system

Abstract

The system according to this embodiment aims to streamline lesson preparation for new instructors and rapidly improve their teaching skills. [Solution] The system according to the embodiment comprises a generation unit, a support unit, and a feedback unit. The generation unit generates lesson plans and teaching materials based on past student information and instructional curricula. The support unit provides real-time support during lessons. The feedback unit provides feedback after lessons.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a novice instructor spends a lot of time preparing for classes and it is difficult to quickly improve their teaching ability.

[0005] The system according to the embodiment aims to streamline the class preparation of novice instructors and quickly improve their teaching ability.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a support unit, and a feedback unit. The generation unit generates a class plan and teaching materials based on past student information and teaching curricula. The support unit provides real-time support during classes. The feedback unit provides feedback after classes. [Effects of the Invention]

[0007] The system according to this embodiment can streamline lesson preparation for new instructors and rapidly improve their teaching skills. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent according to an embodiment of the present invention is a system that utilizes generative AI to support new instructors in preparing for lessons and improve their teaching skills. This AI agent automatically generates lesson plans and teaching materials based on educational data such as past student information and teaching curricula held by each cram school. This significantly reduces the time instructors spend preparing for lessons. Furthermore, it provides real-time support to instructors during lessons, enabling high-quality lessons. After lessons, it provides specific feedback to help improve future lessons. For example, the AI ​​agent automatically generates lesson plans and teaching materials based on past student information and teaching curricula. For example, it analyzes past lesson data and proposes the optimal lesson plan. Next, it provides real-time support to instructors during lessons. For example, it monitors the progress of the lesson and provides advice as needed. Furthermore, it provides specific feedback after lessons to help improve future lessons. For example, it analyzes recorded lesson data and points out areas for improvement. This allows new instructors to quickly improve their teaching skills and provide more high-quality lessons. In addition, it is expected to greatly contribute to improving the overall educational standards of cram schools by making the most of the limited opportunities available to instructors in preparing students for entrance exams. This allows the AI ​​agent to support new instructors in preparing for lessons and improve their teaching skills.

[0029] The AI ​​agent according to this embodiment comprises a generation unit, a support unit, and a feedback unit. The generation unit generates lesson plans and teaching materials based on past student information and instructional curricula. The generation unit, for example, analyzes past lesson data and proposes an optimal lesson plan. The generation unit can generate lesson plans and teaching materials based on past student information and instructional curricula using a generation AI. The generation unit, for example, has the generation AI analyze past lesson data and propose an optimal lesson plan. The generation unit can also have the generation AI analyze past student information and generate teaching materials that are optimal for each individual student. The generation unit can also have the generation AI analyze past instructional curricula and generate an optimal lesson plan. The support unit provides real-time support during lessons. The support unit, for example, monitors the progress of the lesson and provides advice as needed. The support unit can use AI to monitor the progress of the lesson and provide advice as needed. The support unit, for example, has the AI ​​monitor the progress of the lesson and provide advice as needed. The support unit can also have the AI ​​monitor the progress of the lesson in real time and provide advice as needed. The support unit can also use AI to monitor the progress of the lesson and provide advice as needed. The feedback unit provides feedback after the lesson. The feedback unit can, for example, analyze the recording of the lesson and point out areas for improvement. The feedback unit can use AI to analyze the recording of the lesson and point out areas for improvement. The feedback unit can, for example, use AI to analyze the recording of the lesson and point out areas for improvement. The feedback unit can also use AI to analyze the recording of the lesson and point out areas for improvement. The feedback unit can also use AI to analyze the recording of the lesson and point out areas for improvement. As a result, the AI ​​agent according to this embodiment can use generative AI to support new instructors in preparing for lessons and improve their teaching skills.

[0030] The generation unit generates lesson plans and teaching materials based on past student information and instructional curricula. Specifically, the generation unit collects and analyzes past lesson data to propose the optimal lesson plan. The generation AI uses natural language processing technology to analyze past lesson records and student performance data, and generates lesson plans tailored to each student's level of understanding and learning progress. For example, based on past lesson content and student feedback, the generation AI creates a lesson plan that provides supplementary materials and additional practice problems for students who have a low level of understanding of a particular topic. The generation AI can also analyze the content of the instructional curriculum and generate teaching materials tailored to the purpose and objectives of each lesson. For example, the generation AI extracts important points from the curriculum, plans the progress of the lesson based on them, and generates the necessary teaching materials. Furthermore, the generation AI can monitor the progress of the lesson and student reactions in real time and modify the lesson plan as needed. In this way, the generation unit can provide optimal lesson plans and teaching materials for each individual student, supporting effective learning.

[0031] The support department provides real-time support during lessons. Specifically, the support department monitors the progress of the lessons and provides advice as needed. The AI ​​monitors the progress of the lessons in real time and can immediately point out problems and areas for improvement in how the instructor is conducting the lesson. For example, the AI ​​analyzes the frequency of student reactions and questions during the lesson to identify areas where students do not understand. Based on this, it advises the instructor to provide additional explanations or supplementary materials. The AI ​​can also monitor the pace of the lesson and instruct the instructor to adjust the pace according to the students' understanding. Furthermore, the support department also handles technical problems and troubles that occur during lessons. For example, if a communication failure occurs in an online lesson, the AI ​​will quickly detect the problem and suggest appropriate solutions to the instructor. In this way, the support department can help the lessons proceed smoothly and provide support to instructors to conduct lessons effectively.

