. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can use AI to set an appropriate monitoring method by referring to the student's past academic performance data during monitoring. The monitoring unit can also use AI to analyze trends in the student's academic performance and adjust the monitoring method. The monitoring unit can also use AI to set an individual monitoring method based on the student's past academic performance data. This makes it possible to set an optimal monitoring method based on the past academic performance data.

[0048] The monitoring unit can weight the data based on the student's current learning situation during monitoring. The monitoring unit, for example, weights the monitoring data based on the student's current learning situation. The monitoring unit uses AI to weight the data based on the student's current learning situation during monitoring. For example, the monitoring unit weights the monitoring data based on the student's current learning situation. The monitoring unit can also adjust the weighting of the monitoring data taking into account the student's learning progress. Furthermore, the monitoring unit can weight the monitoring data by reflecting the student's current learning situation in real time. This enables the monitoring data to be weighted according to the current learning situation. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI or may be performed without using AI. For example, the monitoring unit can use AI to weight the data based on the student's current learning situation during monitoring. Furthermore, the monitoring unit can use AI to weight the monitoring data based on the student's current learning situation. Furthermore, the monitoring unit can use AI to adjust the weighting of the monitoring data taking into account the student's learning progress. This enables the monitoring unit to weight the monitoring data according to the current learning situation.

[0049] The monitoring unit can perform region-specific monitoring based on the student's geographical location information during monitoring. For example, if the student lives in a specific area, the monitoring unit performs monitoring taking into account the characteristics of the area. The monitoring unit uses AI to perform region-specific monitoring based on the student's geographical location information during monitoring. For example, if the student lives in a specific area, the monitoring unit performs monitoring taking into account the characteristics of the area. The monitoring unit can also set region-specific monitoring criteria based on the student's geographical location information. Furthermore, the monitoring unit can perform monitoring taking into account the educational environment of the student's area. This enables region-specific monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can use AI to perform region-specific monitoring based on the student's geographical location information during monitoring. Furthermore, if the student lives in a specific area, the monitoring unit can use AI to perform monitoring taking into account the characteristics of the area. Furthermore, the monitoring unit can use AI to set region-specific monitoring criteria based on the student's geographical location information. This enables region-specific monitoring.

[0050] The monitoring unit can analyze students' social media activities during monitoring and complement related data. The monitoring unit, for example, complements the monitoring data based on students' social media activities. The monitoring unit can use AI to analyze students' social media activities during monitoring and complement related data. For example, the monitoring unit complements the monitoring data based on students' social media activities. The monitoring unit can also complement the monitoring data based on information shared by students on social media. Furthermore, the monitoring unit can analyze students' social media activities and reflect them in the evaluation of the monitoring data. This enables the monitoring data to be complemented based on social media activities. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can use AI to analyze students' social media activities during monitoring and complement related data. The monitoring unit can also use AI to complement the monitoring data based on students' social media activities. Furthermore, the monitoring unit can use AI to complement the monitoring data based on information shared by students on social media. This enables the monitoring unit to complement the monitoring data based on social media activities.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The support department can monitor teachers' health status and adjust their work load based on their health condition. For example, if a teacher is feeling unwell, the AI ​​can reduce their work load and prioritize easy tasks. Also, if a teacher is in good health, the AI ​​can assign them regular work. Furthermore, the support department can suggest breaks and adjust work schedules based on the teacher's health condition. This makes it possible to adjust a teacher's work load based on their health condition.

[0053] The evaluation unit can set learning goals for students and adjust the calculation method of school report marks based on the set learning goals. For example, if a student is aiming for high grades in a specific subject, the evaluation unit can have the AI ​​calculate school report marks by placing emphasis on grades in that subject. Alternatively, if a student is aiming to improve their overall grades, the evaluation unit can have the AI ​​evaluate grades in all subjects equally. Furthermore, if a student is aiming to acquire a specific skill, the evaluation unit can calculate school report marks by placing emphasis on grades related to that skill. This makes it possible to calculate school report marks according to the student's learning goals.

[0054] The monitoring unit can evaluate the student's learning environment and adjust the monitoring method based on the evaluated learning environment. For example, if the student is studying in a quiet environment, the AI ​​can apply a normal monitoring method. If the student is studying in a noisy environment, the AI ​​can increase the frequency of monitoring. Furthermore, if the student is studying online, the AI ​​can also apply a monitoring method specific to online learning. This makes it possible to adjust the monitoring method to suit the student's learning environment.

[0055] The analysis unit can analyze the student's learning history and suggest an appropriate learning approach. For example, the analysis unit can identify learning methods that the student has had success with in the past and suggest a similar approach. The analysis unit can also suggest that the student avoid learning methods that the student has had difficulty with in the past. Furthermore, the analysis unit can also suggest that the student try a new learning method based on the student's learning history. This makes it possible to suggest an appropriate learning approach based on the student's learning history.

[0056] The monitoring unit can track students' learning progress in real time and adjust the learning plan based on the progress. For example, if the student is progressing faster than planned, the AI ​​can advance the learning plan. Also, if the student is falling behind, the AI ​​can extend the learning plan. Furthermore, the monitoring unit can adjust the learning content according to the student's progress and propose the optimal learning plan. This makes it possible to adjust the learning plan according to the student's learning progress.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The support department supports teachers in their work. Specifically, it assists with lesson preparation, grading, assignment management, etc. The support department uses AI to collect materials necessary for lesson preparation and create teaching materials. It also analyzes past lesson data and proposes optimal lesson plans. It also manages student attendance and assignment submission status and automatically updates the attendance register. For example, it uses a camera to capture students' attendance and uses facial recognition technology to confirm attendance. It also manages assignment submission status and sends reminders for assignments that have passed their submission deadline. Step 2: The analysis department analyzes the students' academic performance data based on the work supported by the support department. The academic performance data includes test scores, assignment evaluations, attendance, etc. The analysis department uses AI to analyze the students' academic performance data and provide basic data for calculating school grades. For example, it analyzes students' test results and calculates the score distribution for each student. It also evaluates students' assignments and reflects the results in the academic performance data. Step 3: The evaluation department calculates the school report score based on the data analyzed by the analysis department. The evaluation department uses machine learning algorithms to fairly calculate the school report score. For example, the evaluation department applies an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation department can also continuously monitor the student's academic performance data and revise the school report score as necessary. Step 4: The monitoring department continuously monitors the assessment scores calculated by the evaluation department and reviews them based on specific conditions. These conditions include fluctuations in grades and changes in attendance. The monitoring department uses AI to monitor student performance data in real time and issues alerts when a review of the assessment score is necessary. For example, if a student's grades change suddenly or their attendance status changes, the assessment score will be reevaluated.

