system

Abstract

The system according to this embodiment aims to propose an optimal educational plan and teaching materials based on each student's learning progress and level of understanding, and to provide individualized instruction and feedback in real time. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, and a teaching unit. The analysis unit analyzes each student's learning progress and level of understanding in real time. The proposal unit proposes an optimal educational plan and teaching materials based on the analysis results obtained by the analysis unit. The teaching unit provides individualized instruction and feedback in real time based on the educational plan and teaching materials proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide an optimal education plan and teaching materials in real time according to the learning progress and understanding degree of each student, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal education plan and teaching materials based on the learning progress and understanding degree of each student, and provide individual guidance and feedback in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, and an instruction unit. The analysis unit analyzes each student's learning progress and level of understanding in real time. The proposal unit proposes an optimal educational plan and teaching materials based on the analysis results obtained by the analysis unit. The instruction unit provides individualized instruction and feedback in real time based on the educational plan and teaching materials proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal educational plan and teaching materials based on each student's learning progress and level of understanding, and can provide individualized instruction and feedback in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is a system that supports learning by proposing the most effective educational plan and teaching materials based on each student's learning progress and level of understanding. This educational support system analyzes each student's learning progress and level of understanding in real time and automatically proposes the optimal educational plan and teaching materials. Furthermore, it provides individualized guidance and feedback in real time during learning. This mechanism enables individualized guidance tailored to the needs of each student, reduces the workload of teachers, and helps maintain learning motivation. For example, the educational support system collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes this data. For example, it analyzes the speed at which students solve math problems, their accuracy rate, and the results of quizzes to evaluate their level of understanding. This allows the system to grasp each student's learning progress and level of understanding. Next, the educational support system automatically proposes the optimal educational plan and teaching materials based on the analysis results. For example, for areas where understanding is low, it proposes teaching materials that allow students to learn step by step from basic to advanced levels, and for students who are learning quickly, it provides teaching materials that include more advanced content. This allows the system to provide each student with the optimal learning plan. Furthermore, the educational support system provides individualized guidance and feedback in real time during learning. For example, while a student is solving a problem, the system can point out their mistakes and teach them the correct solution. It can also provide additional explanations and hints if a student is struggling to understand. This allows students to learn effectively at their own pace. This system enables individualized instruction tailored to each student's needs, reducing the workload on teachers. Furthermore, it can maintain learning motivation and reduce educational disparities. For example, it could be used in tutoring services and educational institutions for students from elementary to high school. This would allow for the provision of higher-quality education. The educational support system proposes optimal learning plans and materials based on the student's learning progress and understanding, and provides real-time individualized instruction and feedback, enabling effective learning support.

[0029] The educational support system according to this embodiment comprises an analysis unit, a proposal unit, and an instruction unit. The analysis unit analyzes each student's learning progress and level of understanding in real time. The analysis unit collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes this data. For example, the analysis unit analyzes the speed at which students solve math problems, their accuracy rate, and the results of quizzes to evaluate their level of understanding. This allows the analysis unit to grasp each student's learning progress and level of understanding. The proposal unit proposes an optimal educational plan and teaching materials based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes teaching materials that allow students to learn step-by-step from basic to advanced levels in areas where their understanding is low. The proposal unit can also propose teaching materials containing more advanced content for students who are learning quickly. This allows the proposal unit to provide each student with an optimal learning plan. The instruction unit provides individualized instruction and feedback in real time based on the educational plan and teaching materials proposed by the proposal unit. For example, the instruction unit points out where a student made a mistake while solving a problem and teaches them the correct way to solve it. The instructional staff can also provide additional explanations and hints if students are having difficulty understanding. This allows students to learn effectively at their own pace. As a result, the educational support system according to this embodiment can provide effective learning support by suggesting optimal educational plans and materials based on students' learning progress and understanding, and by providing real-time individualized instruction and feedback.

[0030] The analytics department analyzes each student's learning progress and comprehension in real time. Specifically, it collects data such as each student's learning history, test results, and assignment submission status, and AI analyzes this data. For example, it analyzes in detail the speed and accuracy of solving math problems, as well as the results of quizzes to evaluate comprehension. Using machine learning algorithms, the AI ​​analyzes students' answer patterns and error tendencies to identify areas of low comprehension and areas of learning progress. Furthermore, the analytics department tracks students' learning history over time and evaluates learning retention by comparing past learning content with current comprehension. This allows for a clear understanding of each student's strengths and weaknesses and provides data to address individual learning needs. The analytics department can also use AI to cluster students' learning patterns and identify groups of students with similar learning tendencies. This provides foundational data for developing optimal learning strategies for each group. The analytics department processes this data in real time and provides it to teachers and other departments of the educational support system, enabling rapid and accurate educational support.

[0031] The Proposal Department proposes optimal educational plans and materials based on the analysis results obtained by the Analysis Department. Specifically, it proposes materials that allow students to learn progressively from basic to advanced levels in areas where their understanding is weak. For example, for students who do not understand basic mathematical concepts, it provides materials that start with basic problems and gradually increase in difficulty. On the other hand, for students who are learning quickly, it proposes materials that include more advanced content and provides opportunities to solve challenging problems. The Proposal Department uses AI to analyze each student's learning history and level of understanding and automatically selects the most suitable materials. Furthermore, the Proposal Department can also customize materials based on students' interests and concerns. For example, for students interested in science, it can provide math problems that include scientific topics to increase their motivation to learn. The Proposal Department also dynamically updates materials according to the student's learning progress, ensuring that students always have access to the most appropriate learning content. In this way, the Proposal Department can provide each student with an optimal learning plan and support effective learning.

[0032] The instruction department provides real-time individualized instruction and feedback based on the educational plans and materials proposed by the proposal department. Specifically, it points out where students made mistakes while solving problems and teaches them the correct way to solve them. For example, if a student makes a calculation error while solving a math problem, the instruction department will immediately point out the error and show the correct calculation method. If a student is having trouble understanding, it will provide additional explanations and hints to support their understanding. The instruction department uses AI to analyze students' answers in real time and automatically generate appropriate feedback. Furthermore, the instruction department flexibly adjusts the content of instruction according to the student's learning progress to meet individual needs. For example, if a student is not making progress in a particular area, the instruction department will focus on that area to improve their understanding. The instruction department can also appropriately insert words of praise and encouragement to increase students' motivation to learn. In this way, the instruction department can support students in learning effectively at their own pace and maximize learning outcomes.

