system

Abstract

The system according to the embodiment aims to optimize the educational process and provide efficient learning support. [Solution] A system according to an embodiment includes a generation unit, a planning unit, an instruction unit, and a grading unit. The generation unit generates teaching materials. The planning unit creates a learning plan based on the teaching materials generated by the generation unit. The instruction unit provides individualized instruction and learning support based on the learning plan created by the planning unit. The grading unit automatically grades the results of the instruction provided by the instruction unit and provides individual feedback.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately optimize or streamline the education process, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the educational process and provide efficient learning support. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a planning unit, a teaching unit, and a grading unit. The generation unit generates teaching materials. The planning unit creates a learning plan based on the teaching materials generated by the generation unit. The teaching unit provides individualized instruction and learning support based on the learning plan created by the planning unit. The grading unit automatically grades the results of the instruction provided by the teaching unit and provides individual feedback. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the educational process and provide efficient learning support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational support system according to an embodiment of the present invention uses generative AI to fundamentally update educational programs, educational processes, and educational settings. This educational support system uses generative AI to develop teaching materials, create learning plans, provide individualized instruction and learning support, and provide automatic grading and individualized feedback. For example, the educational support system uses generative AI to learn from existing teaching material data and generate optimal teaching materials. Next, the educational support system uses generative AI to analyze each student's learning history and level of understanding and propose an optimal learning plan for each student. Furthermore, the educational support system uses generative AI to monitor students' learning progress in real time and provide appropriate guidance as needed. The educational support system also uses generative AI to automatically grade students' answer sheets and provide individualized feedback. This optimizes and streamlines educational programs and reduces the burden on educational settings. For example, by automatically generating teaching materials using generative AI, teachers can reduce the time it takes to create teaching materials. Furthermore, by individually proposing optimal learning plans, the generative AI enables education tailored to each student. Furthermore, by monitoring students' learning progress in real time and providing appropriate guidance, the educational support system can improve their understanding. Automatic grading and individual feedback reduce teachers' grading workload and allow students to receive feedback quickly. In this way, updating educational programs using generative AI improves the quality of education and reduces the burden on classrooms. This is expected to maximize the learning effectiveness of students who will lead the future and improve the education sector, which is a social issue in Japan. In this way, the educational support system can improve the quality of education and reduce the burden on classrooms.

[0029] The education support system according to the embodiment includes a generation unit, a planning unit, an instruction unit, and a grading unit. The generation unit generates teaching materials. For example, the generation unit learns existing teaching material data and generates optimal teaching materials. The generation unit can learn existing teaching material data, such as text data and multimedia data, and generate new teaching materials based on the data. The generation unit can automatically generate the content of the teaching materials using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. The planning unit creates a learning plan based on the teaching materials generated by the generation unit. For example, the planning unit analyzes each student's learning history and level of understanding and proposes an optimal learning plan for each student. The planning unit can analyze each student's learning history and level of understanding using the generation AI and create a learning plan based on the analysis. The instruction unit provides individualized instruction and learning support based on the learning plan created by the planning unit. For example, the instruction unit monitors each student's learning status in real time and provides appropriate instruction as needed. The teaching unit can use, for example, a generating AI to monitor the learning status of students in real time and provide instruction based on that. The scoring unit automatically scores the results of the teaching provided by the teaching unit and provides individual feedback. The scoring unit can, for example, automatically score students' answer sheets and provide individual feedback. The scoring unit can, for example, use a generating AI to automatically score students' answer sheets and provide feedback based on the score. As a result, the education support system according to the embodiment can improve the quality of education and reduce the burden on educational institutions.

[0030] The generation unit can learn existing teaching material data and generate teaching materials. The generation unit, for example, learns existing teaching material data and generates optimal teaching materials. The generation unit can learn existing teaching material data, such as text data or multimedia data, and generate new teaching materials based on the data. The generation unit can automatically generate the content of the teaching materials, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. This makes it possible to generate optimal teaching materials by learning existing teaching material data. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input existing teaching material data into the generation AI and cause the generation AI to generate teaching materials.

[0031] The planning unit can analyze each student's learning history and level of understanding and propose an individual learning plan. The planning unit can, for example, analyze each student's learning history and level of understanding and propose an optimal learning plan for each student. The planning unit can, for example, use a generating AI to analyze each student's learning history and level of understanding and create a learning plan based on the results. The planning unit can, for example, collect learning history such as past test results and study time, and create a learning plan based on the collected data. The planning unit can, for example, evaluate understanding such as test scores and quiz correct answer rates, and create a learning plan based on the collected data. This enables individualized instruction by proposing an optimal learning plan for each student. Some or all of the above-described processing in the planning unit can be performed using, for example, a generating AI, or can be performed without using a generating AI. For example, the planning unit can input each student's learning history and level of understanding into the generating AI and have the generating AI create a learning plan.

[0032] The instruction unit can monitor the learning status of students in real time and provide instruction as necessary. The instruction unit can, for example, monitor the learning status of students in real time and provide appropriate instruction as necessary. The instruction unit can, for example, use a generative AI to monitor the learning status of students in real time and provide instruction based on the monitoring. The instruction unit can, for example, use a sensor or a data collection device to monitor the learning status of students in real time. The instruction unit can, for example, monitor the learning progress and level of understanding of students in real time and provide instruction based on the monitoring. This enables appropriate instruction by monitoring the learning status in real time. Some or all of the above-mentioned processing in the instruction unit can be performed, for example, using a generative AI, or can be performed without using a generative AI. For example, the instruction unit can input the learning status of students into a generative AI and leave the execution of instruction to the generative AI.

[0033] The grading unit can automatically grade students' answer sheets and provide individual feedback. The grading unit can, for example, automatically grade students' answer sheets and provide individual feedback. The grading unit can, for example, use a generation AI to automatically grade students' answer sheets and provide feedback based on the grade. The grading unit can, for example, use a scoring algorithm to automatically grade students' answer sheets. The grading unit can, for example, evaluate students' answer sheets based on scoring criteria and provide feedback based on the evaluation. This reduces the burden on teachers through automatic grading and individual feedback. Some or all of the above-mentioned processing in the grading unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the grading unit can input students' answer sheets into the generation AI and have the generation AI grade them and provide feedback.