[0059] (Example 2) An educational support system according to an embodiment of the present invention utilizes AI to reduce teachers' workloads and eliminate variance in school report scores. In this educational support system, AI supports teachers while they are busy with classes and other tasks. Next, AI analyzes students' grade data and fairly evaluates their grade scores. This system reduces teachers' workloads and eliminates variance in school report scores. For example, AI supports teachers while they are busy with classes and other tasks. For example, AI can assist with lesson preparation and the creation of teaching materials, thereby reducing teachers' workload. Furthermore, AI can manage students' attendance and assignment submission status, thereby reducing the time teachers spend on these tasks. Next, AI analyzes students' grade data and fairly evaluates their grade scores. Specifically, AI calculates grade scores based on students' test results and assignment evaluations using a machine learning algorithm. This eliminates variance in evaluations among teachers and achieves fair evaluations. For example, AI can analyze students' test results and calculate grade scores based on each student's score. AI can also evaluate students' assignments and reflect the results in their school report scores. Furthermore, AI can continuously monitor students' academic performance data and revise their school report scores as necessary. For example, AI can detect fluctuations in students' grades and reevaluate their school report scores, enabling fair evaluations based on the most recent grades. This system reduces teachers' workload and eliminates inconsistencies in school report scores. Teachers can focus on their classes and other tasks, improving the quality of their instruction. Students also receive fair evaluations, which increases their motivation to learn. For example, AI can assist with lesson preparation, allowing teachers more time to concentrate on lessons and improving the quality of their instruction. Furthermore, AI's fair evaluation of school report scores allows students to feel that their efforts are being fairly evaluated, increasing their motivation to learn. In this way, the educational support system can reduce teachers' workload and eliminate inconsistencies in school report scores.

[0060] An education support system according to an embodiment includes a support unit, an analysis unit, an evaluation unit, and a monitoring unit. The support unit supports teachers in their work. Teachers' work includes, but is not limited to, lesson preparation, grade evaluation, and assignment management. For example, the support unit supports lesson preparation and the creation of teaching materials. The support unit can use AI to collect materials necessary for lesson preparation and create teaching materials. For example, the support unit can collect information from the internet and provide materials appropriate for the lesson. The support unit can also analyze past lesson data and propose optimal lesson plans. Furthermore, the support unit manages students' attendance and assignment submission status. The support unit can use AI to monitor students' attendance in real time and automatically update attendance records. For example, the support unit can capture students' attendance with a camera and confirm attendance using facial recognition technology. The support unit can also manage students' assignment submission status and send reminders for assignments that are past their deadline. The analysis unit analyzes students' grade data based on the work supported by the support unit. The academic performance data includes, but is not limited to, test scores, assignment evaluations, and attendance. The analysis unit uses AI to analyze the student's academic performance data and provide basic data for calculating the school report score. For example, the analysis unit analyzes the student's test results and calculates the score distribution for each student. The analysis unit can also evaluate the student's assignments and reflect the results in the academic performance data. The evaluation unit calculates the school report score based on the data analyzed by the analysis unit. The evaluation unit uses a machine learning algorithm to fairly calculate the school report score. For example, the evaluation unit applies an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation unit can also continuously monitor the student's academic performance data and revise the school report score as necessary. The monitoring unit continuously monitors the school report score calculated by the evaluation unit and revise it based on specific conditions. The specific conditions include, but are not limited to, changes in grades and attendance. The monitoring unit can use AI to monitor the student's academic performance data in real time and issue an alert if a review of the school report score is necessary.For example, the monitoring unit reevaluates the student's school report score if there is a sudden change in the student's grades. The monitoring unit can also review the student's school report score if there is a change in the student's attendance. This allows the education support system according to the embodiment to reduce the burden on teachers and eliminate variations in school report scores.

[0061] The support unit can support lesson preparation or teaching material creation. For example, the support unit collects materials necessary for lesson preparation and creates teaching materials. The support unit uses AI to collect information on the internet and provide materials appropriate for the lesson. For example, the support unit analyzes past lesson data and proposes optimal lesson plans. The support unit can also customize the content of teaching materials according to the progress of the lesson. For example, the support unit monitors the progress of the lesson in real time and modifies the content of the teaching materials as needed. This reduces the burden on teachers in lesson preparation and teaching material creation. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without AI. For example, the support unit can use AI to search information on the internet to collect materials necessary for lesson preparation and provide relevant materials. In addition, the support unit can use AI to analyze past lesson data and propose optimal lesson plans when creating teaching materials. Furthermore, the support unit can use AI to customize the content of the teaching materials according to the progress of the lesson. For example, the support unit can use AI to monitor the progress of the lesson in real time and modify the content of the teaching materials as needed. This allows the support department to reduce the burden on teachers in preparing lessons and creating teaching materials.