[0033] The analysis unit can collect data such as each student's learning history, test results, and assignment submission status, and have it analyzed by AI. For example, the analysis unit can collect each student's learning history and analyze learning time, learning content, and learning outcomes. The analysis unit can also collect test results and analyze test type, score, and correct answer rate. Furthermore, the analysis unit can collect assignment submission status and analyze submission deadlines, number of submissions, and submission content. This allows the analysis unit to accurately grasp each student's learning progress and level of understanding. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data such as each student's learning history, test results, and assignment submission status into a generating AI, and have the generating AI perform the analysis.

[0034] The proposal department can propose learning materials that allow students to learn progressively from basic to advanced levels in areas where they have a low level of understanding. For example, the proposal department can identify areas where students have a low level of understanding and propose learning materials that allow them to learn progressively from basic to advanced levels in those areas. The proposal department can also propose learning materials that start with basic materials and gradually increase in difficulty in areas where students have a low level of understanding. Furthermore, the proposal department can improve students' understanding by proposing learning materials that allow them to learn progressively in areas where they have a low level of understanding. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input data to identify areas where students have a low level of understanding into a generating AI, and have the generating AI propose the most suitable learning materials.

[0035] The suggestion unit can suggest learning materials containing more advanced content to students who are progressing quickly. For example, the suggestion unit can identify students who are progressing quickly and suggest learning materials containing more advanced content to those students. The suggestion unit can also suggest advanced-level or specialized learning materials to students who are progressing quickly. Furthermore, by suggesting learning materials containing more advanced content to students who are progressing quickly, the suggestion unit can increase students' motivation to learn. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data to identify students who are progressing quickly into a generating AI, and have the generating AI suggest the most suitable learning materials.

[0036] The instruction department can point out mistakes made by students while they are solving problems and teach them the correct way to solve them. For example, the instruction department can identify and point out mistakes made by students while they are solving problems. The instruction department can also teach the correct way to solve the problem after pointing out the mistakes. Furthermore, the instruction department can improve students' understanding by pointing out mistakes made by students while they are solving problems and teaching them the correct way to solve them. Some or all of the above processes in the instruction department may be performed using AI, for example, or not using AI. For example, the instruction department can input data on problems solved by students into a generating AI, which can then identify mistakes and teach the correct way to solve them.

[0037] The instructional department can provide additional explanations or hints when students are having difficulty understanding. For example, the instructional department can identify areas where students are having difficulty understanding and provide additional explanations for those areas. The instructional department can also provide hints that can help students solve problems when they are having difficulty understanding. Furthermore, the instructional department can improve students' understanding by providing additional explanations or hints when students are having difficulty understanding. Some or all of the above processes in the instructional department may be performed using AI, for example, or not using AI. For example, the instructional department can input data on areas where students are having difficulty understanding into a generating AI, and have the generating AI perform the task of providing additional explanations or hints.

[0038] The analysis unit can perform analysis considering the student's learning environment, in addition to learning history, test results, and assignment submission status. For example, if a student studies at night, the analysis unit will consider their nighttime learning performance. The analysis unit can also consider the home learning environment if a student studies at home. Furthermore, if a student studies in the library, the analysis unit can consider their level of concentration in the library. This allows the analysis unit to perform more accurate analysis by considering the learning environment. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning environment data into a generating AI, which can then perform analysis that takes the learning environment into account.

[0039] The analysis unit can evaluate a student's learning progress and understanding by comparing it with past learning data. For example, the analysis unit can evaluate progress by comparing a student's past test results with their current test results. The analysis unit can also evaluate progress by comparing a student's past assignment submission status with their current assignment submission status. Furthermore, the analysis unit can evaluate progress by comparing a student's past learning speed with their current learning speed. In this way, the analysis unit can accurately grasp the student's learning progress by evaluating it by comparing it with past learning data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a student's past learning data into a generating AI and have the generating AI perform the progress evaluation.

[0040] The analysis unit can analyze students' social media activity in addition to their learning history, test results, and assignment submission status to assess their learning motivation. For example, the analysis unit can analyze the content of students' social media posts to assess their learning motivation. The analysis unit can also analyze the frequency of students' social media activity to assess their learning motivation. Furthermore, the analysis unit can analyze students' social media friendships to assess their learning motivation. In this way, the analysis unit can accurately assess students' learning motivation by analyzing their social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input students' social media activity data into a generating AI and have the generating AI perform the evaluation of learning motivation.

[0041] The analysis unit can perform analyses of learning progress and comprehension while taking into account the student's geographical location. For example, if a student lives in an urban area, the analysis unit will take into account the urban learning environment. The analysis unit can also take into account the rural learning environment if the student lives in a rural area. Furthermore, if a student lives overseas, the analysis unit can take into account the overseas learning environment. This allows the analysis unit to perform more accurate analyses by taking geographical location information into account. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the student's geographical location data into a generating AI, and have the generating AI perform an analysis that takes geographical location information into account.

[0042] The suggestion function can propose learning materials that allow for step-by-step learning from basic to advanced levels, as well as related supplementary materials, for areas where understanding is low. For example, the suggestion function can propose learning materials that allow for step-by-step learning from basic to advanced levels for areas where understanding is low. The suggestion function can also propose related video tutorials for areas where understanding is low. Furthermore, the suggestion function can propose interactive learning tools for areas where understanding is low. In this way, the suggestion function can support learning in areas where understanding is low by proposing related supplementary materials. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input data to identify areas where understanding is low into a generating AI, and have the generating AI propose the most suitable supplementary materials.