[0034] The generation unit can adjust the content of the teaching materials by referring to past learning data when generating the teaching materials. For example, the generation unit can optimize the content of the teaching materials by referring to past learning data when generating the teaching materials. For example, the generation unit can refer to past learning data using a generation AI and adjust the content of the teaching materials based on the data. For example, the generation unit can refer to a student's past test results and generate teaching materials that focus on areas of weakness. For example, the generation unit can analyze a student's past learning history and generate teaching materials that reinforce areas of low understanding. For example, the generation unit generates teaching materials according to the student's progress based on the student's past learning data. In this way, the content of the teaching materials can be optimized by referring to the past learning data. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input past learning data into the generation AI and cause the generation AI to adjust the content of the teaching materials.

[0035] The generation unit can apply different teaching material generation algorithms according to specific learning goals when generating teaching materials. For example, the generation unit can apply different teaching material generation algorithms according to specific learning goals when generating teaching materials. For example, the generation unit can apply different teaching material generation algorithms according to specific learning goals using a generation AI. For example, the generation unit generates teaching materials that include many experiment videos according to a science learning goal. For example, the generation unit generates teaching materials that include many practice problems according to a mathematics learning goal. For example, the generation unit generates teaching materials that include many listening materials according to an English learning goal. This makes it possible to generate teaching materials according to specific learning goals. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a specific learning goal into the generation AI and cause the generation AI to apply a teaching material generation algorithm.

[0036] The generation unit can customize the theme of the teaching material based on the student's interests and concerns when generating the teaching material. For example, the generation unit customizes the theme of the teaching material based on the student's interests and concerns when generating the teaching material. For example, the generation unit can customize the theme of the teaching material based on the student's interests and concerns using a generation AI. For example, if the student is interested in science, the generation AI generates teaching material on a science-related theme. For example, if the student is interested in history, the generation AI generates teaching material on a history-related theme. For example, if the student is interested in sports, the generation AI generates teaching material on a sports-related theme. This makes it possible to generate teaching material based on the student's interests and concerns. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input student interest and concern data into the generation AI and cause the generation AI to customize the theme of the teaching material.

[0037] The generation unit can generate more familiar teaching materials by including content appropriate for the region and culture when generating the teaching materials. The generation unit can generate more familiar teaching materials by including content appropriate for the region and culture when generating the teaching materials. The generation unit can generate more familiar teaching materials by including content appropriate for the region and culture, for example, using a generation AI. The generation unit can generate more familiar teaching materials by including content appropriate for the region and culture, for example. The generation unit generates teaching materials related to the history and culture of the region where the student lives. The generation unit generates teaching materials related to the culture of the language the student is learning. The generation unit generates teaching materials related to a country or region in which the student is interested. This makes it possible to generate teaching materials appropriate for the region and culture. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data related to the region and culture into the generation AI and cause the generation AI to customize the content of the teaching materials.

[0038] The planning unit can improve the accuracy of the study plan by referring to the student's past learning history when formulating the study plan. For example, the planning unit can improve the accuracy of the plan by referring to the student's past learning history when formulating the study plan. For example, the planning unit can refer to the student's past learning history using a generation AI and formulate the study plan based on the past learning history. For example, the planning unit can refer to the student's past test results and formulate a study plan that focuses on areas of weakness. For example, the planning unit can analyze the student's past learning history and formulate a study plan that reinforces areas of low understanding. For example, the planning unit formulates a study plan according to the student's progress based on the student's past learning data. In this way, the accuracy of the study plan is improved by referring to the past learning history. Some or all of the above-described processing in the planning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the planning unit can input the student's past learning history into the generation AI and have the generation AI formulate the study plan.

[0039] The planning unit can apply different planning algorithms depending on specific learning goals when creating a lesson plan. For example, the planning unit can apply different planning algorithms depending on specific learning goals when creating a lesson plan. The planning unit can apply different planning algorithms depending on specific learning goals, for example, using a generation AI. For example, the planning unit creates a lesson plan that includes many experiments depending on a science learning goal. For example, the planning unit creates a lesson plan that includes many practice problems depending on a mathematics learning goal. For example, the planning unit creates a lesson plan that includes many listening comprehension depending on an English learning goal. This makes it possible to create a lesson plan depending on a specific learning goal. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the planning unit can input a specific learning goal into the generation AI and cause the generation AI to apply a planning algorithm.

[0040] The planning unit can customize the study plan based on the student's daily rhythm and schedule when creating the study plan. For example, the planning unit customizes the study plan based on the student's daily rhythm and schedule when creating the study plan. The planning unit can customize the plan based on the student's daily rhythm and schedule using, for example, a generation AI. For example, the planning unit creates the study plan based on the student's school timetable. For example, the planning unit creates the study plan based on the student's home schedule. For example, the planning unit creates the study plan based on the student's sleep rhythm. This makes it possible to create a study plan based on the student's daily rhythm and schedule. Some or all of the above-mentioned processing in the planning unit may be performed using, or without, the generation AI. For example, the planning unit can input data on the student's daily rhythm and schedule into the generation AI and cause the generation AI to customize the study plan.

[0041] The planning unit can provide a collaborative learning environment by including group learning and collaborative projects when formulating a learning plan. The planning unit can provide a collaborative learning environment by, for example, including group learning and collaborative projects when formulating a learning plan. The planning unit can provide a collaborative learning environment by, for example, using a generative AI. For example, the planning unit includes in the learning plan a project that students will work on in a group. For example, the planning unit includes in the learning plan a problem that students will solve together. For example, the planning unit includes in the learning plan an opportunity for students to learn collaboratively. This provides a collaborative learning environment, thereby improving the learning effectiveness of students. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the planning unit can input data on group learning and collaborative projects into the generative AI and cause the generative AI to create a learning plan.

[0042] The teaching unit can improve the accuracy of teaching by referring to past teaching data when providing instruction. For example, the teaching unit can improve the accuracy of teaching by referring to past teaching data when providing instruction. The teaching unit can, for example, use a generation AI to refer to past teaching data and adjust the teaching method based on that data. For example, the teaching unit refers to a student's past teaching history and reapplies effective teaching methods. For example, the teaching unit analyzes a student's past teaching data, finds areas for improvement, and adjusts the teaching method. For example, the teaching unit provides instruction according to the student's progress based on the student's past teaching data. In this way, the accuracy of teaching is improved by referring to the past teaching data. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the teaching unit can input past teaching data into the generation AI and have the generation AI adjust the teaching method.

