System

Abstract

An object of the system according to the embodiment is to provide an optimal learning curriculum based on the learning progress and understanding level of each student.SOLUTION: A system includes a learning history analysis unit, a curriculum generation unit, a question generation unit, and a difficulty level adjustment unit. The learning history analysis unit analyzes the learning history. The curriculum generation unit generates an individual learning curriculum based on the data analyzed by the learning history analysis unit. The question generation part generates a question for understanding level confirmation on the basis of the curriculum generated by the curriculum generation part. The difficulty level adjustment unit adjusts a difficulty level of the question generated by the question generation unit.SELECTED DRAWING: Figure 1

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 have had the problem of making it difficult to provide an optimal learning curriculum for each student based on their learning progress and level of understanding.

[0005] The system according to the embodiment aims to provide an optimal learning curriculum based on the learning progress and level of understanding of each student. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning history analysis unit, a curriculum generation unit, a question generation unit, and a difficulty level adjustment unit. The learning history analysis unit analyzes the learning history. The curriculum generation unit generates an individual learning curriculum based on the data analyzed by the learning history analysis unit. The question generation unit generates questions for confirming understanding based on the curriculum generated by the curriculum generation unit. The difficulty level adjustment unit adjusts the difficulty level of the questions generated by the question generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal learning curriculum based on the learning progress and level of understanding of each student. [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) The educational support system according to an embodiment of the present invention utilizes generative AI to create and update individual learning curricula, generate questions to check comprehension, and adjust the difficulty level of questions and problems. As a result, the educational support system can bring about a wide range of effects, such as promoting individual learning, improving comprehension, increasing motivation to learn, and supporting teachers.

[0029] The education support system according to the embodiment includes a learning history analysis unit, a curriculum generation unit, a question generation unit, and a difficulty level adjustment unit. The learning history analysis unit analyzes learning histories. For example, the learning history analysis unit collects and analyzes data such as students' study time, learning content, and grades. The learning history analysis unit can also analyze students' learning patterns using data mining technology. The learning history analysis unit can also analyze students' learning tendencies using statistical analysis technology. The curriculum generation unit generates an individualized learning curriculum based on the data analyzed by the learning history analysis unit. For example, the curriculum generation unit creates an optimal learning curriculum based on the students' learning goals and learning plans. The curriculum generation unit can also dynamically update the curriculum based on the students' learning history using a generation AI. The curriculum generation unit can also adjust the selection of learning materials and the distribution of learning content. The question generation unit generates questions for checking comprehension levels based on the curriculum generated by the curriculum generation unit. For example, the question generation unit creates questions of an appropriate level of difficulty depending on the students' level of comprehension. The question generation unit can also use the generation AI to analyze students' past response data and adjust the difficulty of questions to aim for an 80% to 90% correct answer rate. The question generation unit can also generate various types of questions, such as multiple-choice questions and written questions. The difficulty adjustment unit adjusts the difficulty of the questions generated by the question generation unit. For example, the difficulty adjustment unit analyzes students' learning history and response data and adjusts the difficulty of questions based on the analysis. The difficulty adjustment unit can also use the generation AI to analyze and understand students' qualifications and adjust the difficulty of questions and problems. The difficulty adjustment unit can also provide high-difficulty questions for students who want to achieve perfect scores and questions of an appropriate level for students who want to clarify what they understand and what they don't understand. This enables the education support system according to the embodiment to create and update individual learning curricula, generate questions to check comprehension, and adjust the difficulty of questions and problems. For example, the generation AI can create an optimal learning curriculum and update the learning content based on the student's learning history and level of comprehension. The generation AI can create questions of an appropriate level of difficulty according to the student's level of comprehension, thereby enhancing their motivation to learn.The generative AI analyzes and understands students' qualities and adjusts the difficulty of questions and problems to maximize learning effectiveness.