[0043] The suggestion unit can suggest challenging problems and project-based learning to students who are progressing quickly, in addition to teaching materials containing more advanced content. For example, the suggestion unit can suggest teaching materials containing more advanced content to students who are progressing quickly. The suggestion unit can also suggest challenging problems to students who are progressing quickly. Furthermore, the suggestion unit can also suggest project-based learning to students who are progressing quickly. In this way, the suggestion unit can increase the motivation of students who are progressing quickly by suggesting challenging problems and project-based learning. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data to identify students who are progressing quickly into a generating AI, and have the generating AI suggest the most suitable challenging problems and project-based learning.

[0044] The proposal function can propose interactive learning tools in addition to learning materials that allow for step-by-step learning from basic to advanced levels for areas where understanding is low. For example, the proposal function can propose interactive quizzes for areas where understanding is low. The proposal function can also propose simulation tools for areas where understanding is low. Furthermore, the proposal function can propose game-based learning tools for areas where understanding is low. In this way, the proposal function can support learning in areas where understanding is low by proposing interactive learning tools. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function can input data to identify areas where understanding is low into a generating AI, and have the generating AI propose the most suitable interactive learning tools.

[0045] The suggestion unit can suggest collaborative learning with other students, in addition to more advanced learning materials, to students who are progressing quickly. For example, the suggestion unit can suggest collaborative projects to students who are progressing quickly. The suggestion unit can also suggest discussion groups to students who are progressing quickly. Furthermore, the suggestion unit can suggest pair work to students who are progressing quickly. In this way, the suggestion unit can increase the motivation of students who are progressing quickly by suggesting collaborative learning with other students. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data to identify students who are progressing quickly into a generating AI, and have the generating AI make optimal collaborative learning suggestions.

[0046] The instructional department can not only point out where students made mistakes while they are solving problems, but also explain the reasons for those mistakes and allow them to try again. For example, the instructional department can point out where the student made a mistake and explain the reason for it. The instructional department can also point out where the student made a mistake and allow them to try again. Furthermore, the instructional department can point out where the student made a mistake and teach them the correct way to solve the problem. In this way, the instructional department can improve the student's understanding by explaining the reasons for mistakes and allowing them to try again. Some or all of the above processes in the instructional department may be performed using AI, for example, or not using AI. For example, the instructional department can input data on problems solved by students into a generating AI, which can then perform explanations of the reasons for mistakes and provide guidance for retrying.

[0047] The instructional staff can provide relevant video tutorials, in addition to additional explanations and hints, when students are having difficulty understanding. For example, the instructional staff can provide additional explanations when students are having difficulty understanding. The instructional staff can also provide hints when students are having difficulty understanding. Furthermore, the instructional staff can also provide relevant video tutorials when students are having difficulty understanding. This allows the instructional staff to improve students' understanding by providing relevant video tutorials. Some or all of the above processes in the instructional staff may be performed using AI, for example, or not using AI. For example, the instructional staff can input data on the parts where students are having difficulty understanding into a generating AI, which can then perform the task of providing additional explanations, hints, and video tutorials.

[0048] The instructional department can not only point out mistakes made by students while they are solving problems, but also deepen their understanding by presenting similar problems. For example, the instructional department can point out where a student made a mistake and present similar problems. The instructional department can also point out where a student made a mistake and have them try again. Furthermore, the instructional department can point out where a student made a mistake and teach them the correct way to solve the problem. In this way, the instructional department can improve students' understanding by presenting similar problems. Some or all of the above processes in the instructional department may be performed using AI, for example, or not using AI. For example, the instructional department can input data on problems solved by students into a generating AI, which can then generate similar problems and provide instruction.

[0049] The instructional staff can provide additional explanations and hints, as well as encourage discussion with other students, when students are having difficulty understanding. For example, the instructional staff can provide additional explanations when students are having difficulty understanding. The instructional staff can also provide hints when students are having difficulty understanding. Furthermore, the instructional staff can encourage discussion with other students when students are having difficulty understanding. This allows the instructional staff to improve students' understanding by encouraging discussion with other students. Some or all of the above processes in the instructional staff may be performed using AI, for example, or not using AI. For example, the instructional staff can input data on the parts where students are having difficulty understanding into a generating AI, which can then perform additional explanations, hints, and facilitate discussion.

[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 analytics unit not only analyzes students' learning progress and comprehension in real time, but also identifies learning patterns and can create long-term learning plans. For example, if a student tends to concentrate more at certain times of the day, the analytics unit can suggest a learning plan tailored to those times. Furthermore, if a student has difficulty with a particular subject, the analytics unit can gradually adjust the learning plan for that subject. In addition, based on a student's learning history, the analytics unit can predict future learning progress and update learning content at appropriate times. This allows the analytics unit to provide more effective learning support by offering long-term learning plans that take students' learning patterns into account.

[0052] The suggestion function not only proposes learning materials that allow students to progress from basic to advanced levels in areas where they have a low level of understanding, but it can also dynamically adjust the difficulty level of the materials according to the student's learning progress. For example, if a student understands a particular unit, the suggestion function can propose additional application problems related to that unit. Furthermore, if a student repeatedly makes mistakes on a particular problem, the suggestion function can provide supplementary materials for that problem. In addition, the suggestion function can automatically select the next topic to be learned according to the student's learning progress, making the learning flow smoother. In this way, the suggestion function can improve students' understanding by providing dynamic learning material suggestions that are tailored to their learning progress.

[0053] The instruction department can not only point out where students made mistakes while solving problems and teach them the correct solutions, but also have them review the foundational knowledge related to those mistakes. For example, if a student makes a mistake on a particular problem, the instruction department can provide materials to review the foundational knowledge related to that problem. The instruction department can also provide additional practice problems to help students understand where they made mistakes. Furthermore, the instruction department can provide step-by-step guides to help students overcome their mistakes. In this way, the instruction department can improve students' understanding through reviewing the foundational knowledge related to the areas where they made mistakes.

[0054] The analysis unit can perform analyses that take into account not only students' learning history, test results, and assignment submission status, but also their learning style. For example, if a student prefers visual learning, the analysis unit can suggest a learning plan that includes many visual materials. Similarly, if a student prefers auditory learning, it can suggest a learning plan that includes many audio materials. Furthermore, if a student prefers practical learning, the analysis unit can suggest a learning plan that includes many practical exercises. In this way, the analysis unit can perform analyses that take into account students' learning styles, enabling more effective learning support.