[0043] The teaching unit can apply different teaching algorithms depending on specific learning goals during instruction. For example, the teaching unit can apply different teaching algorithms depending on specific learning goals during instruction. The teaching unit can apply different teaching algorithms depending on specific learning goals, for example, using a generative AI. For example, the teaching unit provides instruction that includes many experiments depending on science learning goals. For example, the teaching unit provides instruction that includes many practice problems depending on mathematics learning goals. For example, the teaching unit provides instruction that includes many listening comprehension depending on English learning goals. This makes it possible to provide instruction that is tailored to specific learning goals. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the teaching unit can input a specific learning goal into the generative AI and have the generative AI apply the teaching algorithm.

[0044] The teaching unit can customize teaching content based on the student's interests and concerns during teaching. For example, the teaching unit customizes teaching content based on the student's interests and concerns during teaching. The teaching unit can customize teaching content based on the student's interests and concerns using, for example, a generation AI. For example, if a student is interested in science, the generation AI provides science-related teaching content. For example, if a student is interested in history, the generation AI provides history-related teaching content. For example, if a student is interested in sports, the generation AI provides sports-related teaching content. This enables teaching based on the student's interests and concerns. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the teaching unit can input student interest data into the generation AI and have the generation AI customize the teaching content.

[0045] The teaching unit can realize a flexible learning environment by providing hybrid online and offline instruction during instruction. The teaching unit can realize a flexible learning environment by, for example, providing hybrid online and offline instruction during instruction. The teaching unit can realize a flexible learning environment by, for example, using a generation AI to provide hybrid online and offline instruction. For example, when a student studies at home, the generation AI provides online instruction. For example, when a student studies at school, the generation AI provides offline instruction. For example, when a student studies while traveling, the generation AI provides combined online and offline instruction. This realizes a flexible learning environment through hybrid online and offline instruction. Some or all of the above-described processing in the teaching unit may be performed using, or without, the generation AI. For example, the teaching unit can input online and offline instruction data into the generation AI and cause the generation AI to provide hybrid instruction.

[0046] The scoring unit can improve the accuracy of scoring by referring to past scoring data when scoring. For example, the scoring unit can improve the accuracy of scoring by referring to past scoring data when scoring. For example, the scoring unit can refer to past scoring data using a generation AI and adjust the scoring method based on that data. For example, the scoring unit can refer to a student's past answer sheets and provide feedback to prevent the student from making the same mistake again. For example, the scoring unit can analyze a student's past scoring data and provide feedback to reinforce areas where the student's understanding is low. For example, the scoring unit can provide feedback according to the student's progress based on the student's past scoring data. In this way, the accuracy of scoring is improved by referring to the past scoring data. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scoring unit can input past scoring data into a generation AI and have the generation AI adjust the scoring method.

[0047] The scoring unit can apply different scoring algorithms depending on specific learning goals during scoring. For example, the scoring unit can apply different scoring algorithms depending on specific learning goals during scoring. For example, the scoring unit can apply different scoring algorithms depending on specific learning goals using a generation AI. For example, the scoring unit applies a lab report scoring algorithm depending on a science learning goal. For example, the scoring unit applies a practice problem scoring algorithm depending on a mathematics learning goal. For example, the scoring unit applies a listening test scoring algorithm depending on an English learning goal. This enables scoring according to specific learning goals. Some or all of the above-mentioned processing in the scoring unit may be performed using, or without, the generation AI. For example, the scoring unit can input a specific learning goal into the generation AI and have the generation AI apply the scoring algorithm.

[0048] The scoring unit can adjust the level of detail of the feedback based on the student's level of understanding during scoring. The scoring unit, for example, adjusts the level of detail of the feedback based on the student's level of understanding during scoring. The scoring unit can adjust the level of detail of the feedback based on the student's level of understanding, for example, using a generation AI. The scoring unit can evaluate the level of understanding, such as test scores or the percentage of correct answers on quizzes, and adjust the level of detail of the feedback based on the evaluation. For example, if the student's level of understanding is low, the generation AI provides detailed feedback. For example, if the student's level of understanding is medium, the generation AI provides normal feedback. For example, if the student's level of understanding is high, the generation AI provides concise feedback. This enables feedback according to the student's level of understanding. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the scoring unit can input the student's level of understanding data into the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0049] The scoring unit can provide a multifaceted evaluation by including group evaluation and peer review when scoring. The scoring unit can provide a multifaceted evaluation by including group evaluation and peer review when scoring. The scoring unit can provide a multifaceted evaluation by including group evaluation and peer review using, for example, a generation AI. For example, the scoring unit has the generation AI perform group evaluation of assignments worked on by students in groups. For example, the scoring unit has the generation AI reflect the results of students' peer reviews in its scoring. For example, the scoring unit has the generation AI perform multifaceted evaluation of assignments solved by students collaboratively. This improves students' learning effectiveness through multifaceted evaluation. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the scoring unit can input data from group evaluations and peer reviews into the generation AI and leave the execution of the multifaceted evaluation to the generation AI.

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

[0051] The education support system further includes a motivation unit. The motivation unit can provide a reward system to increase students' motivation to learn. For example, the motivation unit can award badges or points when students achieve specific learning goals. The motivation unit can also provide rewards when students achieve a certain amount of study time. Furthermore, the motivation unit can provide a ranking system to increase students' motivation to learn by having them compete with other students. This is expected to increase students' motivation to learn and improve their learning effectiveness.

[0052] The generator can customize the learning materials according to the learning style when generating them. For example, for visual learners, it can generate learning materials that include many diagrams and graphs. For auditory learners, it can generate learning materials that include many audio and video elements. Furthermore, for tactile learners, it can generate learning materials that include many interactive elements. This allows the provision of learning materials that match the students' learning styles, which is expected to improve learning effectiveness.

[0053] The planning section can support students in setting goals when formulating a learning plan. For example, the planning section can help students set short-term learning goals. The planning section can also help students set medium-term learning goals. Furthermore, the planning section can help students set long-term learning goals. This allows students to study with clear goals in mind, which is expected to improve learning effectiveness.

[0054] The teaching staff can adjust the learning pace according to the student's learning situation. For example, if the student's level of understanding is high, the teaching staff can speed up the learning pace. On the other hand, if the student's level of understanding is low, the teaching staff can slow down the learning pace. Furthermore, if a student is struggling in a particular area, the teaching staff can provide instruction that focuses on that area. This allows the teaching to be provided according to the student's learning pace, and is expected to improve learning effectiveness.