[0032] The Feedback Department provides feedback after each lesson. Specifically, the Feedback Department analyzes the recorded lesson data and identifies areas for improvement. AI uses speech recognition technology to transcribe the recorded lesson data into text and analyzes the lesson content, the instructor's speaking style, and the lesson's progress in detail. For example, the AI ​​analyzes what the instructor says during the lesson and points out if the explanation of a particular topic was insufficient or if the answers to students' questions were unclear. The AI ​​also analyzes the pace of the lesson and the instructor's speaking tempo, and provides advice on how to improve the pace if it is too fast or too slow. Furthermore, the Feedback Department collects feedback and evaluations from students and uses this to identify areas for improvement in the lessons. For example, it analyzes questionnaires and evaluation sheets submitted by students after the lesson to collect feedback on points that students found difficult to understand and opinions on the lesson's progress. Based on this, the Feedback Department can propose specific areas for improvement to instructors, which can then be reflected in future lessons. In this way, the Feedback Department can provide specific advice to improve the quality of lessons and enhance the instructors' teaching skills.

[0033] The generation unit can analyze past lesson data and propose the optimal lesson plan. For example, the generation unit can analyze past lesson data and propose the optimal lesson plan. The generation unit can use a generation AI to analyze past lesson data and propose the optimal lesson plan. For example, the generation AI can analyze past lesson data and propose the optimal lesson plan. The generation unit can also have the generation AI analyze past lesson data and propose the optimal lesson plan. This allows the generation unit to propose the optimal lesson plan by analyzing past lesson data.

[0034] The support department can monitor the progress of the lessons and provide advice as needed. For example, the support department can monitor the progress of the lessons and provide advice as needed. The support department can use AI to monitor the progress of the lessons and provide advice as needed. For example, the support department can have AI monitor the progress of the lessons and provide advice as needed. The support department can also have AI monitor the progress of the lessons in real time and provide advice as needed. This allows for timely advice to be provided by monitoring the progress of the lessons.

[0035] The feedback department can analyze recorded lecture data and point out areas for improvement. For example, the feedback department can analyze recorded lecture data and point out areas for improvement. The feedback department can use AI to analyze recorded lecture data and point out areas for improvement. For example, the feedback department can use AI to analyze recorded lecture data and point out areas for improvement. The feedback department can also use AI to analyze recorded lecture data and point out areas for improvement. This allows for the identification of specific areas for improvement by analyzing recorded lecture data.

[0036] The generation unit can generate lesson plans and teaching materials based on past student information and teaching curricula. For example, the generation unit generates lesson plans and teaching materials based on past student information and teaching curricula. The generation unit can use a generation AI to generate lesson plans and teaching materials based on past student information and teaching curricula. For example, the generation AI generates lesson plans and teaching materials based on past student information and teaching curricula. The generation unit can also have the generation AI generate lesson plans and teaching materials based on past student information and teaching curricula. This allows for the generation of optimal lesson plans and teaching materials based on past student information and teaching curricula.

[0037] The support department can provide real-time support to instructors during lessons. For example, the support department can provide real-time support to instructors during lessons. The support department can use AI to provide real-time support to instructors during lessons. For example, the support department can use AI to provide real-time support to instructors during lessons. The support department can also use AI to provide real-time support to instructors during lessons. This enables high-quality lessons by providing real-time support during classes.

[0038] The generation unit can analyze past lesson data and generate lesson plans tailored to the learning style of specific students. For example, if a student prefers visual learning, the generation AI can generate a lesson plan that makes extensive use of diagrams and graphs. If a student prefers auditory learning, the generation AI can also generate a lesson plan that makes extensive use of audio and video. If a student prefers practical learning, the generation AI can also generate a lesson plan that includes many experiments and exercises. By generating lesson plans tailored to the learning style of specific students, the system improves students' understanding. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning style data into the generation AI and have the generation AI generate lesson plans.

[0039] The generation unit can customize lesson plans by considering students' understanding of specific subjects or topics. For example, if a student has difficulty with a particular topic, the generation AI will generate a lesson plan that focuses on that topic. For subjects in which students excel, the generation AI can also generate lesson plans that include many application problems. The generation unit can also have the generation AI adjust the pace of the lesson according to the students' understanding. This allows for the provision of effective lessons by customizing lesson plans to consider students' understanding. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student understanding data into the generation AI and have the generation AI customize the lesson plan.

[0040] The generation unit can optimize lesson plans by considering a specific student's past performance data when generating them. For example, the generation unit can generate lesson plans that focus on areas where the generating AI struggles, based on the student's past performance data. The generation unit can also generate lesson plans that reinforce areas where the generating AI excels, including application problems, based on the student's performance data. The generation unit can also analyze the student's performance data, allowing the generating AI to adjust the pace of the lesson. This enables the provision of effective lessons by optimizing lesson plans by considering the student's past performance data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student performance data into the generating AI and have the generating AI perform the optimization of the lesson plan.

[0041] The generation unit can add relevant teaching materials based on the interests of specific students when generating lesson plans. For example, the generation unit's AI can add teaching materials related to topics that students are interested in. The generation unit can also have the generation AI add experiments or projects related to the lesson plan based on students' interests. The generation unit can also have the generation AI add videos or audio materials related to the lesson plan according to students' interests. This allows for the provision of effective lessons by adding relevant teaching materials based on students' interests. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student interest data into the generation AI and have the generation AI add relevant teaching materials.

[0042] The support unit can monitor the progress of the lesson, analyze student responses in real time, and provide feedback to the instructor. For example, the support unit can analyze students' facial expressions and attitudes, and the AI ​​can provide real-time feedback to the instructor. The support unit can also evaluate students' understanding in real time, and the AI ​​can suggest additional explanations or supplementary information to the instructor. The support unit can monitor the progress of the lesson, and the AI ​​can suggest to the instructor that they adjust the pace of the lesson. This allows for effective feedback by monitoring the progress of the lesson and analyzing student responses in real time. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input student response data into a generating AI and have the generating AI provide feedback.