[0055] The proposal department can not only suggest more advanced learning materials to students who are progressing quickly, but also propose challenging projects tailored to their learning progress. For example, the proposal department can suggest projects focused on solving real-world problems to students who are progressing quickly. It can also suggest group projects that students can work on collaboratively with other students. Furthermore, the proposal department can suggest research projects to deepen specialized knowledge to students who are progressing quickly. In this way, the proposal department can enhance the learning motivation of students who are progressing quickly through challenging projects.

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

[0057] Step 1: The analysis unit analyzes each student's learning progress and understanding in real time. The analysis unit collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes it. For example, it analyzes the speed at which students solve math problems, their accuracy rate, and the results of quizzes to evaluate their understanding. This allows the system to understand each student's learning progress and understanding. Step 2: The proposal department proposes the optimal educational plan and teaching materials based on the analysis results obtained by the analysis department. The proposal department proposes teaching materials that allow students to learn step-by-step from basic to advanced levels in areas where they have a low level of understanding. They can also propose teaching materials containing more advanced content for students who are learning at a faster pace. This allows for the provision of an optimal learning plan for each student. Step 3: The instruction team provides real-time individualized instruction and feedback based on the educational plan and materials proposed by the proposal team. The instruction team points out where students made mistakes while solving problems and teaches them the correct way to solve them. They can also provide additional explanations and hints if students are having difficulty understanding. This allows students to learn effectively at their own pace.

[0058] (Example of form 2) The educational support system according to an embodiment of the present invention is a system that supports learning by proposing the most effective educational plan and teaching materials based on each student's learning progress and level of understanding. This educational support system analyzes each student's learning progress and level of understanding in real time and automatically proposes the optimal educational plan and teaching materials. Furthermore, it provides individualized guidance and feedback in real time during learning. This mechanism enables individualized guidance tailored to the needs of each student, reduces the workload of teachers, and helps maintain learning motivation. For example, the educational support system collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes this data. For example, it analyzes the speed at which students solve math problems, their accuracy rate, and the results of quizzes to evaluate their level of understanding. This allows the system to grasp each student's learning progress and level of understanding. Next, the educational support system automatically proposes the optimal educational plan and teaching materials based on the analysis results. For example, for areas where understanding is low, it proposes teaching materials that allow students to learn step by step from basic to advanced levels, and for students who are learning quickly, it provides teaching materials that include more advanced content. This allows the system to provide each student with the optimal learning plan. Furthermore, the educational support system provides individualized guidance and feedback in real time during learning. For example, while a student is solving a problem, the system can point out their mistakes and teach them the correct solution. It can also provide additional explanations and hints if a student is struggling to understand. This allows students to learn effectively at their own pace. This system enables individualized instruction tailored to each student's needs, reducing the workload on teachers. Furthermore, it can maintain learning motivation and reduce educational disparities. For example, it could be used in tutoring services and educational institutions for students from elementary to high school. This would allow for the provision of higher-quality education. The educational support system proposes optimal learning plans and materials based on the student's learning progress and understanding, and provides real-time individualized instruction and feedback, enabling effective learning support.

[0059] The educational support system according to this embodiment comprises an analysis unit, a proposal unit, and an instruction unit. The analysis unit analyzes each student's learning progress and level of understanding in real time. The analysis unit collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes this data. For example, the analysis unit analyzes the speed at which students solve math problems, their accuracy rate, and the results of quizzes to evaluate their level of understanding. This allows the analysis unit to grasp each student's learning progress and level of understanding. The proposal unit proposes an optimal educational plan and teaching materials based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes teaching materials that allow students to learn step-by-step from basic to advanced levels in areas where their understanding is low. The proposal unit can also propose teaching materials containing more advanced content for students who are learning quickly. This allows the proposal unit to provide each student with an optimal learning plan. The instruction unit provides individualized instruction and feedback in real time based on the educational plan and teaching materials proposed by the proposal unit. For example, the instruction unit points out where a student made a mistake while solving a problem and teaches them the correct way to solve it. The instructional staff can also provide additional explanations and hints if students are having difficulty understanding. This allows students to learn effectively at their own pace. As a result, the educational support system according to this embodiment can provide effective learning support by suggesting optimal educational plans and materials based on students' learning progress and understanding, and by providing real-time individualized instruction and feedback.

[0060] The analytics department analyzes each student's learning progress and comprehension in real time. Specifically, it collects data such as each student's learning history, test results, and assignment submission status, and AI analyzes this data. For example, it analyzes in detail the speed and accuracy of solving math problems, as well as the results of quizzes to evaluate comprehension. Using machine learning algorithms, the AI ​​analyzes students' answer patterns and error tendencies to identify areas of low comprehension and areas of learning progress. Furthermore, the analytics department tracks students' learning history over time and evaluates learning retention by comparing past learning content with current comprehension. This allows for a clear understanding of each student's strengths and weaknesses and provides data to address individual learning needs. The analytics department can also use AI to cluster students' learning patterns and identify groups of students with similar learning tendencies. This provides foundational data for developing optimal learning strategies for each group. The analytics department processes this data in real time and provides it to teachers and other departments of the educational support system, enabling rapid and accurate educational support.

[0061] The Proposal Department proposes optimal educational plans and materials based on the analysis results obtained by the Analysis Department. Specifically, it proposes materials that allow students to learn progressively from basic to advanced levels in areas where their understanding is weak. For example, for students who do not understand basic mathematical concepts, it provides materials that start with basic problems and gradually increase in difficulty. On the other hand, for students who are learning quickly, it proposes materials that include more advanced content and provides opportunities to solve challenging problems. The Proposal Department uses AI to analyze each student's learning history and level of understanding and automatically selects the most suitable materials. Furthermore, the Proposal Department can also customize materials based on students' interests and concerns. For example, for students interested in science, it can provide math problems that include scientific topics to increase their motivation to learn. The Proposal Department also dynamically updates materials according to the student's learning progress, ensuring that students always have access to the most appropriate learning content. In this way, the Proposal Department can provide each student with an optimal learning plan and support effective learning.