[0062] The support unit can manage students' attendance or assignment submission status. For example, the support unit monitors students' attendance in real time and automatically updates the attendance record. The support unit uses AI to capture students' attendance with a camera and confirm attendance using facial recognition technology. For example, the support unit captures students' attendance with a camera and confirms attendance using facial recognition technology. The support unit can also manage students' assignment submission status and send reminders for assignments that are past their deadline. For example, the support unit manages students' assignment submission status and sends reminders for assignments that are past their deadline. This reduces the workload of teachers and enables efficient management. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without AI. For example, the support unit can use AI to monitor students' attendance in real time and automatically update the attendance record. The support unit can also use AI to manage students' assignment submission status and send reminders for assignments that are past their deadline. This will enable the support department to reduce the workload of teachers and enable efficient management.

[0063] The analysis unit can analyze the grade data based on the students' test results or assignment evaluations. The analysis unit, for example, analyzes the students' test results and calculates the score distribution for each student. The analysis unit can analyze the students' test results and calculate the score distribution for each student using AI. For example, the analysis unit can analyze the students' test results and calculate the score distribution for each student. The analysis unit can also evaluate the students' assignments and reflect the results in the grade data. For example, the analysis unit can evaluate the students' assignments and reflect the results in the grade data. This allows the student grade data to be accurately analyzed. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze the students' test results and calculate the score distribution for each student. The analysis unit can also use AI to evaluate the students' assignments and reflect the results in the grade data. This allows the analysis unit to accurately analyze the student grade data.

[0064] The evaluation unit can calculate the school report score using a machine learning algorithm. For example, the evaluation unit applies an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation unit uses AI to apply an algorithm to calculate the school report score based on the student's test results and assignment evaluations. For example, the evaluation unit applies an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation unit can also continuously monitor the student's academic performance data and revise the school report score as necessary. For example, the evaluation unit can continuously monitor the student's academic performance data and revise the school report score as necessary. This allows the calculation of the school report score to be fair and accurate. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without AI. For example, the evaluation unit can use AI to apply an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation unit can also use AI to continuously monitor the student's academic performance data and revise the school report score as necessary. This allows the evaluation unit to calculate the school report score to be fair and accurate.

[0065] The monitoring unit can continuously monitor student performance data and revise school report scores based on specific conditions. For example, the monitoring unit can monitor student performance data in real time and issue an alert when a review of school report scores is necessary. The monitoring unit can use AI to monitor student performance data in real time and issue an alert when a review of school report scores is necessary. For example, the monitoring unit can reevaluate school report scores when a student's performance suddenly changes. The monitoring unit can also revise school report scores when a student's attendance status changes. This ensures that school report scores are reviewed in a timely manner and fair evaluations are maintained. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI or without AI. For example, the monitoring unit can use AI to monitor student performance data in real time and issue an alert when a review of school report scores is necessary. The monitoring unit can also use AI to reevaluate school report scores when a student's performance suddenly changes. Furthermore, the monitoring department can use AI to review grades if a student's attendance status changes, ensuring that grades are reviewed in a timely manner and maintaining fair evaluation.

[0066] The support unit can estimate the teacher's emotions and adjust the priority of lesson preparation based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, the support unit uses AI to adjust the priority of lesson preparation so that the teacher starts with an easier task. The support unit can estimate the teacher's emotions and adjust the priority of lesson preparation based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, the support unit uses AI to adjust the priority of lesson preparation so that the teacher starts with an easier task. The support unit can also prioritize complex tasks if the teacher is relaxed. Furthermore, if the teacher is tired, the support unit can suggest a break and adjust the lesson preparation schedule. This reduces the teacher's burden by adjusting the priority of lesson preparation based on the teacher's emotions. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without AI. For example, the support unit can use AI to estimate the teacher's emotions and adjust the priority of lesson preparation based on the estimated teacher's emotions. The support department can also use AI to adjust the priority of lesson preparations so that teachers start with easier tasks when they are feeling stressed. Furthermore, the support department can use AI to prioritize complex tasks when teachers are feeling relaxed. This allows the support department to reduce the burden on teachers by adjusting the priority of lesson preparations according to their emotions.

[0067] The support unit can analyze a teacher's past lesson preparation history and suggest an appropriate teaching material creation method. For example, the support unit can analyze the effectiveness of teaching materials used by the teacher in the past and suggest the most effective teaching material creation method. The support unit can use AI to analyze a teacher's past lesson preparation history and suggest an appropriate teaching material creation method. For example, the support unit can analyze the effectiveness of teaching materials used by the teacher in the past and suggest the most effective teaching material creation method. The support unit can also suggest areas for improvement based on feedback from the teacher's past lessons. Furthermore, the support unit can analyze the types and formats of teaching materials used by the teacher in the past and suggest the optimal combination. This can reduce the teacher's burden by suggesting the optimal teaching material creation method based on the teacher's past lesson preparation history. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without AI. For example, the support unit can use AI to analyze a teacher's past lesson preparation history and suggest an appropriate teaching material creation method. The support unit can also use AI to analyze the effectiveness of teaching materials used by the teacher in the past and suggest the most effective teaching material creation method. The support unit can also use AI to suggest areas for improvement based on feedback from the teacher's past lessons. This allows the support department to reduce the burden on teachers by suggesting the most appropriate way to create teaching materials based on past lesson preparation history.

[0068] The support unit can customize the content of teaching materials based on the teacher's current lesson progress when preparing for a lesson. For example, the support unit customizes the teaching materials to be used in the next lesson based on the content of the lesson the teacher is currently teaching. The support unit uses AI to customize the content of teaching materials based on the teacher's current lesson progress when preparing for a lesson. For example, the support unit customizes the teaching materials to be used in the next lesson based on the content of the lesson the teacher is currently teaching. The support unit can also suggest related teaching materials based on topics covered by the teacher in the lesson. Furthermore, the support unit can analyze the effectiveness of the teaching materials used by the teacher in the lesson and suggest areas for improvement in the next lesson. This makes it possible to customize teaching materials according to the lesson progress. Some or all of the above-mentioned processing in the support unit may be performed using AI or without AI. For example, the support unit can use AI to customize the content of teaching materials based on the teacher's current lesson progress when preparing for a lesson. Furthermore, the support unit can use AI to customize the content of teaching materials based on the teacher's current lesson progress. Furthermore, the support unit can use AI to customize the teaching materials to be used in the next lesson based on the content of the lesson the teacher is currently teaching. Furthermore, the support unit can use AI to suggest related teaching materials based on topics covered by the teacher in the lesson. This allows the support department to customize teaching materials according to the progress of the class.