[0043] The support unit can assess a particular student's understanding in real time during a lesson and provide additional explanations or supplementary materials as needed. For example, the support unit can assess a student's understanding in real time and have the AI ​​suggest additional explanations to the instructor. The support unit can also have the AI ​​provide supplementary materials if a student's understanding of a particular topic is insufficient. The support unit can also have the AI ​​suggest adjusting the pace of the lesson based on the student's understanding. This allows for the provision of additional explanations or supplementary materials as needed by assessing a particular student's understanding in real time. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input student understanding data into a generating AI and have the generating AI provide additional explanations or supplementary materials.

[0044] The support unit can monitor the progress of the lesson, assess the concentration level of specific students, and suggest breaks at appropriate times. For example, if a student's concentration level is declining, the AI ​​can suggest a break to the instructor. The support unit can also monitor the progress of the lesson and have the AI ​​suggest breaks at appropriate times. The support unit can analyze student responses and have the AI ​​suggest break timings to the instructor. This allows for suggesting breaks at appropriate times by assessing the concentration level of specific students. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input student concentration data into a generating AI and have the generating AI execute break suggestions.

[0045] The support unit can refer to a specific student's question history during class and provide relevant additional information. For example, the support unit can refer to a student's question history and have the AI ​​provide relevant additional information. The support unit can also have the AI ​​provide relevant supplementary materials based on questions the student has asked in the past. The support unit can also analyze a student's question history and have the AI ​​provide additional information in line with the progress of the class. This allows the support unit to provide relevant additional information by referring to a specific student's question history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input student question history data into a generating AI and have the generating AI perform the task of providing additional information.

[0046] The feedback unit can analyze recorded lesson data, evaluate the instructor's responses to specific students' comments and questions, and provide feedback. For example, the feedback unit can analyze recorded lesson data and have AI provide feedback on the instructor's strengths. The feedback unit can also evaluate the instructor's responses to students' comments and questions, and the AI ​​can suggest areas for improvement. Based on the recorded lesson data, the feedback unit can also have AI evaluate the consistency of the instructor's responses and provide feedback. This allows for specific feedback on the instructor's strengths and areas for improvement by analyzing the recorded lesson data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input recorded lesson data into a generating AI and have the generating AI perform the evaluation of the instructor's responses and provide feedback.

[0047] The feedback unit can evaluate students' understanding after class and suggest specific areas for improvement for the next class. For example, the feedback unit can evaluate students' understanding and have the AI ​​suggest specific areas for improvement for the next class. The feedback unit can also collect student feedback after class and have the AI ​​suggest areas for improvement to the instructor. Based on students' understanding, the feedback unit can also have the AI ​​suggest adjustments to the pace and content of the next class. This allows for the suggestion of specific areas for improvement for the next class by evaluating students' understanding. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input student understanding data into a generating AI and have the generating AI generate suggestions for improvements for the next class.

[0048] The feedback unit can analyze recorded lesson data, evaluate the responses of specific students, and provide feedback. For example, the feedback unit can analyze recorded lesson data, and the AI ​​can evaluate the students' responses and provide feedback. Based on the students' responses, the AI ​​can also suggest areas for improvement to the instructor. Based on the recorded lesson data, the AI ​​can also evaluate the consistency of the students' responses and provide feedback. This allows for the evaluation of student responses and the provision of specific feedback to instructors by analyzing recorded lesson data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input recorded lesson data into a generating AI and have the generating AI perform the evaluation of student responses and provide feedback.

[0049] The feedback unit can evaluate students' learning progress after class and propose specific action plans for the next class. For example, the feedback unit can evaluate students' learning progress and have the AI ​​propose specific action plans for the next class. The feedback unit can also collect student progress data after class and have the AI ​​propose action plans for the instructor. Based on students' learning progress, the feedback unit can also have the AI ​​suggest adjustments to the pace and content of the next class. This allows for the proposal of specific action plans for the next class by evaluating students' learning progress. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input student learning progress data into a generating AI and have the generating AI propose action plans for the next class.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can optimize lesson plans by considering the instructor's past lesson evaluation data. For example, it can analyze the teaching styles in which the instructor has received high ratings in the past and generate lesson plans that incorporate similar styles. It can also adjust lesson plans to avoid lesson content in which the instructor has received low ratings in the past. Furthermore, based on the instructor's evaluation data, the generating AI can adjust the pace of the lesson and the difficulty level of the teaching materials. In this way, by optimizing lesson plans while considering the instructor's past lesson evaluation data, effective lessons can be provided.

[0052] The generation unit can customize lesson plans by considering the specific home environment and background information of individual students. For example, if a student has limited study time at home, the generation AI can create an efficient lesson plan. If a student has a specific cultural background, the generation AI can also create a lesson plan that takes that background into account. If a student requires special support, the generation AI can also create a lesson plan that takes that support into consideration. In this way, by customizing lesson plans to take into account students' home environment and background information, effective lessons can be provided.

[0053] The feedback system can evaluate students' motivation after class and suggest specific strategies to improve their motivation for the next lesson. For example, if students are highly motivated, the AI ​​can suggest challenging tasks. If students are less motivated, the AI ​​can suggest engaging learning materials. If students are moderately motivated, the AI ​​can suggest a well-balanced lesson plan. By evaluating students' motivation and suggesting strategies to improve it for the next lesson, the system can deliver more effective lessons.