[0055] The grading department can visualize students' learning progress based on the grading results. For example, the grading department can display students' test results in graphs or charts. The grading department can also display students' learning progress on a timeline. Furthermore, the grading department can display students' understanding levels in different colors. This allows students to visually grasp their learning progress, which is expected to increase their motivation to learn.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The generation unit generates teaching materials. The generation unit, for example, learns existing teaching material data and generates optimal teaching materials. The generation unit can learn existing teaching material data, such as text data and multimedia data, and generate new teaching materials based on the data. The generation unit can automatically generate the content of the teaching materials, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 2: The planning unit creates a learning plan based on the learning materials generated by the generation unit. The planning unit, for example, analyzes each student's learning history and level of understanding and proposes an optimal learning plan for each student. For example, the planning unit can use a generation AI to analyze each student's learning history and level of understanding and create a learning plan based on that. Step 3: The teaching department provides individual instruction and learning support based on the learning plan drawn up by the planning department. The teaching department, for example, monitors the student's learning situation in real time and provides appropriate instruction as needed. The teaching department can, for example, use generative AI to monitor the student's learning situation in real time and provide instruction based on that. Step 4: The grading unit automatically grades the results of the instruction provided by the teaching unit and provides individual feedback. The grading unit, for example, automatically grades students' answer sheets and provides individual feedback. The grading unit can, for example, use generative AI to automatically grade students' answer sheets and provide feedback based on the grades.

[0058] (Example 2) An educational support system according to an embodiment of the present invention uses generative AI to fundamentally update educational programs, educational processes, and educational settings. This educational support system uses generative AI to develop teaching materials, create learning plans, provide individualized instruction and learning support, and provide automatic grading and individualized feedback. For example, the educational support system uses generative AI to learn from existing teaching material data and generate optimal teaching materials. Next, the educational support system uses generative AI to analyze each student's learning history and level of understanding and propose an optimal learning plan for each student. Furthermore, the educational support system uses generative AI to monitor students' learning progress in real time and provide appropriate guidance as needed. The educational support system also uses generative AI to automatically grade students' answer sheets and provide individualized feedback. This optimizes and streamlines educational programs and reduces the burden on educational settings. For example, by automatically generating teaching materials using generative AI, teachers can reduce the time it takes to create teaching materials. Furthermore, by individually proposing optimal learning plans, the generative AI enables education tailored to each student. Furthermore, by monitoring students' learning progress in real time and providing appropriate guidance, the educational support system can improve their understanding. Automatic grading and individual feedback reduce teachers' grading workload and allow students to receive feedback quickly. In this way, updating educational programs using generative AI improves the quality of education and reduces the burden on classrooms. This is expected to maximize the learning effectiveness of students who will lead the future and improve the education sector, which is a social issue in Japan. In this way, the educational support system can improve the quality of education and reduce the burden on classrooms.

[0059] The education support system according to the embodiment includes a generation unit, a planning unit, an instruction unit, and a grading unit. The generation unit generates teaching materials. For example, the generation unit learns existing teaching material data and generates optimal teaching materials. The generation unit can learn existing teaching material data, such as text data and multimedia data, and generate new teaching materials based on the data. The generation unit can automatically generate the content of the teaching materials using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. The planning unit creates a learning plan based on the teaching materials generated by the generation unit. For example, the planning unit analyzes each student's learning history and level of understanding and proposes an optimal learning plan for each student. The planning unit can analyze each student's learning history and level of understanding using the generation AI and create a learning plan based on the analysis. The instruction unit provides individualized instruction and learning support based on the learning plan created by the planning unit. For example, the instruction unit monitors each student's learning status in real time and provides appropriate instruction as needed. The teaching unit can use, for example, a generating AI to monitor the learning status of students in real time and provide instruction based on that. The scoring unit automatically scores the results of the teaching provided by the teaching unit and provides individual feedback. The scoring unit can, for example, automatically score students' answer sheets and provide individual feedback. The scoring unit can, for example, use a generating AI to automatically score students' answer sheets and provide feedback based on the score. As a result, the education support system according to the embodiment can improve the quality of education and reduce the burden on educational institutions.

[0060] The generation unit can learn existing teaching material data and generate teaching materials. The generation unit, for example, learns existing teaching material data and generates optimal teaching materials. The generation unit can learn existing teaching material data, such as text data or multimedia data, and generate new teaching materials based on the data. The generation unit can automatically generate the content of the teaching materials, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. This makes it possible to generate optimal teaching materials by learning existing teaching material data. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input existing teaching material data into the generation AI and cause the generation AI to generate teaching materials.

[0061] The planning unit can analyze each student's learning history and level of understanding and propose an individual learning plan. The planning unit can, for example, analyze each student's learning history and level of understanding and propose an optimal learning plan for each student. The planning unit can, for example, use a generating AI to analyze each student's learning history and level of understanding and create a learning plan based on the results. The planning unit can, for example, collect learning history such as past test results and study time, and create a learning plan based on the collected data. The planning unit can, for example, evaluate understanding such as test scores and quiz correct answer rates, and create a learning plan based on the collected data. This enables individualized instruction by proposing an optimal learning plan for each student. Some or all of the above-described processing in the planning unit can be performed using, for example, a generating AI, or can be performed without using a generating AI. For example, the planning unit can input each student's learning history and level of understanding into the generating AI and have the generating AI create a learning plan.

[0062] The instruction unit can monitor the learning status of students in real time and provide instruction as necessary. The instruction unit can, for example, monitor the learning status of students in real time and provide appropriate instruction as necessary. The instruction unit can, for example, use a generative AI to monitor the learning status of students in real time and provide instruction based on the monitoring. The instruction unit can, for example, use a sensor or a data collection device to monitor the learning status of students in real time. The instruction unit can, for example, monitor the learning progress and level of understanding of students in real time and provide instruction based on the monitoring. This enables appropriate instruction by monitoring the learning status in real time. Some or all of the above-mentioned processing in the instruction unit can be performed, for example, using a generative AI, or can be performed without using a generative AI. For example, the instruction unit can input the learning status of students into a generative AI and leave the execution of instruction to the generative AI.

[0063] The grading unit can automatically grade students' answer sheets and provide individual feedback. The grading unit can, for example, automatically grade students' answer sheets and provide individual feedback. The grading unit can, for example, use a generation AI to automatically grade students' answer sheets and provide feedback based on the grade. The grading unit can, for example, use a scoring algorithm to automatically grade students' answer sheets. The grading unit can, for example, evaluate students' answer sheets based on scoring criteria and provide feedback based on the evaluation. This reduces the burden on teachers through automatic grading and individual feedback. Some or all of the above-mentioned processing in the grading unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the grading unit can input students' answer sheets into the generation AI and have the generation AI grade them and provide feedback.