[0069] The support unit can estimate the teacher's emotions and adjust the timing of creating teaching materials based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, the AI ​​delays the timing of creating teaching materials. The support unit can estimate the teacher's emotions and adjust the timing of creating teaching materials based on the estimated teacher's emotions using AI. For example, if the teacher is feeling stressed, the AI ​​delays the timing of creating teaching materials. The support unit can also advance the timing of creating teaching materials if the teacher is relaxed. Furthermore, if the teacher is tired, the AI ​​can suggest a break and adjust the teaching material creation schedule. This makes it possible to reduce the teacher's burden by adjusting the timing of creating teaching materials according to the teacher's emotions. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can use AI to estimate the teacher's emotions and adjust the timing of creating teaching materials based on the estimated teacher's emotions. Furthermore, the support unit can use AI to delay the timing of creating teaching materials if the teacher is feeling stressed. Furthermore, the support unit can use AI to speed up the timing of creating teaching materials when the teacher is relaxed. This allows the support unit to adjust the timing of creating teaching materials according to the teacher's emotions, thereby reducing the teacher's burden.

[0070] The support unit can create region-specific teaching materials based on the geographical location information of the teacher when preparing for a lesson. For example, if a teacher is teaching a lesson in a specific region, the support unit suggests region-specific teaching materials. The support unit uses AI to create region-specific teaching materials based on the geographical location information of the teacher when preparing for a lesson. For example, if a teacher is teaching a lesson in a specific region, the support unit suggests region-specific teaching materials. The support unit can also use AI to provide relevant information when a teacher is creating teaching materials based on the culture or history of the region. Furthermore, the support unit can also use AI to suggest optimal materials when a teacher is creating teaching materials that take the characteristics of the region into consideration. This makes it possible to create region-specific teaching materials. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can use AI to create region-specific teaching materials based on the geographical location information of the teacher when preparing for a lesson. Furthermore, if a teacher is teaching a lesson in a specific region, the support unit can use AI to suggest region-specific teaching materials. Furthermore, the support unit can use AI to provide relevant information when a teacher is creating teaching materials based on the culture or history of the region. This will enable the support department to create educational materials that are specific to the region.

[0071] The support unit can analyze the social media activity of the teacher when preparing for a lesson and suggest relevant teaching materials. For example, the support unit can suggest relevant teaching materials based on information shared by the teacher on social media. The support unit can use AI to analyze the social media activity of the teacher when preparing for a lesson and suggest relevant teaching materials. For example, the support unit can suggest relevant teaching materials based on information shared by the teacher on social media. The support unit can also customize teaching materials based on information about accounts the teacher follows on social media. The support unit can also suggest areas for improving the teaching materials based on feedback the teacher receives on social media. This makes it possible to suggest teaching materials based on social media activity. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can use AI to analyze the social media activity of the teacher when preparing for a lesson and suggest relevant teaching materials. The support unit can also use AI to customize teaching materials based on information about accounts the teacher follows on social media. The support unit can also use AI to suggest areas for improving the teaching materials based on feedback the teacher receives on social media. This makes it possible to suggest teaching materials based on social media activity.

[0072] The analysis unit can estimate a student's emotions and adjust the analysis method of the grade data based on the estimated student emotions. For example, if a student is feeling stressed, the analysis unit uses AI to adjust the analysis method of the grade data to reduce stress. The analysis unit uses AI to estimate a student's emotions and adjust the analysis method of the grade data based on the estimated student emotions. For example, if a student is feeling stressed, the analysis unit uses AI to adjust the analysis method of the grade data to reduce stress. The analysis unit can also use AI to perform a detailed analysis if the student is relaxed. Furthermore, the analysis unit can also use AI to use a simplified analysis method if the student is tired. This makes it possible to analyze grade data according to the student's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to estimate a student's emotions and adjust the analysis method of the grade data based on the estimated student emotions. Furthermore, the analysis unit can use AI to adjust the analysis method of the grade data to reduce stress if a student is feeling stressed. Furthermore, the analysis unit can use AI to perform detailed analysis when a student is relaxed, which allows the analysis unit to analyze performance data according to the student's emotions.

[0073] The analysis unit can analyze the student's past academic performance data and set appropriate evaluation criteria. The analysis unit can, for example, set optimal evaluation criteria based on the student's past academic performance data. The analysis unit can use AI to analyze the student's past academic performance data and set appropriate evaluation criteria. For example, the analysis unit can set optimal evaluation criteria based on the student's past academic performance data. The analysis unit can also analyze the student's academic performance trends and adjust the evaluation criteria. Furthermore, the analysis unit can set individual evaluation criteria based on the student's past academic performance data. This makes it possible to set optimal evaluation criteria based on the past academic performance data. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without using AI. For example, the analysis unit can use AI to analyze the student's past academic performance data and set appropriate evaluation criteria. The analysis unit can also use AI to analyze the student's academic performance trends and adjust the evaluation criteria. Furthermore, the analysis unit can use AI to set individual evaluation criteria based on the student's past academic performance data. This makes it possible for the analysis unit to set optimal evaluation criteria based on the past academic performance data.