[0054] The generation unit can adjust lesson plans while considering the health condition of specific students. For example, if a student is tired, the generation AI will generate a concise and efficient lesson plan. If a student is unwell, the generation AI can also generate a less burdensome lesson plan. If a student is healthy, the generation AI can also generate a lesson plan that includes active activities. In this way, by adjusting lesson plans to take into account the health condition of students, effective lessons can be provided.

[0055] The generation unit can optimize lesson plans by considering the learning objectives of specific students. For example, if a student is preparing for a particular exam, the generation AI will generate a lesson plan tailored to that exam. If a student wants to acquire a specific skill, the generation AI can also generate a lesson plan focused on that skill. If a student aims for overall academic improvement, the generation AI can generate a balanced lesson plan. This allows for the provision of effective lessons by optimizing lesson plans to consider students' learning objectives.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The generation unit generates lesson plans and teaching materials based on past student information and teaching curricula. The generation unit uses a generation AI to analyze past lesson data and propose the optimal lesson plan. The generation unit can also use the generation AI to analyze past student information and generate teaching materials best suited to each individual student. The generation unit can also use the generation AI to analyze past teaching curricula and generate the optimal lesson plan. Step 2: The support team provides real-time support during class. The support team uses AI to monitor the progress of the class and provides advice as needed. Step 3: The feedback team provides feedback after the lesson. The feedback team uses AI to analyze the recorded lesson data and point out areas for improvement.

[0058] (Example of form 2) The AI ​​agent according to an embodiment of the present invention is a system that utilizes generative AI to support new instructors in preparing for lessons and improve their teaching skills. This AI agent automatically generates lesson plans and teaching materials based on educational data such as past student information and teaching curricula held by each cram school. This significantly reduces the time instructors spend preparing for lessons. Furthermore, it provides real-time support to instructors during lessons, enabling high-quality lessons. After lessons, it provides specific feedback to help improve future lessons. For example, the AI ​​agent automatically generates lesson plans and teaching materials based on past student information and teaching curricula. For example, it analyzes past lesson data and proposes the optimal lesson plan. Next, it provides real-time support to instructors during lessons. For example, it monitors the progress of the lesson and provides advice as needed. Furthermore, it provides specific feedback after lessons to help improve future lessons. For example, it analyzes recorded lesson data and points out areas for improvement. This allows new instructors to quickly improve their teaching skills and provide more high-quality lessons. In addition, it is expected to greatly contribute to improving the overall educational standards of cram schools by making the most of the limited opportunities available to instructors in preparing students for entrance exams. This allows the AI ​​agent to support new instructors in preparing for lessons and improve their teaching skills.

[0059] The AI ​​agent according to this embodiment comprises a generation unit, a support unit, and a feedback unit. The generation unit generates lesson plans and teaching materials based on past student information and instructional curricula. The generation unit, for example, analyzes past lesson data and proposes an optimal lesson plan. The generation unit can generate lesson plans and teaching materials based on past student information and instructional curricula using a generation AI. The generation unit, for example, has the generation AI analyze past lesson data and propose an optimal lesson plan. The generation unit can also have the generation AI analyze past student information and generate teaching materials that are optimal for each individual student. The generation unit can also have the generation AI analyze past instructional curricula and generate an optimal lesson plan. The support unit provides real-time support during lessons. The support unit, for example, monitors the progress of the lesson and provides advice as needed. The support unit can use AI to monitor the progress of the lesson and provide advice as needed. The support unit, for example, has the AI ​​monitor the progress of the lesson and provide advice as needed. The support unit can also have the AI ​​monitor the progress of the lesson in real time and provide advice as needed. The support unit can also use AI to monitor the progress of the lesson and provide advice as needed. The feedback unit provides feedback after the lesson. The feedback unit can, for example, analyze the recording of the lesson and point out areas for improvement. The feedback unit can use AI to analyze the recording of the lesson and point out areas for improvement. The feedback unit can, for example, use AI to analyze the recording of the lesson and point out areas for improvement. The feedback unit can also use AI to analyze the recording of the lesson and point out areas for improvement. The feedback unit can also use AI to analyze the recording of the lesson and point out areas for improvement. As a result, the AI ​​agent according to this embodiment can use generative AI to support new instructors in preparing for lessons and improve their teaching skills.

[0060] The generation unit generates lesson plans and teaching materials based on past student information and instructional curricula. Specifically, the generation unit collects and analyzes past lesson data to propose the optimal lesson plan. The generation AI uses natural language processing technology to analyze past lesson records and student performance data, and generates lesson plans tailored to each student's level of understanding and learning progress. For example, based on past lesson content and student feedback, the generation AI creates a lesson plan that provides supplementary materials and additional practice problems for students who have a low level of understanding of a particular topic. The generation AI can also analyze the content of the instructional curriculum and generate teaching materials tailored to the purpose and objectives of each lesson. For example, the generation AI extracts important points from the curriculum, plans the progress of the lesson based on them, and generates the necessary teaching materials. Furthermore, the generation AI can monitor the progress of the lesson and student reactions in real time and modify the lesson plan as needed. In this way, the generation unit can provide optimal lesson plans and teaching materials for each individual student, supporting effective learning.

[0061] The support department provides real-time support during lessons. Specifically, the support department monitors the progress of the lessons and provides advice as needed. The AI ​​monitors the progress of the lessons in real time and can immediately point out problems and areas for improvement in how the instructor is conducting the lesson. For example, the AI ​​analyzes the frequency of student reactions and questions during the lesson to identify areas where students do not understand. Based on this, it advises the instructor to provide additional explanations or supplementary materials. The AI ​​can also monitor the pace of the lesson and instruct the instructor to adjust the pace according to the students' understanding. Furthermore, the support department also handles technical problems and troubles that occur during lessons. For example, if a communication failure occurs in an online lesson, the AI ​​will quickly detect the problem and suggest appropriate solutions to the instructor. In this way, the support department can help the lessons proceed smoothly and provide support to instructors to conduct lessons effectively.