[0064] The generation unit can estimate the student's emotions and adjust the difficulty of the teaching materials based on the estimated student's emotions. The generation unit can, for example, estimate the student's emotions and adjust the difficulty of the teaching materials based on the estimated student's emotions. The generation unit can, for example, use a generation AI to estimate the student's emotions and adjust the difficulty of the teaching materials based on the estimated student's emotions. The generation unit can, for example, estimate the student's emotions using technologies such as facial expression recognition and voice analysis. For example, if the student is stressed, the generation AI generates teaching materials that include many easy questions. For example, if the student is relaxed, the generation AI generates teaching materials that include difficult questions. For example, if the student is excited, the generation AI generates teaching materials that include many challenging questions. This improves learning effectiveness by adjusting the difficulty of the teaching materials according to the student's emotions. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the student's emotion data into the generation AI and cause the generation AI to adjust the difficulty of the teaching materials.

[0065] The generation unit can adjust the content of the teaching materials by referring to past learning data when generating the teaching materials. For example, the generation unit can optimize the content of the teaching materials by referring to past learning data when generating the teaching materials. For example, the generation unit can refer to past learning data using a generation AI and adjust the content of the teaching materials based on the data. For example, the generation unit can refer to a student's past test results and generate teaching materials that focus on areas of weakness. For example, the generation unit can analyze a student's past learning history and generate teaching materials that reinforce areas of low understanding. For example, the generation unit generates teaching materials according to the student's progress based on the student's past learning data. In this way, the content of the teaching materials can be optimized by referring to the past learning data. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input past learning data into the generation AI and cause the generation AI to adjust the content of the teaching materials.

[0066] The generation unit can apply different teaching material generation algorithms according to specific learning goals when generating teaching materials. For example, the generation unit can apply different teaching material generation algorithms according to specific learning goals when generating teaching materials. For example, the generation unit can apply different teaching material generation algorithms according to specific learning goals using a generation AI. For example, the generation unit generates teaching materials that include many experiment videos according to a science learning goal. For example, the generation unit generates teaching materials that include many practice problems according to a mathematics learning goal. For example, the generation unit generates teaching materials that include many listening materials according to an English learning goal. This makes it possible to generate teaching materials according to specific learning goals. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a specific learning goal into the generation AI and cause the generation AI to apply a teaching material generation algorithm.

[0067] The generation unit can estimate the student's emotions and select the format of the teaching materials based on the estimated student's emotions. For example, the generation unit can estimate the student's emotions and select the format of the teaching materials based on the estimated student's emotions. For example, the generation unit can estimate the student's emotions using a generation AI and select the format of the teaching materials based on the estimated student's emotions. For example, the generation unit can estimate the student's emotions using technologies such as facial expression recognition and voice analysis. For example, if the student is tired, the generation AI selects short video teaching materials. For example, if the student is concentrating, the generation AI selects text teaching materials. For example, if the student is excited, the generation AI selects interactive teaching materials. This improves learning effectiveness by selecting the format of the teaching materials according to the student's emotions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the student's emotion data into the generation AI and have the generation AI select the format of the teaching materials.

[0068] The generation unit can customize the theme of the teaching material based on the student's interests and concerns when generating the teaching material. For example, the generation unit customizes the theme of the teaching material based on the student's interests and concerns when generating the teaching material. For example, the generation unit can customize the theme of the teaching material based on the student's interests and concerns using a generation AI. For example, if the student is interested in science, the generation AI generates teaching material on a science-related theme. For example, if the student is interested in history, the generation AI generates teaching material on a history-related theme. For example, if the student is interested in sports, the generation AI generates teaching material on a sports-related theme. This makes it possible to generate teaching material based on the student's interests and concerns. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input student interest and concern data into the generation AI and cause the generation AI to customize the theme of the teaching material.

[0069] The generation unit can generate more familiar teaching materials by including content appropriate for the region and culture when generating the teaching materials. The generation unit can generate more familiar teaching materials by including content appropriate for the region and culture when generating the teaching materials. The generation unit can generate more familiar teaching materials by including content appropriate for the region and culture, for example, using a generation AI. The generation unit can generate more familiar teaching materials by including content appropriate for the region and culture, for example. The generation unit generates teaching materials related to the history and culture of the region where the student lives. The generation unit generates teaching materials related to the culture of the language the student is learning. The generation unit generates teaching materials related to a country or region in which the student is interested. This makes it possible to generate teaching materials appropriate for the region and culture. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input data related to the region and culture into the generation AI and cause the generation AI to customize the content of the teaching materials.

[0070] The planning unit can estimate the student's emotions and adjust the progress rate of the lesson plan based on the estimated student's emotions. The planning unit can, for example, estimate the student's emotions and adjust the progress rate of the lesson plan based on the estimated student's emotions. The planning unit can, for example, use a generation AI to estimate the student's emotions and adjust the progress rate of the lesson plan based on the estimated student's emotions. The planning unit can, for example, estimate the student's emotions using technologies such as facial expression recognition and voice analysis. For example, if the student is feeling stressed, the generation AI slows down the progress rate of the lesson plan. For example, if the student is relaxed, the generation AI normalizes the progress rate of the lesson plan. For example, if the student is excited, the generation AI speeds up the progress rate of the lesson plan. This improves learning effectiveness by adjusting the progress rate of the lesson plan according to the student's emotions. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the planning department can input the student's emotional data into the generation AI and have the generation AI adjust the progress speed of the learning plan.

[0071] The planning unit can improve the accuracy of the study plan by referring to the student's past learning history when formulating the study plan. For example, the planning unit can improve the accuracy of the plan by referring to the student's past learning history when formulating the study plan. For example, the planning unit can refer to the student's past learning history using a generation AI and formulate the study plan based on the past learning history. For example, the planning unit can refer to the student's past test results and formulate a study plan that focuses on areas of weakness. For example, the planning unit can analyze the student's past learning history and formulate a study plan that reinforces areas of low understanding. For example, the planning unit formulates a study plan according to the student's progress based on the student's past learning data. In this way, the accuracy of the study plan is improved by referring to the past learning history. Some or all of the above-described processing in the planning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the planning unit can input the student's past learning history into the generation AI and have the generation AI formulate the study plan.