[0074] The analysis unit can weight the data based on the student's current learning situation when analyzing the grade data. The analysis unit, for example, weights the grade data based on the student's current learning situation. The analysis unit uses AI to weight the data based on the student's current learning situation when analyzing the grade data. For example, the analysis unit weights the grade data based on the student's current learning situation. The analysis unit can also adjust the weighting of the grade data taking into account the student's learning progress. Furthermore, the analysis unit can weight the grade data by reflecting the student's current learning situation in real time. This makes it possible to weight the grade data according to the current learning situation. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to weight the data based on the student's current learning situation when analyzing the grade data. The analysis unit can also use AI to weight the grade data based on the student's current learning situation. The analysis unit can also use AI to adjust the weighting of the grade data taking into account the student's learning progress. This allows the analysis unit to weight the grade data according to the current learning situation.

[0075] The analysis unit can estimate the student's emotions and adjust the timing of analyzing the grade data based on the estimated student's emotions. For example, if the student is feeling stressed, the AI ​​delays the timing of analyzing the grade data. The analysis unit can estimate the student's emotions and adjust the timing of analyzing the grade data based on the estimated student's emotions using AI. For example, if the student is feeling stressed, the analysis unit can delay the timing of analyzing the grade data. Also, if the student is relaxed, the analysis unit can advance the timing of analyzing the grade data. Furthermore, if the student is tired, the AI ​​can suggest a break and adjust the timing of analyzing the grade data. This makes it possible to adjust the timing of analyzing the grade data according to the student's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to estimate the student's emotions and adjust the timing of analyzing the grade data based on the estimated student's emotions. Also, the analysis unit can use AI to delay the timing of analyzing the grade data if the student is feeling stressed. Furthermore, the analysis unit can use AI to speed up the timing of analyzing grade data when a student is relaxed, which enables the analysis unit to adjust the timing of analyzing grade data according to the student's emotions.

[0076] The analysis unit can perform region-specific evaluations based on the student's geographical location information when analyzing the academic performance data. For example, if a student lives in a specific region, the analysis unit performs evaluations taking into account the characteristics of the region. The analysis unit uses AI to perform region-specific evaluations based on the student's geographical location information when analyzing the academic performance data. For example, if a student lives in a specific region, the analysis unit performs evaluations taking into account the characteristics of the region. The analysis unit can also set region-specific evaluation criteria based on the student's geographical location information. Furthermore, the analysis unit can evaluate the academic performance data taking into account the educational environment of the student's region. This enables region-specific evaluations. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can use AI to perform region-specific evaluations based on the student's geographical location information when analyzing the academic performance data. Furthermore, if a student lives in a specific region, the analysis unit can use AI to perform evaluations taking into account the characteristics of the region. Furthermore, the analysis unit can use AI to set region-specific evaluation criteria based on the student's geographical location information. This enables region-specific evaluations.

[0077] The analysis unit can analyze students' social media activities when analyzing the academic performance data and complement the relevant data. The analysis unit, for example, complements the academic performance data based on students' social media activities. The analysis unit can use AI to analyze students' social media activities when analyzing the academic performance data and complement the relevant data. For example, the analysis unit complements the academic performance data based on students' social media activities. The analysis unit can also complement the academic performance data based on information shared by students on social media. The analysis unit can also analyze students' social media activities and reflect them in the evaluation of the academic performance data. This makes it possible to complement the academic performance data based on social media activities. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze students' social media activities when analyzing the academic performance data and complement the relevant data. The analysis unit can also use AI to complement the academic performance data based on students' social media activities. The analysis unit can also use AI to complement the academic performance data based on information shared by students on social media. This makes it possible for the analysis unit to complement the academic performance data based on social media activities.

[0078] The evaluation unit can estimate the student's emotions and adjust the calculation method of the school report score based on the estimated student's emotions. For example, if the student is feeling stressed, the evaluation unit uses AI to adjust the calculation method of the school report score to reduce stress. The evaluation unit uses AI to estimate the student's emotions and adjust the calculation method of the school report score based on the estimated student's emotions. For example, if the student is feeling stressed, the evaluation unit uses AI to adjust the calculation method of the school report score to reduce stress. The evaluation unit can also use a detailed calculation method if the student is relaxed. Furthermore, the evaluation unit can use a simplified calculation method if the student is tired. This makes it possible to adjust the calculation method of the school report score according to the student's emotions. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can use AI to estimate the student's emotions and adjust the calculation method of the school report score based on the estimated student's emotions. The evaluation department can also use AI to adjust the calculation method of school report scores when a student is feeling stressed, thereby reducing stress. Furthermore, the evaluation department can use AI to use a more detailed calculation method when a student is relaxed. This allows the evaluation department to adjust the calculation method of school report scores according to the student's emotions.

[0079] The evaluation unit can apply an appropriate evaluation algorithm by referring to the student's past academic performance data when calculating the internal assessment score. The evaluation unit, for example, applies an optimal evaluation algorithm based on the student's past academic performance data. The evaluation unit uses AI to apply an appropriate evaluation algorithm by referring to the student's past academic performance data when calculating the internal assessment score. For example, the evaluation unit can apply an optimal evaluation algorithm based on the student's past academic performance data. The evaluation unit can also analyze trends in the student's academic performance and adjust the evaluation algorithm. The evaluation unit can also apply an individual evaluation algorithm based on the student's past academic performance data. This makes it possible to apply an optimal evaluation algorithm based on the past academic performance data. Some or all of the above-described processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can use AI to apply an appropriate evaluation algorithm by referring to the student's past academic performance data when calculating the internal assessment score. The evaluation unit can also use AI to analyze trends in the student's academic performance and adjust the evaluation algorithm. The evaluation unit can also use AI to apply an individual evaluation algorithm based on the student's past academic performance data. This allows the evaluation unit to apply an optimal evaluation algorithm based on past performance data.