[0062] The Feedback Department provides feedback after each lesson. Specifically, the Feedback Department analyzes the recorded lesson data and identifies areas for improvement. AI uses speech recognition technology to transcribe the recorded lesson data into text and analyzes the lesson content, the instructor's speaking style, and the lesson's progress in detail. For example, the AI ​​analyzes what the instructor says during the lesson and points out if the explanation of a particular topic was insufficient or if the answers to students' questions were unclear. The AI ​​also analyzes the pace of the lesson and the instructor's speaking tempo, and provides advice on how to improve the pace if it is too fast or too slow. Furthermore, the Feedback Department collects feedback and evaluations from students and uses this to identify areas for improvement in the lessons. For example, it analyzes questionnaires and evaluation sheets submitted by students after the lesson to collect feedback on points that students found difficult to understand and opinions on the lesson's progress. Based on this, the Feedback Department can propose specific areas for improvement to instructors, which can then be reflected in future lessons. In this way, the Feedback Department can provide specific advice to improve the quality of lessons and enhance the instructors' teaching skills.

[0063] The generation unit can analyze past lesson data and propose the optimal lesson plan. For example, the generation unit can analyze past lesson data and propose the optimal lesson plan. The generation unit can use a generation AI to analyze past lesson data and propose the optimal lesson plan. For example, the generation AI can analyze past lesson data and propose the optimal lesson plan. The generation unit can also have the generation AI analyze past lesson data and propose the optimal lesson plan. This allows the generation unit to propose the optimal lesson plan by analyzing past lesson data.

[0064] The support department can monitor the progress of the lessons and provide advice as needed. For example, the support department can monitor the progress of the lessons and provide advice as needed. The support department can use AI to monitor the progress of the lessons and provide advice as needed. For example, the support department can have AI monitor the progress of the lessons and provide advice as needed. The support department can also have AI monitor the progress of the lessons in real time and provide advice as needed. This allows for timely advice to be provided by monitoring the progress of the lessons.

[0065] The feedback department can analyze recorded lecture data and point out areas for improvement. For example, the feedback department can analyze recorded lecture data and point out areas for improvement. The feedback department can use AI to analyze recorded lecture data and point out areas for improvement. For example, the feedback department can use AI to analyze recorded lecture data and point out areas for improvement. The feedback department can also use AI to analyze recorded lecture data and point out areas for improvement. This allows for the identification of specific areas for improvement by analyzing recorded lecture data.

[0066] The generation unit can generate lesson plans and teaching materials based on past student information and teaching curricula. For example, the generation unit generates lesson plans and teaching materials based on past student information and teaching curricula. The generation unit can use a generation AI to generate lesson plans and teaching materials based on past student information and teaching curricula. For example, the generation AI generates lesson plans and teaching materials based on past student information and teaching curricula. The generation unit can also have the generation AI generate lesson plans and teaching materials based on past student information and teaching curricula. This allows for the generation of optimal lesson plans and teaching materials based on past student information and teaching curricula.

[0067] The support department can provide real-time support to instructors during lessons. For example, the support department can provide real-time support to instructors during lessons. The support department can use AI to provide real-time support to instructors during lessons. For example, the support department can use AI to provide real-time support to instructors during lessons. The support department can also use AI to provide real-time support to instructors during lessons. This enables high-quality lessons by providing real-time support during classes.

[0068] The generation unit can estimate the instructor's emotions and adjust the content of the lesson plan based on the estimated emotions. For example, if the instructor is nervous, the generation AI can suggest a lesson plan that will help them relax. If the instructor is tired, the generation AI can also suggest a concise and efficient lesson plan. If the instructor is confident, the generation AI can also suggest a lesson plan that includes challenging content. In this way, by adjusting the content of the lesson plan based on the instructor's emotions, lessons can be provided that are tailored to the instructor's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the instructor's emotion data into the generation AI and have the generation AI adjust the content of the lesson plan.

[0069] The generation unit can analyze past lesson data and generate lesson plans tailored to the learning style of specific students. For example, if a student prefers visual learning, the generation AI can generate a lesson plan that makes extensive use of diagrams and graphs. If a student prefers auditory learning, the generation AI can also generate a lesson plan that makes extensive use of audio and video. If a student prefers practical learning, the generation AI can also generate a lesson plan that includes many experiments and exercises. By generating lesson plans tailored to the learning style of specific students, the system improves students' understanding. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning style data into the generation AI and have the generation AI generate lesson plans.

[0070] The generation unit can customize lesson plans by considering students' understanding of specific subjects or topics. For example, if a student has difficulty with a particular topic, the generation AI will generate a lesson plan that focuses on that topic. For subjects in which students excel, the generation AI can also generate lesson plans that include many application problems. The generation unit can also have the generation AI adjust the pace of the lesson according to the students' understanding. This allows for the provision of effective lessons by customizing lesson plans to consider students' understanding. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student understanding data into the generation AI and have the generation AI customize the lesson plan.

[0071] The generation unit can estimate the instructor's emotions and adjust the difficulty level of the teaching materials based on the estimated emotions. For example, if the instructor is confident, the generation AI can suggest more difficult teaching materials. If the instructor is feeling anxious, the generation AI can suggest less difficult teaching materials. If the instructor is tired, the generation AI can suggest concise and easy-to-understand teaching materials. In this way, by adjusting the difficulty level of the teaching materials based on the instructor's emotions, lessons can be provided that are tailored to the instructor's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the instructor's emotion data into the generation AI and have the generation AI adjust the difficulty level of the teaching materials.