[0030] The learning history analysis unit can analyze a student's learning style and create an optimal learning curriculum based on that analysis. For example, the learning history analysis unit evaluates visual, auditory, and tactile elements so that the generation AI can analyze the student's learning style. For example, it can provide materials that make extensive use of diagrams and graphs to a student who prefers visual learning, and materials that include a lot of audio commentary to a student who prefers auditory learning. The learning history analysis unit also uses past learning data and survey results to analyze a student's learning style. For example, it can create a curriculum that makes extensive use of visual notes for a student who prefers visual learning, and one that includes a lot of experiments and hands-on activities for a student who prefers tactile learning. The learning history analysis unit also allows the generation AI to analyze a student's learning style in real time and dynamically adjust the learning curriculum. For example, if a student shows high concentration on visual materials during learning, the system can increase the visual elements in subsequent curriculum. This allows the system to provide an optimal curriculum tailored to each student's learning style.

[0031] The learning history analysis unit can analyze a student's daily rhythm and peak concentration times to propose an optimal study schedule. For example, the learning history analysis unit collects daily activity data and identifies peak concentration times so that the generation AI can analyze the student's daily rhythm. For example, it assigns more difficult tasks to morning people in the morning, and tasks requiring more concentration in the evening for night people. The learning history analysis unit also uses data from wearable devices to analyze a student's peak concentration times. For example, it identifies times when students are most likely to concentrate based on their heart rate and activity level, and assigns important learning content to those times. The generation AI also analyzes a student's daily rhythm in real time and dynamically adjusts the study schedule. For example, if concentration tends to decline on certain days or at certain times of the day, it assigns lighter tasks or review material to those times. This allows the system to provide an optimal study schedule tailored to the student's daily rhythm and concentration level.

[0032] The curriculum generation unit can create a curriculum that integrates home learning and school learning based on the home environment and parents' educational policies. For example, the curriculum generation unit uses a generation AI to analyze the home environment and parents' educational policies and create a curriculum that integrates home learning and school learning based on that. For example, the curriculum can reflect the subjects and learning methods that parents prioritize. The curriculum generation unit also collects home environment data and creates a curriculum that balances home learning and school learning. For example, it takes into account study time and resources at home and provides tasks that complement the content learned at school. The curriculum generation unit also takes into account parents' educational policies and dynamically adjusts the curriculum that integrates home learning and school learning. For example, it incorporates learning methods and materials recommended by parents into the curriculum, seamlessly integrating home and school learning. This makes it possible to provide an optimal curriculum that suits the home environment and parents' educational policies.

[0033] The curriculum generation unit can create a group learning curriculum with a common learning theme for students of different grades or subjects. For example, the generation AI analyzes students of different grades or subjects and creates a group learning curriculum with a common learning theme. For example, students of different grades collaborate to learn basic mathematical concepts as a common theme. The curriculum generation unit also creates a project-based curriculum with a common learning theme for students of different grades or subjects. For example, students of different grades collaborate to learn through science experiments or research projects. The curriculum generation unit also analyzes the learning data of students of different grades or subjects and dynamically adjusts the curriculum with a common learning theme. For example, if there are differences in understanding of a particular theme, individual supplementary materials are provided. This allows students of different grades or subjects to collaborate to learn.

[0034] The question generation unit can analyze a student's past error patterns, identify the cause of the error, and generate questions to help the student overcome that. For example, the question generation unit uses a generation AI to analyze a student's past error patterns and identify the cause of the error. For example, it detects a misunderstanding of a specific concept or calculation method and generates questions to help the student overcome that misunderstanding. The question generation unit also analyzes a student's error patterns and generates questions that include supplementary explanations based on the cause of the error. For example, it provides questions to re-explain the concept that caused the error and confirm that understanding. The question generation unit also analyzes a student's error patterns in real time, identify the cause of the error, and generate questions to help the student overcome it immediately. For example, it provides a related supplementary question immediately after an incorrect answer to deepen understanding. This makes it possible to identify the cause of a student's error and provide questions to help the student overcome it.