[0072] The planning unit can apply different planning algorithms depending on specific learning goals when creating a lesson plan. For example, the planning unit can apply different planning algorithms depending on specific learning goals when creating a lesson plan. The planning unit can apply different planning algorithms depending on specific learning goals, for example, using a generation AI. For example, the planning unit creates a lesson plan that includes many experiments depending on a science learning goal. For example, the planning unit creates a lesson plan that includes many practice problems depending on a mathematics learning goal. For example, the planning unit creates a lesson plan that includes many listening comprehension depending on an English learning goal. This makes it possible to create a lesson plan depending on a specific learning goal. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the planning unit can input a specific learning goal into the generation AI and cause the generation AI to apply a planning algorithm.

[0073] The planning unit can estimate the student's emotions and prioritize the lesson plans based on the estimated student's emotions. The planning unit can, for example, estimate the student's emotions and prioritize the lesson plans based on the estimated student's emotions. The planning unit can, for example, use a generation AI to estimate the student's emotions and prioritize the lesson plans based on the estimated student's emotions. The planning unit can, for example, estimate the student's emotions using technologies such as facial expression recognition and voice analysis. For example, if the student is feeling stressed, the planning unit prioritizes relaxing content. For example, if the student is relaxed, the planning unit prioritizes regular learning content. For example, if the student is excited, the planning unit prioritizes challenging content. This improves learning effectiveness by prioritizing lesson plans according to the student's emotions. Some or all of the above-described processing in the planning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the planning unit can input student emotion data into the generation AI and have the generation AI determine the priority of the lesson plans.

[0074] The planning unit can customize the study plan based on the student's daily rhythm and schedule when creating the study plan. For example, the planning unit customizes the study plan based on the student's daily rhythm and schedule when creating the study plan. The planning unit can customize the plan based on the student's daily rhythm and schedule using, for example, a generation AI. For example, the planning unit creates the study plan based on the student's school timetable. For example, the planning unit creates the study plan based on the student's home schedule. For example, the planning unit creates the study plan based on the student's sleep rhythm. This makes it possible to create a study plan based on the student's daily rhythm and schedule. Some or all of the above-mentioned processing in the planning unit may be performed using, or without, the generation AI. For example, the planning unit can input data on the student's daily rhythm and schedule into the generation AI and cause the generation AI to customize the study plan.

[0075] The planning unit can provide a collaborative learning environment by including group learning and collaborative projects when formulating a learning plan. The planning unit can provide a collaborative learning environment by, for example, including group learning and collaborative projects when formulating a learning plan. The planning unit can provide a collaborative learning environment by, for example, using a generative AI. For example, the planning unit includes in the learning plan a project that students will work on in a group. For example, the planning unit includes in the learning plan a problem that students will solve together. For example, the planning unit includes in the learning plan an opportunity for students to learn collaboratively. This provides a collaborative learning environment, thereby improving the learning effectiveness of students. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the planning unit can input data on group learning and collaborative projects into the generative AI and cause the generative AI to create a learning plan.

[0076] The teaching unit can estimate the student's emotions and adjust the teaching method based on the estimated student's emotions. The teaching unit can, for example, estimate the student's emotions and adjust the teaching method based on the estimated student's emotions. The teaching unit can, for example, use a generation AI to estimate the student's emotions and adjust the teaching method based on the estimated student's emotions. The teaching unit can, for example, estimate the student's emotions using technologies such as facial expression recognition and voice analysis. For example, if the student is stressed, the generation AI can provide instruction in a gentle tone. For example, if the student is relaxed, the generation AI can provide instruction in a normal tone. For example, if the student is excited, the generation AI can provide instruction that includes many encouraging words. This improves learning effectiveness by adjusting the teaching method according to the student's emotions. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the teaching unit can input the student's emotion data into the generation AI and have the generation AI adjust the teaching method.

[0077] The teaching unit can improve the accuracy of teaching by referring to past teaching data when providing instruction. For example, the teaching unit can improve the accuracy of teaching by referring to past teaching data when providing instruction. The teaching unit can, for example, use a generation AI to refer to past teaching data and adjust the teaching method based on that data. For example, the teaching unit refers to a student's past teaching history and reapplies effective teaching methods. For example, the teaching unit analyzes a student's past teaching data, finds areas for improvement, and adjusts the teaching method. For example, the teaching unit provides instruction according to the student's progress based on the student's past teaching data. In this way, the accuracy of teaching is improved by referring to the past teaching data. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the teaching unit can input past teaching data into the generation AI and have the generation AI adjust the teaching method.

[0078] The teaching unit can apply different teaching algorithms depending on specific learning goals during instruction. For example, the teaching unit can apply different teaching algorithms depending on specific learning goals during instruction. The teaching unit can apply different teaching algorithms depending on specific learning goals, for example, using a generative AI. For example, the teaching unit provides instruction that includes many experiments depending on science learning goals. For example, the teaching unit provides instruction that includes many practice problems depending on mathematics learning goals. For example, the teaching unit provides instruction that includes many listening comprehension depending on English learning goals. This makes it possible to provide instruction that is tailored to specific learning goals. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the teaching unit can input a specific learning goal into the generative AI and have the generative AI apply the teaching algorithm.

[0079] The teaching unit can estimate the student's emotions and adjust the timing of instruction based on the estimated student's emotions. The teaching unit can, for example, estimate the student's emotions and adjust the timing of instruction based on the estimated student's emotions. The teaching unit can, for example, use a generation AI to estimate the student's emotions and adjust the timing of instruction based on the estimated student's emotions. The teaching unit can, for example, estimate the student's emotions using technologies such as facial expression recognition and voice analysis. For example, if the student is feeling stressed, the generation AI can suggest a break and delay the timing of instruction. For example, if the student is relaxed, the generation AI can provide instruction at a normal timing. For example, if the student is excited, the generation AI can provide instruction at a timing when the student's concentration is highest. This improves learning effectiveness by adjusting the timing of instruction according to the student's emotions. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the teaching unit can input student emotion data into the generation AI and have the generation AI adjust the timing of instruction.

[0080] The teaching unit can customize teaching content based on the student's interests and concerns during teaching. For example, the teaching unit customizes teaching content based on the student's interests and concerns during teaching. The teaching unit can customize teaching content based on the student's interests and concerns using, for example, a generation AI. For example, if a student is interested in science, the generation AI provides science-related teaching content. For example, if a student is interested in history, the generation AI provides history-related teaching content. For example, if a student is interested in sports, the generation AI provides sports-related teaching content. This enables teaching based on the student's interests and concerns. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the teaching unit can input student interest data into the generation AI and have the generation AI customize the teaching content.