[0072] The generation unit can optimize lesson plans by considering a specific student's past performance data when generating them. For example, the generation unit can generate lesson plans that focus on areas where the generating AI struggles, based on the student's past performance data. The generation unit can also generate lesson plans that reinforce areas where the generating AI excels, including application problems, based on the student's performance data. The generation unit can also analyze the student's performance data, allowing the generating AI to adjust the pace of the lesson. This enables the provision of effective lessons by optimizing lesson plans by considering the student's past performance data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student performance data into the generating AI and have the generating AI perform the optimization of the lesson plan.

[0073] The generation unit can add relevant teaching materials based on the interests of specific students when generating lesson plans. For example, the generation unit's AI can add teaching materials related to topics that students are interested in. The generation unit can also have the generation AI add experiments or projects related to the lesson plan based on students' interests. The generation unit can also have the generation AI add videos or audio materials related to the lesson plan according to students' interests. This allows for the provision of effective lessons by adding relevant teaching materials based on students' interests. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student interest data into the generation AI and have the generation AI add relevant teaching materials.

[0074] The support unit can estimate the instructor's emotions and adjust the content of in-class advice based on the estimated emotions. For example, if the instructor is nervous, the AI ​​can provide advice to help them relax. If the instructor is tired, the AI ​​can also provide advice on how to conduct the lesson efficiently. If the instructor is confident, the AI ​​can also provide advice that includes challenging content. In this way, by adjusting the content of in-class advice based on the instructor's emotions, support can be provided that is appropriate to the instructor's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the instructor's emotion data into a generative AI and have the generative AI adjust the content of in-class advice.

[0075] The support unit can monitor the progress of the lesson, analyze student responses in real time, and provide feedback to the instructor. For example, the support unit can analyze students' facial expressions and attitudes, and the AI ​​can provide real-time feedback to the instructor. The support unit can also evaluate students' understanding in real time, and the AI ​​can suggest additional explanations or supplementary information to the instructor. The support unit can monitor the progress of the lesson, and the AI ​​can suggest to the instructor that they adjust the pace of the lesson. This allows for effective feedback by monitoring the progress of the lesson and analyzing student responses in real time. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input student response data into a generating AI and have the generating AI provide feedback.

[0076] The support unit can assess a particular student's understanding in real time during a lesson and provide additional explanations or supplementary materials as needed. For example, the support unit can assess a student's understanding in real time and have the AI ​​suggest additional explanations to the instructor. The support unit can also have the AI ​​provide supplementary materials if a student's understanding of a particular topic is insufficient. The support unit can also have the AI ​​suggest adjusting the pace of the lesson based on the student's understanding. This allows for the provision of additional explanations or supplementary materials as needed by assessing a particular student's understanding in real time. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input student understanding data into a generating AI and have the generating AI provide additional explanations or supplementary materials.

[0077] The support unit can estimate the instructor's emotions and adjust the frequency of support during class based on the estimated emotions. For example, if the instructor is confident, the AI ​​can reduce the frequency of support. If the instructor is feeling anxious, the AI ​​can increase the frequency of support. If the instructor is tired, the AI ​​can provide more efficient support. This allows for support tailored to the instructor's state by adjusting the frequency of support during class based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the instructor's emotion data into a generative AI and have the generative AI adjust the frequency of support during class.

[0078] The support unit can monitor the progress of the lesson, assess the concentration level of specific students, and suggest breaks at appropriate times. For example, if a student's concentration level is declining, the AI ​​can suggest a break to the instructor. The support unit can also monitor the progress of the lesson and have the AI ​​suggest breaks at appropriate times. The support unit can analyze student responses and have the AI ​​suggest break timings to the instructor. This allows for suggesting breaks at appropriate times by assessing the concentration level of specific students. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input student concentration data into a generating AI and have the generating AI execute break suggestions.

[0079] The support unit can refer to a specific student's question history during class and provide relevant additional information. For example, the support unit can refer to a student's question history and have the AI ​​provide relevant additional information. The support unit can also have the AI ​​provide relevant supplementary materials based on questions the student has asked in the past. The support unit can also analyze a student's question history and have the AI ​​provide additional information in line with the progress of the class. This allows the support unit to provide relevant additional information by referring to a specific student's question history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input student question history data into a generating AI and have the generating AI perform the task of providing additional information.

[0080] The feedback unit can estimate the instructor's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the instructor is confident, the AI ​​can suggest challenging areas for improvement. If the instructor is feeling anxious, the AI ​​can also suggest specific and actionable areas for improvement. If the instructor is tired, the AI ​​can also suggest concise and efficient areas for improvement. In this way, by adjusting the content of the feedback based on the instructor's emotions, appropriate feedback can be provided according to the instructor's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the instructor's emotion data into the generative AI and have the generative AI adjust the content of the feedback.

[0081] The feedback unit can analyze recorded lesson data, evaluate the instructor's responses to specific students' comments and questions, and provide feedback. For example, the feedback unit can analyze recorded lesson data and have AI provide feedback on the instructor's strengths. The feedback unit can also evaluate the instructor's responses to students' comments and questions, and the AI ​​can suggest areas for improvement. Based on the recorded lesson data, the feedback unit can also have AI evaluate the consistency of the instructor's responses and provide feedback. This allows for specific feedback on the instructor's strengths and areas for improvement by analyzing the recorded lesson data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input recorded lesson data into a generating AI and have the generating AI perform the evaluation of the instructor's responses and provide feedback.