[0035] The question generation unit can analyze the student's learning progress in real time and update the questions for checking comprehension as appropriate. In the question generation unit, for example, a generation AI analyzes the student's learning progress in real time and dynamically updates the questions for checking comprehension. For example, new questions are provided at an appropriate time depending on the progress of the learning content. The question generation unit also builds a system that appropriately updates the questions for checking comprehension based on the student's learning progress data. For example, when a specific unit is completed, questions related to that unit are generated. In addition, the question generation unit monitors the student's learning progress in real time using a generation AI and dynamically adjusts the questions for checking comprehension. For example, if the student's learning progress is slow, more basic questions are asked, and if the student's progress is fast, more applied questions are asked. This makes it possible to provide questions for checking comprehension according to the student's learning progress.

[0036] The question generation unit generates cross-curriculum questions that integrate knowledge from different departments and subjects, making it possible to check the overall level of understanding. For example, the generation AI in the question generation unit integrates knowledge from different departments and subjects to generate cross-curriculum questions. For example, it creates questions that combine knowledge of mathematics and science to check the overall level of understanding. The question generation unit also generates project-based questions that integrate knowledge from different departments and subjects. For example, it provides questions that mathematically analyze the results of science experiments to check the overall level of understanding. The question generation unit also analyzes learning data from different departments and subjects and dynamically generates cross-curriculum questions. For example, it provides questions that analyze historical events from a scientific perspective to check the overall level of understanding. This makes it possible to provide questions that integrate knowledge from different departments and subjects and check the overall level of understanding.

[0037] The question generation unit generates questions related to students' interests and hobbies, making the learning content feel more familiar. For example, the question generation unit uses a generation AI to analyze students' interests and hobbies and generate questions related to them. For example, a student who likes sports is provided with sports-related math questions. The question generation unit also generates questions based on students' hobbies and interests, making the learning content feel more familiar. For example, a student who likes music is provided with science questions related to music. The question generation unit also uses a generation AI to analyze students' interests and hobbies in real time and dynamically generate questions based on the analysis. For example, questions related to the latest movies or games are provided, making the learning content feel more familiar. This provides questions related to students' interests and hobbies, making the learning content feel more familiar.

[0038] The difficulty adjustment unit can analyze a student's response speed and adjust the difficulty of the questions in real time according to the response speed. For example, the generation AI analyzes a student's response speed and adjusts the difficulty of the questions in real time according to the response speed. For example, it provides more difficult questions to students who respond quickly and provides less difficult questions to students who respond slowly. The difficulty adjustment unit also builds a system that dynamically adjusts the difficulty of questions based on student response speed data. For example, if the response speed exceeds a certain standard, it increases the difficulty of the next question. The difficulty adjustment unit also monitors a student's response speed in real time using the generation AI and dynamically adjusts the difficulty of the questions. For example, if the response speed is slow, it adjusts the difficulty by providing hints, etc. This makes it possible to adjust the difficulty of questions in real time according to the student's response speed.

[0039] The difficulty adjustment unit can generate questions that are related to past learning content based on the student's learning history and adjust the difficulty of the questions. For example, the difficulty adjustment unit uses a generation AI to analyze the student's learning history and generate questions that are related to past learning content. For example, the difficulty adjustment unit provides questions for reviewing content that has been learned in the past and adjusts the difficulty. The difficulty adjustment unit also builds a system that generates highly relevant questions and adjusts the difficulty based on the student's learning history data. For example, the difficulty adjustment unit provides questions that help the student advance to the next step based on past learning content. The difficulty adjustment unit also uses a generation AI to analyze the student's learning history in real time and dynamically generate questions that are related to past learning content. For example, the difficulty adjustment unit provides questions that help the student review content that they struggled with in the past and adjusts the difficulty. This makes it possible to adjust the difficulty of questions based on the student's learning history.

[0040] The difficulty adjustment unit can adjust the difficulty of questions from an international perspective by referring to the educational curricula of different cultures and regions. In the difficulty adjustment unit, for example, the generation AI analyzes the educational curricula of different cultures and regions and adjusts the difficulty of questions from an international perspective. For example, the difficulty of questions is set by referring to the educational standards of different countries. The difficulty adjustment unit also builds a system that adjusts the difficulty of questions based on the educational curricula of different cultures and regions. For example, the difficulty of questions is adjusted based on international educational standards. In addition, the difficulty adjustment unit analyzes educational data from different cultures and regions in real time by the generation AI and dynamically adjusts the difficulty of questions from an international perspective. For example, the difficulty of questions is set based on the educational curricula of different countries. This makes it possible to adjust the difficulty of questions from an international perspective.