[0080] The evaluation unit can customize the evaluation criteria based on the student's current learning situation when calculating the internal assessment score. The evaluation unit customizes the evaluation criteria based on, for example, the student's current learning situation. The evaluation unit uses AI to customize the evaluation criteria based on the student's current learning situation when calculating the internal assessment score. For example, the evaluation unit customizes the evaluation criteria based on the student's current learning situation. The evaluation unit can also adjust the evaluation criteria taking into account the student's learning progress. Furthermore, the evaluation unit can customize the evaluation criteria by reflecting the student's current learning situation in real time. This makes it possible to customize the evaluation criteria according to the current learning situation. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can use AI to customize the evaluation criteria based on the student's current learning situation when calculating the internal assessment score. Furthermore, the evaluation unit can use AI to customize the evaluation criteria based on the student's current learning situation. Furthermore, the evaluation unit can use AI to adjust the evaluation criteria taking into account the student's learning progress. This allows the evaluation unit to customize the evaluation criteria according to the current learning situation.

[0081] The evaluation unit can estimate the student's emotions and adjust the timing of calculating the school report score based on the estimated student's emotions. For example, if the student is feeling stressed, the AI ​​delays the timing of calculating the school report score. The evaluation unit can estimate the student's emotions and adjust the timing of calculating the school report score based on the estimated student's emotions using AI. For example, if the student is feeling stressed, the AI ​​delays the timing of calculating the school report score. The evaluation unit can also advance the timing of calculating the school report score if the student is relaxed. Furthermore, if the student is tired, the AI ​​can suggest a break and adjust the timing of calculating the school report score. This makes it possible to adjust the timing of calculating the school report score according to the student's emotions. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can use AI to estimate the student's emotions and adjust the timing of calculating the school report score based on the estimated student's emotions. The evaluation department can also use AI to delay the timing of calculating the school report score if the student is feeling stressed. Furthermore, the evaluation department can use AI to accelerate the timing of calculating the school report score if the student is relaxed. This allows the evaluation department to adjust the timing of calculating the school report score according to the student's emotions.

[0082] The evaluation unit can perform a region-specific evaluation based on the student's geographical location information when calculating the school report score. For example, if the student lives in a specific region, the evaluation unit performs the evaluation taking into account the characteristics of the region. The evaluation unit uses AI to perform a region-specific evaluation based on the student's geographical location information when calculating the school report score. For example, if the student lives in a specific region, the evaluation unit performs the evaluation taking into account the characteristics of the region. The evaluation unit can also set region-specific evaluation criteria based on the student's geographical location information. Furthermore, the evaluation unit can evaluate the school report score taking into account the educational environment of the student's region. This enables a region-specific evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without AI. For example, the evaluation unit can use AI to perform a region-specific evaluation based on the student's geographical location information when calculating the school report score. Furthermore, if the student lives in a specific region, the evaluation unit can use AI to perform the evaluation taking into account the characteristics of the region. Furthermore, the evaluation unit can use AI to set region-specific evaluation criteria based on the student's geographical location information. This allows the evaluation unit to perform region-specific evaluations.

[0083] The evaluation unit can analyze a student's social media activity and supplement relevant data when calculating the school report score. The evaluation unit, for example, supplements the school report score based on the student's social media activity. The evaluation unit can use AI to analyze a student's social media activity and supplement relevant data when calculating the school report score. For example, the evaluation unit supplements the school report score based on the student's social media activity. The evaluation unit can also supplement the school report score based on information shared by the student on social media. Furthermore, the evaluation unit can analyze the student's social media activity and reflect it in the evaluation of the school report score. This makes it possible to supplement the school report score based on social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit can use AI to analyze a student's social media activity and supplement relevant data when calculating the school report score. The evaluation unit can also use AI to supplement the school report score based on the student's social media activity. Furthermore, the evaluation unit can use AI to supplement the school report score based on information shared by the student on social media. This will allow the evaluation department to supplement school grades based on social media activity.

[0084] The monitoring unit can estimate the student's emotions and adjust the monitoring frequency based on the estimated student's emotions. For example, if the student is feeling stressed, the AI ​​reduces the monitoring frequency. The monitoring unit can use AI to estimate the student's emotions and adjust the monitoring frequency based on the estimated student's emotions. For example, if the student is feeling stressed, the AI ​​can reduce the monitoring frequency. The monitoring unit can also increase the monitoring frequency if the student is relaxed. Furthermore, if the student is tired, the AI ​​can suggest a break and adjust the monitoring frequency. This makes it possible to adjust the monitoring frequency according to the student's emotions. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI or may be performed without using AI. For example, the monitoring unit can use AI to estimate the student's emotions and adjust the monitoring frequency based on the estimated student's emotions. Furthermore, if the student is feeling stressed, the AI ​​can reduce the monitoring frequency. Furthermore, if the student is relaxed, the AI ​​can increase the monitoring frequency. This allows the monitoring department to adjust the frequency of monitoring according to the student's emotions.

[0085] The monitoring unit can set an appropriate monitoring method by referring to the student's past academic performance data during monitoring. The monitoring unit, for example, sets an optimal monitoring method based on the student's past academic performance data. The monitoring unit uses AI to set an appropriate monitoring method by referring to the student's past academic performance data during monitoring. For example, the monitoring unit sets an optimal monitoring method based on the student's past academic performance data. The monitoring unit can also analyze trends in the student's academic performance and adjust the monitoring method. The monitoring unit can also set an individual monitoring method based on the student's past academic performance data. This makes it possible to set an optimal monitoring method based on the past academic performance data. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can use AI to set an appropriate monitoring method by referring to the student's past academic performance data during monitoring. The monitoring unit can also use AI to analyze trends in the student's academic performance and adjust the monitoring method. The monitoring unit can also use AI to set an individual monitoring method based on the student's past academic performance data. This makes it possible to set an optimal monitoring method based on the past academic performance data.