[0062] The instruction department provides real-time individualized instruction and feedback based on the educational plans and materials proposed by the proposal department. Specifically, it points out where students made mistakes while solving problems and teaches them the correct way to solve them. For example, if a student makes a calculation error while solving a math problem, the instruction department will immediately point out the error and show the correct calculation method. If a student is having trouble understanding, it will provide additional explanations and hints to support their understanding. The instruction department uses AI to analyze students' answers in real time and automatically generate appropriate feedback. Furthermore, the instruction department flexibly adjusts the content of instruction according to the student's learning progress to meet individual needs. For example, if a student is not making progress in a particular area, the instruction department will focus on that area to improve their understanding. The instruction department can also appropriately insert words of praise and encouragement to increase students' motivation to learn. In this way, the instruction department can support students in learning effectively at their own pace and maximize learning outcomes.

[0063] The analysis unit can collect data such as each student's learning history, test results, and assignment submission status, and have it analyzed by AI. For example, the analysis unit can collect each student's learning history and analyze learning time, learning content, and learning outcomes. The analysis unit can also collect test results and analyze test type, score, and correct answer rate. Furthermore, the analysis unit can collect assignment submission status and analyze submission deadlines, number of submissions, and submission content. This allows the analysis unit to accurately grasp each student's learning progress and level of understanding. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data such as each student's learning history, test results, and assignment submission status into a generating AI, and have the generating AI perform the analysis.

[0064] The proposal department can propose learning materials that allow students to learn progressively from basic to advanced levels in areas where they have a low level of understanding. For example, the proposal department can identify areas where students have a low level of understanding and propose learning materials that allow them to learn progressively from basic to advanced levels in those areas. The proposal department can also propose learning materials that start with basic materials and gradually increase in difficulty in areas where students have a low level of understanding. Furthermore, the proposal department can improve students' understanding by proposing learning materials that allow them to learn progressively in areas where they have a low level of understanding. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input data to identify areas where students have a low level of understanding into a generating AI, and have the generating AI propose the most suitable learning materials.

[0065] The suggestion unit can suggest learning materials containing more advanced content to students who are progressing quickly. For example, the suggestion unit can identify students who are progressing quickly and suggest learning materials containing more advanced content to those students. The suggestion unit can also suggest advanced-level or specialized learning materials to students who are progressing quickly. Furthermore, by suggesting learning materials containing more advanced content to students who are progressing quickly, the suggestion unit can increase students' motivation to learn. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data to identify students who are progressing quickly into a generating AI, and have the generating AI suggest the most suitable learning materials.

[0066] The instruction department can point out mistakes made by students while they are solving problems and teach them the correct way to solve them. For example, the instruction department can identify and point out mistakes made by students while they are solving problems. The instruction department can also teach the correct way to solve the problem after pointing out the mistakes. Furthermore, the instruction department can improve students' understanding by pointing out mistakes made by students while they are solving problems and teaching them the correct way to solve them. Some or all of the above processes in the instruction department may be performed using AI, for example, or not using AI. For example, the instruction department can input data on problems solved by students into a generating AI, which can then identify mistakes and teach the correct way to solve them.

[0067] The instructional department can provide additional explanations or hints when students are having difficulty understanding. For example, the instructional department can identify areas where students are having difficulty understanding and provide additional explanations for those areas. The instructional department can also provide hints that can help students solve problems when they are having difficulty understanding. Furthermore, the instructional department can improve students' understanding by providing additional explanations or hints when students are having difficulty understanding. Some or all of the above processes in the instructional department may be performed using AI, for example, or not using AI. For example, the instructional department can input data on areas where students are having difficulty understanding into a generating AI, and have the generating AI perform the task of providing additional explanations or hints.

[0068] The analysis unit can estimate a student's emotions and adjust the analysis method for learning progress and comprehension based on the estimated emotions. For example, if a student is stressed, the analysis unit can simplify the analysis method to reduce the burden. The analysis unit can also perform a detailed analysis and evaluate the level of deep comprehension if the student is relaxed. Furthermore, if the student is focused, the analysis unit can provide detailed feedback in real time. This allows the analysis unit to perform more appropriate analysis by adjusting the analysis method based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input student emotion data into a generative AI, which can then perform emotion estimation and adjustment of the analysis method.

[0069] The analysis unit can perform analysis considering the student's learning environment, in addition to learning history, test results, and assignment submission status. For example, if a student studies at night, the analysis unit will consider their nighttime learning performance. The analysis unit can also consider the home learning environment if a student studies at home. Furthermore, if a student studies in the library, the analysis unit can consider their level of concentration in the library. This allows the analysis unit to perform more accurate analysis by considering the learning environment. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning environment data into a generating AI, which can then perform analysis that takes the learning environment into account.

[0070] The analysis unit can evaluate a student's learning progress and understanding by comparing it with past learning data. For example, the analysis unit can evaluate progress by comparing a student's past test results with their current test results. The analysis unit can also evaluate progress by comparing a student's past assignment submission status with their current assignment submission status. Furthermore, the analysis unit can evaluate progress by comparing a student's past learning speed with their current learning speed. In this way, the analysis unit can accurately grasp the student's learning progress by evaluating it by comparing it with past learning data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a student's past learning data into a generating AI and have the generating AI perform the progress evaluation.

[0071] The analysis unit can estimate a student's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if a student is stressed, the analysis unit provides a simple display. The analysis unit can also display detailed analysis results if the student is relaxed. Furthermore, if the student is focused, the analysis unit can display detailed feedback in real time. This allows the analysis unit to provide more appropriate feedback by adjusting how the analysis results are displayed based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input student emotion data into a generative AI, which can then perform emotion estimation and adjust how the analysis results are displayed.