[0082] The feedback unit can evaluate students' understanding after class and suggest specific areas for improvement for the next class. For example, the feedback unit can evaluate students' understanding and have the AI ​​suggest specific areas for improvement for the next class. The feedback unit can also collect student feedback after class and have the AI ​​suggest areas for improvement to the instructor. Based on students' understanding, the feedback unit can also have the AI ​​suggest adjustments to the pace and content of the next class. This allows for the suggestion of specific areas for improvement for the next class by evaluating students' understanding. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input student understanding data into a generating AI and have the generating AI generate suggestions for improvements for the next class.

[0083] The feedback unit can estimate the instructor's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the instructor is tired, the AI ​​in the feedback unit can delay the timing of feedback. If the instructor is relaxed, the AI ​​in the feedback unit can also speed up the timing of feedback. If the instructor is in a hurry, the AI ​​in the feedback unit can provide concise feedback. This allows for providing feedback at an appropriate time according to the instructor's state by adjusting the timing of feedback based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input the instructor's emotion data into the generative AI and have the generative AI adjust the timing of feedback.

[0084] The feedback unit can analyze recorded lesson data, evaluate the responses of specific students, and provide feedback. For example, the feedback unit can analyze recorded lesson data, and the AI ​​can evaluate the students' responses and provide feedback. Based on the students' responses, the AI ​​can also suggest areas for improvement to the instructor. Based on the recorded lesson data, the AI ​​can also evaluate the consistency of the students' responses and provide feedback. This allows for the evaluation of student responses and the provision of specific feedback to instructors by analyzing recorded lesson data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input recorded lesson data into a generating AI and have the generating AI perform the evaluation of student responses and provide feedback.

[0085] The feedback unit can evaluate students' learning progress after class and propose specific action plans for the next class. For example, the feedback unit can evaluate students' learning progress and have the AI ​​propose specific action plans for the next class. The feedback unit can also collect student progress data after class and have the AI ​​propose action plans for the instructor. Based on students' learning progress, the feedback unit can also have the AI ​​suggest adjustments to the pace and content of the next class. This allows for the proposal of specific action plans for the next class by evaluating students' learning progress. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input student learning progress data into a generating AI and have the generating AI propose action plans for the next class.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The generation unit can optimize lesson plans by considering the instructor's past lesson evaluation data. For example, it can analyze the teaching styles in which the instructor has received high ratings in the past and generate lesson plans that incorporate similar styles. It can also adjust lesson plans to avoid lesson content in which the instructor has received low ratings in the past. Furthermore, based on the instructor's evaluation data, the generating AI can adjust the pace of the lesson and the difficulty level of the teaching materials. In this way, by optimizing lesson plans while considering the instructor's past lesson evaluation data, effective lessons can be provided.

[0088] The support system can estimate students' emotions during class and adjust the pace of the lesson based on those estimates. For example, if students are bored, the AI ​​can suggest to the instructor that they speed up the lesson. If students are excited, the AI ​​can also suggest to the instructor that they calm down the lesson. If students are feeling anxious, the AI ​​can also suggest to the instructor that they provide additional explanations or supplementary information. This allows for more effective lessons by adjusting the pace of the lesson based on students' emotions.

[0089] The feedback system can estimate students' emotions after a lesson and adjust the content of the feedback based on those estimates. For example, if a student is satisfied with the lesson, the AI ​​will provide positive feedback to the instructor. If a student is dissatisfied with the lesson, the AI ​​can also suggest specific areas for improvement. If a student has neutral feelings about the lesson, the AI ​​can also provide advice for the next lesson. This allows for more effective feedback by tailoring the content of the feedback based on the student's emotions.

[0090] The generation unit can customize lesson plans by considering the specific home environment and background information of individual students. For example, if a student has limited study time at home, the generation AI can create an efficient lesson plan. If a student has a specific cultural background, the generation AI can also create a lesson plan that takes that background into account. If a student requires special support, the generation AI can also create a lesson plan that takes that support into consideration. In this way, by customizing lesson plans to take into account students' home environment and background information, effective lessons can be provided.

[0091] The support system can estimate students' emotions during class and provide real-time advice based on those estimates. For example, if a student is confused, the AI ​​can suggest that the instructor provide additional explanations. If a student is agitated, the AI ​​can also suggest that the instructor calm down the pace of the lesson. If a student is bored, the AI ​​can also suggest that the instructor speed up the pace of the lesson. This allows for more effective lessons by providing real-time advice based on students' emotions.

[0092] The feedback system can evaluate students' motivation after class and suggest specific strategies to improve their motivation for the next lesson. For example, if students are highly motivated, the AI ​​can suggest challenging tasks. If students are less motivated, the AI ​​can suggest engaging learning materials. If students are moderately motivated, the AI ​​can suggest a well-balanced lesson plan. By evaluating students' motivation and suggesting strategies to improve it for the next lesson, the system can deliver more effective lessons.

[0093] The generation unit can adjust lesson plans while considering the health condition of specific students. For example, if a student is tired, the generation AI will generate a concise and efficient lesson plan. If a student is unwell, the generation AI can also generate a less burdensome lesson plan. If a student is healthy, the generation AI can also generate a lesson plan that includes active activities. In this way, by adjusting lesson plans to take into account the health condition of students, effective lessons can be provided.