[0086] The monitoring unit can weight the data based on the student's current learning situation during monitoring. The monitoring unit, for example, weights the monitoring data based on the student's current learning situation. The monitoring unit uses AI to weight the data based on the student's current learning situation during monitoring. For example, the monitoring unit weights the monitoring data based on the student's current learning situation. The monitoring unit can also adjust the weighting of the monitoring data taking into account the student's learning progress. Furthermore, the monitoring unit can weight the monitoring data by reflecting the student's current learning situation in real time. This enables the monitoring data to be weighted according to the current learning situation. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI or may be performed without using AI. For example, the monitoring unit can use AI to weight the data based on the student's current learning situation during monitoring. Furthermore, the monitoring unit can use AI to weight the monitoring data based on the student's current learning situation. Furthermore, the monitoring unit can use AI to adjust the weighting of the monitoring data taking into account the student's learning progress. This enables the monitoring unit to weight the monitoring data according to the current learning situation.

[0087] The monitoring unit can estimate the student's emotions and determine the monitoring priority based on the estimated student's emotions. For example, if the student is feeling stressed, the AI ​​lowers the monitoring priority. The monitoring unit can use AI to estimate the student's emotions and determine the monitoring priority based on the estimated student's emotions. For example, if the student is feeling stressed, the AI ​​lowers the monitoring priority. The monitoring unit can also increase the monitoring priority if the student is relaxed. Furthermore, if the student is tired, the AI ​​can suggest a break and adjust the monitoring priority. This makes it possible to determine the monitoring priority according to the student's emotions. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can use AI to estimate the student's emotions and determine the monitoring priority based on the estimated student's emotions. Furthermore, the monitoring unit can use AI to lower the monitoring priority if the student is feeling stressed. Furthermore, the monitoring unit can use AI to prioritize monitoring when a student is relaxed, which allows the monitoring unit to prioritize monitoring according to the student's emotions.

[0088] The monitoring unit can perform region-specific monitoring based on the student's geographical location information during monitoring. For example, if the student lives in a specific area, the monitoring unit performs monitoring taking into account the characteristics of the area. The monitoring unit uses AI to perform region-specific monitoring based on the student's geographical location information during monitoring. For example, if the student lives in a specific area, the monitoring unit performs monitoring taking into account the characteristics of the area. The monitoring unit can also set region-specific monitoring criteria based on the student's geographical location information. Furthermore, the monitoring unit can perform monitoring taking into account the educational environment of the student's area. This enables region-specific monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can use AI to perform region-specific monitoring based on the student's geographical location information during monitoring. Furthermore, if the student lives in a specific area, the monitoring unit can use AI to perform monitoring taking into account the characteristics of the area. Furthermore, the monitoring unit can use AI to set region-specific monitoring criteria based on the student's geographical location information. This enables region-specific monitoring.

[0089] The monitoring unit can analyze students' social media activities during monitoring and complement related data. The monitoring unit, for example, complements the monitoring data based on students' social media activities. The monitoring unit can use AI to analyze students' social media activities during monitoring and complement related data. For example, the monitoring unit complements the monitoring data based on students' social media activities. The monitoring unit can also complement the monitoring data based on information shared by students on social media. Furthermore, the monitoring unit can analyze students' social media activities and reflect them in the evaluation of the monitoring data. This enables the monitoring data to be complemented based on social media activities. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can use AI to analyze students' social media activities during monitoring and complement related data. The monitoring unit can also use AI to complement the monitoring data based on students' social media activities. Furthermore, the monitoring unit can use AI to complement the monitoring data based on information shared by students on social media. This enables the monitoring unit to complement the monitoring data based on social media activities. === Hard Collateral 1-1 === Each of the multiple elements including the support unit, analysis unit, evaluation unit, and monitoring unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the support unit is realized by the control unit 46A of the smart device 14 and supports the teacher's work. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the student's academic performance data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the school report score. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14 and reviews the school report score. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned support unit, analysis unit, evaluation unit, and monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the support unit is realized by the control unit 46A of the smart glasses 214 and supports the teacher's work. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes student performance data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates school report scores. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and reviews the school report scores. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned support unit, analysis unit, evaluation unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the support unit is realized by the control unit 46A of the headset type terminal 314 and supports the teacher's work. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the student's academic performance data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the school report score. The monitoring unit is realized, for example, by the control unit 46A of the headset type terminal 314 and reviews the school report score. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned support unit, analysis unit, evaluation unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the support unit is realized by the control unit 46A of the robot 414 and supports the teacher's work. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the student's academic performance data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the school report score. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and reviews the school report score.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The support department can monitor teachers' health status and adjust their work load based on their health condition. For example, if a teacher is feeling unwell, the AI ​​can reduce their work load and prioritize easy tasks. Also, if a teacher is in good health, the AI ​​can assign them regular work. Furthermore, the support department can suggest breaks and adjust work schedules based on the teacher's health condition. This makes it possible to adjust a teacher's work load based on their health condition.

[0092] The analysis unit can estimate a student's learning style and adjust the analysis method for grade data based on the estimated learning style. For example, if the student is a visual learner, the analysis unit can have the AI ​​emphasize visual data in its analysis. Also, if the student is an auditory learner, the analysis unit can have the AI ​​emphasize auditory data in its analysis. Furthermore, if the student is an experiential learner, the analysis unit can have the AI ​​emphasize experiential data in its analysis. This makes it possible to analyze grade data according to the student's learning style.

[0093] The evaluation unit can set learning goals for students and adjust the calculation method of school report marks based on the set learning goals. For example, if a student is aiming for high grades in a specific subject, the evaluation unit can have the AI ​​calculate school report marks by placing emphasis on grades in that subject. Alternatively, if a student is aiming to improve their overall grades, the evaluation unit can have the AI ​​evaluate grades in all subjects equally. Furthermore, if a student is aiming to acquire a specific skill, the evaluation unit can calculate school report marks by placing emphasis on grades related to that skill. This makes it possible to calculate school report marks according to the student's learning goals.