[0072] The analysis unit can analyze students' social media activity in addition to their learning history, test results, and assignment submission status to assess their learning motivation. For example, the analysis unit can analyze the content of students' social media posts to assess their learning motivation. The analysis unit can also analyze the frequency of students' social media activity to assess their learning motivation. Furthermore, the analysis unit can analyze students' social media friendships to assess their learning motivation. In this way, the analysis unit can accurately assess students' learning motivation by analyzing their social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input students' social media activity data into a generating AI and have the generating AI perform the evaluation of learning motivation.

[0073] The analysis unit can perform analyses of learning progress and comprehension while taking into account the student's geographical location. For example, if a student lives in an urban area, the analysis unit will take into account the urban learning environment. The analysis unit can also take into account the rural learning environment if the student lives in a rural area. Furthermore, if a student lives overseas, the analysis unit can take into account the overseas learning environment. This allows the analysis unit to perform more accurate analyses by taking geographical location information into account. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the student's geographical location data into a generating AI, and have the generating AI perform an analysis that takes geographical location information into account.

[0074] The suggestion unit can estimate a student's emotions and adjust the educational plan and material suggestion method based on the estimated emotions. For example, if a student is feeling stressed, the suggestion unit can suggest relaxing materials. If a student is relaxed, the suggestion unit can also suggest challenging materials. Furthermore, if a student is focused, the suggestion unit can suggest materials that help maintain their concentration. In this way, the suggestion unit can provide more appropriate educational plans and materials by adjusting the suggestion method based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the suggestion method.

[0075] The suggestion function can propose learning materials that allow for step-by-step learning from basic to advanced levels, as well as related supplementary materials, for areas where understanding is low. For example, the suggestion function can propose learning materials that allow for step-by-step learning from basic to advanced levels for areas where understanding is low. The suggestion function can also propose related video tutorials for areas where understanding is low. Furthermore, the suggestion function can propose interactive learning tools for areas where understanding is low. In this way, the suggestion function can support learning in areas where understanding is low by proposing related supplementary materials. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input data to identify areas where understanding is low into a generating AI, and have the generating AI propose the most suitable supplementary materials.

[0076] The suggestion unit can suggest challenging problems and project-based learning to students who are progressing quickly, in addition to teaching materials containing more advanced content. For example, the suggestion unit can suggest teaching materials containing more advanced content to students who are progressing quickly. The suggestion unit can also suggest challenging problems to students who are progressing quickly. Furthermore, the suggestion unit can also suggest project-based learning to students who are progressing quickly. In this way, the suggestion unit can increase the motivation of students who are progressing quickly by suggesting challenging problems and project-based learning. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data to identify students who are progressing quickly into a generating AI, and have the generating AI suggest the most suitable challenging problems and project-based learning.

[0077] The suggestion unit can estimate a student's emotions and adjust the difficulty level of the suggested learning materials based on those emotions. For example, if a student is stressed, the suggestion unit can suggest materials with a lower difficulty level. If a student is relaxed, the suggestion unit can also suggest materials with a higher difficulty level. Furthermore, if a student is focused, the suggestion unit can suggest materials of an appropriate difficulty level. In this way, the suggestion unit can provide more appropriate learning materials by adjusting the difficulty level of the materials based on the student's emotions. 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 suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input student emotion data into a generative AI, which can then perform emotion estimation and adjust the difficulty level of the learning materials.

[0078] The proposal function can propose interactive learning tools in addition to learning materials that allow for step-by-step learning from basic to advanced levels for areas where understanding is low. For example, the proposal function can propose interactive quizzes for areas where understanding is low. The proposal function can also propose simulation tools for areas where understanding is low. Furthermore, the proposal function can propose game-based learning tools for areas where understanding is low. In this way, the proposal function can support learning in areas where understanding is low by proposing interactive learning tools. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function can input data to identify areas where understanding is low into a generating AI, and have the generating AI propose the most suitable interactive learning tools.

[0079] The suggestion unit can suggest collaborative learning with other students, in addition to more advanced learning materials, to students who are progressing quickly. For example, the suggestion unit can suggest collaborative projects to students who are progressing quickly. The suggestion unit can also suggest discussion groups to students who are progressing quickly. Furthermore, the suggestion unit can suggest pair work to students who are progressing quickly. In this way, the suggestion unit can increase the motivation of students who are progressing quickly by suggesting collaborative learning with other students. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data to identify students who are progressing quickly into a generating AI, and have the generating AI make optimal collaborative learning suggestions.

[0080] The instruction department can estimate a student's emotions and adjust its individualized instruction and feedback methods based on those emotions. For example, if a student is stressed, the instruction department can provide feedback in a gentle tone. If a student is relaxed, the instruction department can also provide detailed feedback. Furthermore, if a student is focused, the instruction department can provide real-time feedback. This allows the instruction department to provide more appropriate instruction by adjusting its individualized instruction and feedback methods based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 instruction department may be performed using AI, or not using AI. For example, the instruction department can input student emotion data into a generative AI, which can then perform emotion estimation and adjustment of instruction methods.

[0081] The instructional department can not only point out where students made mistakes while they are solving problems, but also explain the reasons for those mistakes and allow them to try again. For example, the instructional department can point out where the student made a mistake and explain the reason for it. The instructional department can also point out where the student made a mistake and allow them to try again. Furthermore, the instructional department can point out where the student made a mistake and teach them the correct way to solve the problem. In this way, the instructional department can improve the student's understanding by explaining the reasons for mistakes and allowing them to try again. Some or all of the above processes in the instructional department may be performed using AI, for example, or not using AI. For example, the instructional department can input data on problems solved by students into a generating AI, which can then perform explanations of the reasons for mistakes and provide guidance for retrying.

[0082] The instructional staff can provide relevant video tutorials, in addition to additional explanations and hints, when students are having difficulty understanding. For example, the instructional staff can provide additional explanations when students are having difficulty understanding. The instructional staff can also provide hints when students are having difficulty understanding. Furthermore, the instructional staff can also provide relevant video tutorials when students are having difficulty understanding. This allows the instructional staff to improve students' understanding by providing relevant video tutorials. Some or all of the above processes in the instructional staff may be performed using AI, for example, or not using AI. For example, the instructional staff can input data on the parts where students are having difficulty understanding into a generating AI, which can then perform the task of providing additional explanations, hints, and video tutorials.