[0081] The teaching unit can realize a flexible learning environment by providing hybrid online and offline instruction during instruction. The teaching unit can realize a flexible learning environment by, for example, providing hybrid online and offline instruction during instruction. The teaching unit can realize a flexible learning environment by, for example, using a generation AI to provide hybrid online and offline instruction. For example, when a student studies at home, the generation AI provides online instruction. For example, when a student studies at school, the generation AI provides offline instruction. For example, when a student studies while traveling, the generation AI provides combined online and offline instruction. This realizes a flexible learning environment through hybrid online and offline instruction. Some or all of the above-described processing in the teaching unit may be performed using, or without, the generation AI. For example, the teaching unit can input online and offline instruction data into the generation AI and cause the generation AI to provide hybrid instruction.

[0082] The scoring unit can estimate the student's emotions and adjust the way feedback is expressed based on the estimated student's emotions. The scoring unit can, for example, estimate the student's emotions and adjust the way feedback is expressed based on the estimated student's emotions. The scoring unit can, for example, use a generation AI to estimate the student's emotions and adjust the way feedback is expressed based on the estimated student's emotions. The scoring unit can, for example, estimate the student's emotions using technologies such as facial expression recognition or voice analysis. For example, if the student is feeling stressed, the scoring unit can provide feedback using gentle words. For example, if the student is relaxed, the scoring unit can provide feedback using normal words. For example, if the student is excited, the scoring unit can provide feedback that includes many encouraging words. This improves learning effectiveness by adjusting the way feedback is expressed based on the student's emotions. Some or all of the above-mentioned processing in the scoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the scoring department can input students' emotional data into the generation AI and have the generation AI adjust the way feedback is expressed.

[0083] The scoring unit can improve the accuracy of scoring by referring to past scoring data when scoring. For example, the scoring unit can improve the accuracy of scoring by referring to past scoring data when scoring. For example, the scoring unit can refer to past scoring data using a generation AI and adjust the scoring method based on that data. For example, the scoring unit can refer to a student's past answer sheets and provide feedback to prevent the student from making the same mistake again. For example, the scoring unit can analyze a student's past scoring data and provide feedback to reinforce areas where the student's understanding is low. For example, the scoring unit can provide feedback according to the student's progress based on the student's past scoring data. In this way, the accuracy of scoring is improved by referring to the past scoring data. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scoring unit can input past scoring data into a generation AI and have the generation AI adjust the scoring method.

[0084] The scoring unit can apply different scoring algorithms depending on specific learning goals during scoring. For example, the scoring unit can apply different scoring algorithms depending on specific learning goals during scoring. For example, the scoring unit can apply different scoring algorithms depending on specific learning goals using a generation AI. For example, the scoring unit applies a lab report scoring algorithm depending on a science learning goal. For example, the scoring unit applies a practice problem scoring algorithm depending on a mathematics learning goal. For example, the scoring unit applies a listening test scoring algorithm depending on an English learning goal. This enables scoring according to specific learning goals. Some or all of the above-mentioned processing in the scoring unit may be performed using, or without, the generation AI. For example, the scoring unit can input a specific learning goal into the generation AI and have the generation AI apply the scoring algorithm.

[0085] The scoring unit can estimate the student's emotions and adjust the timing of feedback based on the estimated student's emotions. The scoring unit can, for example, estimate the student's emotions and adjust the timing of feedback based on the estimated student's emotions. The scoring unit can, for example, use a generation AI to estimate the student's emotions and adjust the timing of feedback based on the estimated student's emotions. The scoring unit can, for example, estimate the student's emotions using technologies such as facial expression recognition and voice analysis. For example, if the student is feeling stressed, the scoring unit causes the generation AI to delay the timing of feedback. For example, if the student is relaxed, the scoring unit causes the generation AI to provide feedback at a normal timing. For example, if the student is excited, the scoring unit causes the generation AI to provide feedback quickly. This improves learning effectiveness by adjusting the timing of feedback according to the student's emotions. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the scoring unit can input the student's emotion data into the generation AI and cause the generation AI to adjust the timing of feedback.

[0086] The scoring unit can adjust the level of detail of the feedback based on the student's level of understanding during scoring. The scoring unit, for example, adjusts the level of detail of the feedback based on the student's level of understanding during scoring. The scoring unit can adjust the level of detail of the feedback based on the student's level of understanding, for example, using a generation AI. The scoring unit can evaluate the level of understanding, such as test scores or the percentage of correct answers on quizzes, and adjust the level of detail of the feedback based on the evaluation. For example, if the student's level of understanding is low, the generation AI provides detailed feedback. For example, if the student's level of understanding is medium, the generation AI provides normal feedback. For example, if the student's level of understanding is high, the generation AI provides concise feedback. This enables feedback according to the student's level of understanding. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the scoring unit can input the student's level of understanding data into the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0087] The scoring unit can provide a multifaceted evaluation by including group evaluation and peer review when scoring. The scoring unit can provide a multifaceted evaluation by including group evaluation and peer review when scoring. The scoring unit can provide a multifaceted evaluation by including group evaluation and peer review using, for example, a generation AI. For example, the scoring unit has the generation AI perform group evaluation of assignments worked on by students in groups. For example, the scoring unit has the generation AI reflect the results of students' peer reviews in its scoring. For example, the scoring unit has the generation AI perform multifaceted evaluation of assignments solved by students collaboratively. This improves students' learning effectiveness through multifaceted evaluation. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the scoring unit can input data from group evaluations and peer reviews into the generation AI and leave the execution of the multifaceted evaluation to the generation AI. === Hard Collateral 1-1 === Each of the multiple elements, including the generation unit, planning unit, instruction unit, and grading unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and learns existing teaching material data to generate optimal teaching materials. The planning unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and analyzes each student's learning history and level of understanding and proposes an optimal learning plan for each student. The instruction unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and monitors students' learning progress in real time and provides appropriate guidance as needed. The grading unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and automatically grades students' answers and provides individual feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the generation unit, planning unit, instruction unit, and grading unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and learns existing teaching material data and generates optimal teaching materials. The planning unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and analyzes each student's learning history and level of understanding and proposes an optimal learning plan for each student. The instruction unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and monitors students' learning status in real time and provides appropriate guidance as needed. The grading unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and automatically grades students' answers and provides individual feedback. === Hard Collateral 1-3 === Each of the multiple elements, including the generation unit, planning unit, instruction unit, and grading unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and learns existing teaching material data to generate optimal teaching materials. The planning unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and analyzes each student's learning history and level of understanding and proposes an optimal individual learning plan. The instruction unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and monitors students' learning status in real time and provides appropriate guidance as needed. The grading unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and automatically grades students' answers and provides individual feedback. === Hard Collateral 1-4 === Each of the multiple elements, including the generation unit, planning unit, instruction unit, and grading unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and learns existing teaching material data and generates optimal teaching materials. The planning unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and analyzes each student's learning history and level of understanding and proposes an optimal learning plan for each student. The instruction unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and monitors students' learning status in real time and provides appropriate guidance as needed. The grading unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and automatically grades students' answers and provides individual feedback.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The education support system further includes a motivation unit. The motivation unit can provide a reward system to increase students' motivation to learn. For example, the motivation unit can award badges or points when students achieve specific learning goals. The motivation unit can also provide rewards when students achieve a certain amount of study time. Furthermore, the motivation unit can provide a ranking system to increase students' motivation to learn by having them compete with other students. This is expected to increase students' motivation to learn and improve their learning effectiveness.