[0094] The monitoring unit can evaluate the student's learning environment and adjust the monitoring method based on the evaluated learning environment. For example, if the student is studying in a quiet environment, the AI ​​can apply a normal monitoring method. If the student is studying in a noisy environment, the AI ​​can increase the frequency of monitoring. Furthermore, if the student is studying online, the AI ​​can also apply a monitoring method specific to online learning. This makes it possible to adjust the monitoring method to suit the student's learning environment.

[0095] The support unit can estimate the teacher's emotions and adjust the way the lesson proceeds based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, the AI ​​can adjust the pace of the lesson to a slower pace. If the teacher is relaxed, the AI ​​can return the pace of the lesson to a normal pace. Furthermore, if the teacher is tired, the AI ​​can suggest a break and temporarily suspend the lesson. This makes it possible to adjust the way the lesson proceeds according to the teacher's emotions.

[0096] The analysis unit can analyze the student's learning history and suggest an appropriate learning approach. For example, the analysis unit can identify learning methods that the student has had success with in the past and suggest a similar approach. The analysis unit can also suggest that the student avoid learning methods that the student has had difficulty with in the past. Furthermore, the analysis unit can also suggest that the student try a new learning method based on the student's learning history. This makes it possible to suggest an appropriate learning approach based on the student's learning history.

[0097] The evaluation unit can estimate the student's emotions and adjust the content of the feedback based on the estimated student's emotions. For example, if the student is feeling stressed, the AI ​​can prioritize providing positive feedback. If the student is relaxed, the AI ​​can provide detailed feedback. Furthermore, if the student is tired, the AI ​​can provide concise feedback. This makes it possible to adjust the content of feedback according to the student's emotions.

[0098] The monitoring unit can track students' learning progress in real time and adjust the learning plan based on the progress. For example, if the student is progressing faster than planned, the AI ​​can advance the learning plan. Also, if the student is falling behind, the AI ​​can extend the learning plan. Furthermore, the monitoring unit can adjust the learning content according to the student's progress and propose the optimal learning plan. This makes it possible to adjust the learning plan according to the student's learning progress.

[0099] The support unit can estimate the teacher's emotions and customize the content of lessons based on the estimated teacher's emotions. For example, if the teacher is feeling stressed, the AI ​​will prioritize easy content. If the teacher is relaxed, the AI ​​can cover more complex content. Furthermore, if the teacher is tired, the AI ​​can suggest a break and temporarily suspend the lesson content. This makes it possible to customize the content of lessons according to the teacher's emotions.

[0100] The evaluation unit can estimate a student's learning style and adjust the calculation method of the school report score based on the estimated learning style. For example, if the student is a visual learner, the evaluation unit can have the AI ​​calculate the school report score by emphasizing visual data. Also, if the student is an auditory learner, the evaluation unit can have the AI ​​calculate the school report score by emphasizing auditory data. Furthermore, if the student is an experiential learner, the evaluation unit can have the AI ​​calculate the school report score by emphasizing experiential data. This makes it possible to calculate school report scores according to the student's learning style.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The support department supports teachers in their work. Specifically, it assists with lesson preparation, grading, assignment management, etc. The support department uses AI to collect materials necessary for lesson preparation and create teaching materials. It also analyzes past lesson data and proposes optimal lesson plans. It also manages student attendance and assignment submission status and automatically updates the attendance register. For example, it uses a camera to capture students' attendance and uses facial recognition technology to confirm attendance. It also manages assignment submission status and sends reminders for assignments that have passed their submission deadline. Step 2: The analysis department analyzes the students' academic performance data based on the work supported by the support department. The academic performance data includes test scores, assignment evaluations, attendance, etc. The analysis department uses AI to analyze the students' academic performance data and provide basic data for calculating school grades. For example, it analyzes students' test results and calculates the score distribution for each student. It also evaluates students' assignments and reflects the results in the academic performance data. Step 3: The evaluation department calculates the school report score based on the data analyzed by the analysis department. The evaluation department uses machine learning algorithms to fairly calculate the school report score. For example, the evaluation department applies an algorithm to calculate the school report score based on the student's test results and assignment evaluations. The evaluation department can also continuously monitor the student's academic performance data and revise the school report score as necessary. Step 4: The monitoring department continuously monitors the assessment scores calculated by the evaluation department and reviews them based on specific conditions. These conditions include fluctuations in grades and changes in attendance. The monitoring department uses AI to monitor student performance data in real time and issues alerts when a review of the assessment score is necessary. For example, if a student's grades change suddenly or their attendance status changes, the assessment score will be reevaluated.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A support department that supports teachers in their work, an analysis unit that analyzes the student's grade data based on the work supported by the support unit; an evaluation unit that calculates a school report score based on the data analyzed by the analysis unit; A monitoring unit continuously monitors the internal assessment scores calculated by the evaluation unit and reviews them based on specific conditions. A system characterized by:

2. The support unit Assist with lesson preparation or the creation of teaching materials 2. The system of claim 1.

3. The support unit Track student attendance or assignment submission status 2. The system of claim 1.

4. The analysis unit Analyze performance data based on student test results or assignment evaluations 2. The system of claim 1.

5. The evaluation unit Calculate school grades using machine learning algorithms 2. The system of claim 1.

6. The monitoring unit Continuously monitor student performance data and review school grades based on specific conditions 2. The system of claim 1.

7. The support unit Estimating teacher emotions and adjusting lesson preparation priorities based on the estimated teacher emotions 2. The system of claim 1.

8. The support unit Analyzing teachers' past lesson preparation history and suggesting appropriate teaching material creation methods 2. The system of claim 1.

Images