[0083] The instruction department can estimate a student's emotions and adjust the timing of feedback based on the estimated emotions. For example, if a student is stressed, the instruction department may delay the timing of feedback. The instruction department can also provide feedback in real time if the student is relaxed. Furthermore, the instruction department can provide feedback at an appropriate time if the student is focused. This allows the instruction department to provide feedback at a more appropriate time by adjusting the timing of feedback based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction department may be performed using AI or not using AI. For example, the instruction department can input student emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of feedback timing.

[0084] The instructional department can not only point out mistakes made by students while they are solving problems, but also deepen their understanding by presenting similar problems. For example, the instructional department can point out where a student made a mistake and present similar problems. The instructional department can also point out where a student made a mistake and have them try again. Furthermore, the instructional department can point out where a student made a mistake and teach them the correct way to solve the problem. In this way, the instructional department can improve students' understanding by presenting similar problems. Some or all of the above processes in the instructional department may be performed using AI, for example, or not using AI. For example, the instructional department can input data on problems solved by students into a generating AI, which can then generate similar problems and provide instruction.

[0085] The instructional staff can provide additional explanations and hints, as well as encourage discussion with other students, when students are having difficulty understanding. For example, the instructional staff can provide additional explanations when students are having difficulty understanding. The instructional staff can also provide hints when students are having difficulty understanding. Furthermore, the instructional staff can encourage discussion with other students when students are having difficulty understanding. This allows the instructional staff to improve students' understanding by encouraging discussion with other students. Some or all of the above processes in the instructional staff may be performed using AI, for example, or not using AI. For example, the instructional staff can input data on the parts where students are having difficulty understanding into a generating AI, which can then perform additional explanations, hints, and facilitate discussion.

[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 analytics unit not only analyzes students' learning progress and comprehension in real time, but also identifies learning patterns and can create long-term learning plans. For example, if a student tends to concentrate more at certain times of the day, the analytics unit can suggest a learning plan tailored to those times. Furthermore, if a student has difficulty with a particular subject, the analytics unit can gradually adjust the learning plan for that subject. In addition, based on a student's learning history, the analytics unit can predict future learning progress and update learning content at appropriate times. This allows the analytics unit to provide more effective learning support by offering long-term learning plans that take students' learning patterns into account.

[0088] The suggestion function not only proposes learning materials that allow students to progress from basic to advanced levels in areas where they have a low level of understanding, but it can also dynamically adjust the difficulty level of the materials according to the student's learning progress. For example, if a student understands a particular unit, the suggestion function can propose additional application problems related to that unit. Furthermore, if a student repeatedly makes mistakes on a particular problem, the suggestion function can provide supplementary materials for that problem. In addition, the suggestion function can automatically select the next topic to be learned according to the student's learning progress, making the learning flow smoother. In this way, the suggestion function can improve students' understanding by providing dynamic learning material suggestions that are tailored to their learning progress.

[0089] The instruction department can not only point out where students made mistakes while solving problems and teach them the correct solutions, but also have them review the foundational knowledge related to those mistakes. For example, if a student makes a mistake on a particular problem, the instruction department can provide materials to review the foundational knowledge related to that problem. The instruction department can also provide additional practice problems to help students understand where they made mistakes. Furthermore, the instruction department can provide step-by-step guides to help students overcome their mistakes. In this way, the instruction department can improve students' understanding through reviewing the foundational knowledge related to the areas where they made mistakes.

[0090] The analysis unit can perform analyses that take into account not only students' learning history, test results, and assignment submission status, but also their learning style. For example, if a student prefers visual learning, the analysis unit can suggest a learning plan that includes many visual materials. Similarly, if a student prefers auditory learning, it can suggest a learning plan that includes many audio materials. Furthermore, if a student prefers practical learning, the analysis unit can suggest a learning plan that includes many practical exercises. In this way, the analysis unit can perform analyses that take into account students' learning styles, enabling more effective learning support.

[0091] The proposal department can not only suggest more advanced learning materials to students who are progressing quickly, but also propose challenging projects tailored to their learning progress. For example, the proposal department can suggest projects focused on solving real-world problems to students who are progressing quickly. It can also suggest group projects that students can work on collaboratively with other students. Furthermore, the proposal department can suggest research projects to deepen specialized knowledge to students who are progressing quickly. In this way, the proposal department can enhance the learning motivation of students who are progressing quickly through challenging projects.

[0092] The analysis unit can estimate students' emotions and adjust the analysis method for learning progress and comprehension based on the estimated emotions. For example, if a student is stressed, the analysis unit can simplify the analysis method to reduce the burden. Conversely, if a student is relaxed, the analysis unit can perform a detailed analysis to assess their level of understanding. Furthermore, if a student is focused, the analysis unit can provide detailed feedback in real time. In this way, the analysis unit can perform more appropriate analysis by adjusting the analysis method based on students' emotions.

[0093] The suggestion department can estimate students' emotions and adjust the educational plan and material suggestions based on those estimates. For example, if a student is feeling stressed, the suggestion department can suggest relaxing materials. If a student is relaxed, the suggestion department can suggest challenging materials. Furthermore, if a student is focused, the suggestion department can suggest materials that help maintain their concentration. In this way, the suggestion department can provide more appropriate educational plans and materials by adjusting its suggestions based on students' emotions.

[0094] The instruction department can estimate students' emotions and adjust individualized instruction and feedback methods based on those estimates. For example, if a student is stressed, the department can provide feedback in a gentle tone. Conversely, if a student is relaxed, the department can provide detailed feedback. Furthermore, if a student is focused, the department can provide real-time feedback. This allows the instruction department to provide more appropriate guidance by adjusting individualized instruction and feedback methods based on students' emotions.

[0095] The analysis unit can estimate the student's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the student is stressed, the analysis unit can provide a simple display. If the student is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the student is focused, the analysis unit can display detailed feedback in real time. This allows the analysis unit to provide more appropriate feedback by adjusting how the analysis results are displayed based on the student's emotions.