[0090] The generator can customize the learning materials according to the learning style when generating them. For example, for visual learners, it can generate learning materials that include many diagrams and graphs. For auditory learners, it can generate learning materials that include many audio and video elements. Furthermore, for tactile learners, it can generate learning materials that include many interactive elements. This allows the provision of learning materials that match the students' learning styles, which is expected to improve learning effectiveness.

[0091] The planning section can support students in setting goals when formulating a learning plan. For example, the planning section can help students set short-term learning goals. The planning section can also help students set medium-term learning goals. Furthermore, the planning section can help students set long-term learning goals. This allows students to study with clear goals in mind, which is expected to improve learning effectiveness.

[0092] The teaching staff can adjust the learning pace according to the student's learning situation. For example, if the student's level of understanding is high, the teaching staff can speed up the learning pace. On the other hand, if the student's level of understanding is low, the teaching staff can slow down the learning pace. Furthermore, if a student is struggling in a particular area, the teaching staff can provide instruction that focuses on that area. This allows the teaching to be provided according to the student's learning pace, and is expected to improve learning effectiveness.

[0093] The grading department can visualize students' learning progress based on the grading results. For example, the grading department can display students' test results in graphs or charts. The grading department can also display students' learning progress on a timeline. Furthermore, the grading department can display students' understanding levels in different colors. This allows students to visually grasp their learning progress, which is expected to increase their motivation to learn.

[0094] The generation unit can estimate the student's emotions and adjust the learning content based on the estimated student's emotions. For example, if a student is feeling anxious, the generation AI can generate learning materials containing relaxing content. If a student is excited, the generation AI can generate learning materials containing challenging content. Furthermore, if a student is tired, the generation AI can generate learning materials containing content that can be completed in a short amount of time. This is expected to provide learning content that corresponds to the student's emotions, improving learning effectiveness.

[0095] The planning unit can estimate the student's emotions and adjust the flexibility of the lesson plan based on the estimated student emotions. For example, if the student is feeling stressed, the generating AI can include more break time in the lesson plan. If the student is relaxed, the generating AI can proceed with the lesson plan as usual. Furthermore, if the student is excited, the generating AI can include more challenging tasks in the lesson plan. This is expected to provide a flexible lesson plan that responds to the student's emotions and improve learning effectiveness.

[0096] The teaching unit can estimate the student's emotions and adjust the tone of instruction based on the estimated student's emotions. For example, if a student is feeling anxious, the generation AI can provide instruction in a gentle tone. If the student is relaxed, the generation AI can provide instruction in a normal tone. Furthermore, if the student is excited, the generation AI can provide instruction in an encouraging tone. This is expected to provide instruction that is appropriate for the student's emotions, improving learning effectiveness.

[0097] The grading department can estimate the student's emotions and adjust the content of the feedback based on the estimated student's emotions. For example, if a student is feeling stressed, the generation AI can provide content that includes a lot of positive feedback. If the student is relaxed, the generation AI can provide normal feedback. Furthermore, if the student is excited, the generation AI can provide challenging feedback. This is expected to provide feedback that corresponds to the student's emotions, improving learning effectiveness.

[0098] The planning unit can estimate the student's emotions and adjust the priority of the lesson plan based on the estimated student's emotions. For example, if the student is feeling stressed, the generating AI can prioritize relaxing content. Also, if the student is relaxed, the generating AI can prioritize regular learning content. Furthermore, if the student is excited, the generating AI can prioritize challenging content. This is expected to provide lesson plan priorities according to the student's emotions, improving learning effectiveness.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The generation unit generates teaching materials. The generation unit, for example, learns existing teaching material data and generates optimal teaching materials. The generation unit can learn existing teaching material data, such as text data and multimedia data, and generate new teaching materials based on the data. The generation unit can automatically generate the content of the teaching materials, for example, using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 2: The planning unit creates a learning plan based on the learning materials generated by the generation unit. The planning unit, for example, analyzes each student's learning history and level of understanding and proposes an optimal learning plan for each student. For example, the planning unit can use a generation AI to analyze each student's learning history and level of understanding and create a learning plan based on that. Step 3: The teaching department provides individual instruction and learning support based on the learning plan drawn up by the planning department. The teaching department, for example, monitors the student's learning situation in real time and provides appropriate instruction as needed. The teaching department can, for example, use generative AI to monitor the student's learning situation in real time and provide instruction based on that. Step 4: The grading unit automatically grades the results of the instruction provided by the teaching unit and provides individual feedback. The grading unit, for example, automatically grades students' answer sheets and provides individual feedback. The grading unit can, for example, use generative AI to automatically grade students' answer sheets and provide feedback based on the grades.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that generates teaching materials; a planning unit that creates a learning plan based on the learning materials generated by the generation unit; a teaching department that provides individual instruction and learning support based on the learning plan drawn up by the planning department; a scoring unit that automatically scores the results of the instruction provided by the instruction unit and provides individual feedback; A system characterized by:

2. The generation unit Learning from existing teaching material data and generating teaching materials 2. The system of claim 1.

3. The planning unit Analyze each student's learning history and level of understanding and propose individual learning plans 2. The system of claim 1.

4. The leadership team: Monitor student learning progress in real time and provide guidance as needed 2. The system of claim 1.

5. The scoring unit Automatically grade student work and provide personalized feedback 2. The system of claim 1.

6. The generation unit Estimate student emotions and adjust the difficulty of the material based on the estimated student emotions 2. The system of claim 1.

7. The generation unit When generating teaching materials, refer to past learning data to adjust the content of the materials.

2. The system of claim 1.

8. The generation unit When generating learning materials, different learning material generation algorithms are applied depending on the specific learning objectives.

2. The system of claim 1.

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