[0094] The support system can estimate students' emotions during class and adjust the pace of the lesson based on those estimates. For example, if a student is excited, the AI ​​can suggest to the instructor that the lesson be conducted in a calmer manner. If a student is bored, the AI ​​can also suggest to the instructor that the lesson be conducted more quickly. If a student is confused, the AI ​​can also suggest to the instructor that they provide additional explanations. This allows for more effective lessons by adjusting the pace of the lesson based on students' emotions.

[0095] The feedback system can estimate students' emotions after a lesson and adjust the content of the next lesson based on those estimates. For example, if students are satisfied with the lesson, the AI ​​will suggest a lesson plan that includes similar content. If students are dissatisfied, the AI ​​can suggest a lesson plan that reflects areas for improvement. If students have neutral emotions towards the lesson, the AI ​​can suggest a lesson plan that incorporates a new approach. This allows for more effective lessons by adjusting the content of the next lesson based on students' emotions.

[0096] The generation unit can optimize lesson plans by considering the learning objectives of specific students. For example, if a student is preparing for a particular exam, the generation AI will generate a lesson plan tailored to that exam. If a student wants to acquire a specific skill, the generation AI can also generate a lesson plan focused on that skill. If a student aims for overall academic improvement, the generation AI can generate a balanced lesson plan. This allows for the provision of effective lessons by optimizing lesson plans to consider students' learning objectives.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The generation unit generates lesson plans and teaching materials based on past student information and teaching curricula. The generation unit uses a generation AI to analyze past lesson data and propose the optimal lesson plan. The generation unit can also use the generation AI to analyze past student information and generate teaching materials best suited to each individual student. The generation unit can also use the generation AI to analyze past teaching curricula and generate the optimal lesson plan. Step 2: The support team provides real-time support during class. The support team uses AI to monitor the progress of the class and provides advice as needed. Step 3: The feedback team provides feedback after the lesson. The feedback team uses AI to analyze the recorded lesson data and point out areas for improvement.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements, including the generation unit, support unit, and feedback unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates lesson plans and teaching materials based on past student information and instructional curricula. The support unit is implemented by the control unit 46A of the smart device 14 and provides real-time support during lessons. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback after lessons. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements, including the generation unit, support unit, and feedback unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates lesson plans and teaching materials based on past student information and instructional curricula. The support unit is implemented by the control unit 46A of the smart glasses 214, which provides real-time support during lessons. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides feedback after lessons. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements, including the generation unit, support unit, and feedback unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates lesson plans and teaching materials based on past student information and instructional curricula. The support unit is implemented by the control unit 46A of the headset terminal 314, which provides real-time support during lessons. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides feedback after lessons. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements, including the generation unit, support unit, and feedback unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates lesson plans and teaching materials based on past student information and instructional curricula. The support unit is implemented by the control unit 46A of the robot 414, which provides real-time support during lessons. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides feedback after lessons. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A generation unit that generates lesson plans and teaching materials based on past student information and instructional curriculum, The support department provides real-time support during classes, It includes a feedback department that provides feedback after class. A system characterized by the following features. (Note 2) The generating unit is We analyze past lesson data and propose the optimal lesson plan. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is Monitor the progress of the lesson and provide advice as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Analyze the recorded lecture data and identify areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on past student information and teaching curriculum, lesson plans and teaching materials are generated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is Provide real-time support to the instructor during class. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The system estimates the instructor's emotions and adjusts the lesson plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is By analyzing past lesson data, we generate lesson plans tailored to the learning style of specific students. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating lesson plans, customize them to take into account students' level of understanding of specific subjects or topics. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates the instructor's emotions and adjusts the difficulty level of the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating lesson plans, optimize the plan by considering the past performance data of specific students. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating lesson plans, add relevant materials based on the interests and concerns of specific students. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned support unit is The system estimates the instructor's emotions and adjusts the content of the advice given during the lesson based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned support unit is The system monitors the progress of the lesson, analyzes student responses in real time, and provides feedback to the instructor. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned support unit is During class, the system assesses the understanding of individual students in real time and provides additional explanations or supplementary materials as needed. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is The system estimates the instructor's emotions and adjusts the frequency of support during lessons based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is Monitor the progress of the lesson, assess the concentration levels of individual students, and suggest breaks at appropriate times. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is During class, refer to a specific student's question history and provide relevant additional information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is The system estimates the instructor's emotions and adjusts the content of the feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is We analyze recorded lesson data to evaluate the instructor's responses to specific students' comments and questions, and provide feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is After the lesson, assess the students' level of understanding and propose specific areas for improvement for the next lesson. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is The system estimates the instructor's emotions and adjusts the timing of feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is Analyze recorded lesson data to evaluate the responses of specific students and provide feedback. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is After the lesson, assess the students' learning progress and propose a concrete action plan for the next lesson. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that generates lesson plans and teaching materials based on past student information and instructional curriculum, The support department provides real-time support during classes, It includes a feedback department that provides feedback after class. A system characterized by the following features.

2. The generating unit is We analyze past lesson data and propose the optimal lesson plan. The system according to feature 1.

3. The aforementioned support unit is Monitor the progress of the lesson and provide advice as needed. The system according to feature 1.

4. The aforementioned feedback unit is Analyze the recorded lecture data and identify areas for improvement. The system according to feature 1.

5. The generating unit is Based on past student information and teaching curriculum, lesson plans and teaching materials are generated. The system according to feature 1.

6. The aforementioned support unit is Provide real-time support to the instructor during class. The system according to feature 1.

7. The generating unit is The system estimates the instructor's emotions and adjusts the lesson plan based on those emotions. The system according to feature 1.

8. The generating unit is By analyzing past lesson data, we generate lesson plans tailored to the learning style of specific students. The system according to feature 1.

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