[0096] The teaching staff can estimate students' emotions and adjust the timing of feedback based on those estimates. For example, if a student is stressed, the staff can delay the timing of feedback. Conversely, if a student is relaxed, the staff can provide feedback in real time. Furthermore, if a student is focused, the staff can provide feedback at an appropriate time. In this way, the teaching staff can provide feedback at a more appropriate time by adjusting the timing of feedback based on students' emotions.

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

[0098] Step 1: The analysis unit analyzes each student's learning progress and understanding in real time. The analysis unit collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes it. For example, it analyzes the speed at which students solve math problems, their accuracy rate, and the results of quizzes to evaluate their understanding. This allows the system to understand each student's learning progress and understanding. Step 2: The proposal department proposes the optimal educational plan and teaching materials based on the analysis results obtained by the analysis department. The proposal department proposes teaching materials that allow students to learn step-by-step from basic to advanced levels in areas where they have a low level of understanding. They can also propose teaching materials containing more advanced content for students who are learning at a faster pace. This allows for the provision of an optimal learning plan for each student. Step 3: The instruction team provides real-time individualized instruction and feedback based on the educational plan and materials proposed by the proposal team. The instruction team points out where students made mistakes while solving problems and teaches them the correct way to solve them. They can also provide additional explanations and hints if students are having difficulty understanding. This allows students to learn effectively at their own pace.

[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 described above, including the analysis unit, proposal unit, and instruction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14, which collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes it. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal educational plan and teaching materials based on the analysis results. The instruction unit is implemented by the control unit 46A of the smart device 14, which provides real-time individual instruction and feedback while students are solving problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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 described above, including the analysis unit, proposal unit, and instruction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214, which collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes it. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal educational plan and teaching materials based on the analysis results. The instruction unit is implemented by the control unit 46A of the smart glasses 214, which provides real-time individual instruction and feedback while the student is solving problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes 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 described above, including the analysis unit, proposal unit, and instruction unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314, which collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes it. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal educational plan and teaching materials based on the analysis results. The instruction unit is implemented by the control unit 46A of the headset terminal 314, which provides real-time individual instruction and feedback while the student is solving problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes 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 described above, including the analysis unit, proposal unit, and instruction unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414, which collects data such as each student's learning history, test results, and assignment submission status, and the AI ​​analyzes it. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal educational plan and teaching materials based on the analysis results. The instruction unit is implemented by the control unit 46A of the robot 414, which provides real-time individual instruction and feedback while students are solving problems. 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) An analysis unit that analyzes each student's learning progress and level of understanding in real time, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal educational plan and teaching materials. The instruction department provides real-time individualized instruction and feedback based on the educational plan and teaching materials proposed by the aforementioned proposal department. Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system collects data such as each student's learning history, test results, and assignment submission status, and then uses AI to analyze it. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose teaching materials that allow learners to progress step-by-step from basic to advanced levels in areas where understanding is low. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, For students who are learning at a fast pace, we propose teaching materials that include more advanced content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned leadership, The teacher points out the mistakes made by the student while they are solving the problem and teaches them the correct way to solve it. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned leadership, Provide additional explanations or hints if students are having difficulty understanding. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate students' emotions and adjust the analysis method for learning progress and comprehension based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, In addition to learning history, test results, and assignment submission status, the analysis takes into account the student's learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing students' learning progress and understanding, we evaluate their progress by comparing it with past learning data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates students' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, In addition to learning history, test results, and assignment submission status, we analyze students' social media activity to assess their motivation to learn. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing learning progress and comprehension, the analysis takes into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, We estimate students' emotions and adjust the teaching plan and material suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, For areas where understanding is low, we propose teaching materials that allow for gradual learning from basic to advanced levels, as well as related supplementary materials. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, For students who are progressing quickly, we propose challenging problems and project-based learning in addition to materials containing more advanced content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system estimates students' emotions and adjusts the difficulty level of suggested learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, For areas where understanding is low, we propose interactive learning tools in addition to teaching materials that allow for step-by-step learning from basic to advanced levels. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, For students who are progressing quickly, we propose collaborative learning with other students, in addition to providing materials that include more advanced content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned leadership, We estimate students' emotions and adjust individualized instruction and feedback methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned leadership, While students are working on a problem, the teacher should not only point out the mistakes but also explain the reasons for those mistakes and encourage them to try again. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned leadership, If students are having difficulty understanding, provide additional explanations and hints, as well as relevant video tutorials. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned leadership, The system estimates the student'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 leadership, While students are solving problems, the teacher not only points out their mistakes but also presents similar problems to deepen their understanding. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned leadership, If a student is having difficulty understanding, encourage discussion with other students in addition to providing additional explanations and hints. 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. An analysis unit that analyzes each student's learning progress and level of understanding in real time, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal educational plan and teaching materials. The system comprises: an instruction unit that provides real-time individualized instruction and feedback based on the educational plan and teaching materials proposed by the aforementioned proposal unit; A system characterized by the following features.

2. The aforementioned analysis unit, The AI ​​collects data such as each student's learning history, test results, and assignment submission status, and then analyzes it. The system according to feature 1.

3. The aforementioned proposal section is, We propose teaching materials that allow learners to progress step-by-step from basic to advanced levels in areas where understanding is low. The system according to feature 1.

4. The aforementioned proposal section is, For students who are learning at a fast pace, we propose teaching materials that include more advanced content. The system according to feature 1.

5. The aforementioned leadership, The teacher points out the mistakes made by the student while they are solving the problem and teaches them the correct way to solve it. The system according to feature 1.

6. The aforementioned leadership, Provide additional explanations or hints if students are having difficulty understanding. The system according to feature 1.

7. The aforementioned analysis unit, We estimate students' emotions and adjust the analysis method for learning progress and comprehension based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, In addition to learning history, test results, and assignment submission status, the analysis takes into account the student's learning environment. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing students' learning progress and understanding, we evaluate their progress by comparing it with past learning data. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates students' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

Images