[0041] The difficulty adjustment unit can generate questions for acquiring necessary skills based on the student's future career goals and adjust the difficulty of the questions. For example, the difficulty adjustment unit uses a generation AI to analyze the student's future career goals and generate questions for acquiring necessary skills based on the goals. For example, a student aiming to become an engineer is provided with questions related to programming. The difficulty adjustment unit also builds a system that generates questions for acquiring necessary skills and adjusts the difficulty based on the student's career goal data. For example, a student aiming to work in the medical field is provided with questions related to medicine. The difficulty adjustment unit also analyzes the student's career goals in real time and dynamically generates questions for acquiring necessary skills. For example, questions for acquiring skills related to the future career are provided and the difficulty is adjusted. This makes it possible to adjust the difficulty of the questions based on the student's future career goals.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The education support system can further include a health management unit that monitors students' health. For example, the health management unit collects data from wearable devices and analyzes students' heart rates and sleep patterns. This makes it possible to adjust study schedules based on their health status. For example, for students who have consistently experienced sleep deprivation, the unit can adjust their schedules to reduce their study load. The health management unit can also conduct regular health checks and suggest breaks and exercise as needed. This makes it possible to support effective learning while maintaining students' health.

[0044] The education support system can further include a communication support unit for improving students' social skills. The communication support unit supports, for example, group discussions and presentation practice. This allows students to increase opportunities to learn collaboratively with other students. For example, it can provide an online discussion forum, creating a space for students to exchange opinions. The communication support unit can also include a feedback function to support the improvement of students' communication skills. This can improve students' social skills and enhance the quality of their learning.

[0045] The education support system can further include a creative support section to foster students' creativity. The creative support section supports creative activities such as art, music, and programming. This provides students with opportunities to demonstrate their interests and talents. For example, it can hold online art contests or music recitals, providing a venue for students to present their work. The creative support section can also promote project-based learning and support projects in which students put their ideas into practice. This fosters students' creativity and allows them to experience the joy of learning.

[0046] The education support system can further include a career support section that supports students' career education. The career support section provides, for example, work experience and internship opportunities, allowing students to have a concrete image of their future careers. For example, the section can provide online job introductions and collaborative programs with companies, giving students the opportunity to experience actual workplaces. The career support section can also provide career counseling and propose career plans based on students' interests and aptitudes. This supports students' career education and helps them clarify their future goals.

[0047] The education support system may further include a time management support unit for improving students' self-management skills. The time management support unit, for example, manages students' study schedules and suggests ways to use their time efficiently. This allows students to acquire the ability to effectively manage their own time. For example, it provides functions for prioritizing study tasks and creating timelines. The time management support unit can also send regular reminders to support students in progressing with their studies as planned. This improves students' self-management skills and increases learning efficiency.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The learning history analysis unit analyzes the learning history. For example, the learning history analysis unit collects and analyzes data such as the student's study time, learning content, and grades. It can also use data mining and statistical analysis techniques to analyze the student's learning patterns and learning tendencies. Step 2: The curriculum generation unit generates an individualized learning curriculum based on the data analyzed by the learning history analysis unit. For example, an optimal learning curriculum can be created based on the student's learning goals and learning plan, and dynamically updated using the generation AI. It can also adjust the selection of learning materials and the distribution of learning content. Step 3: The question generation unit generates questions to check comprehension based on the curriculum generated by the curriculum generation unit. For example, questions of an appropriate level of difficulty can be created based on the student's level of comprehension, and the generation AI can analyze past response data and adjust the difficulty of the questions to aim for an 80% to 90% correct answer rate. It can also generate questions in various formats, such as multiple-choice and essay-style questions. Step 4: The difficulty adjustment unit adjusts the difficulty of the questions generated by the question generation unit. For example, it analyzes the student's learning history and response data and adjusts the difficulty of the questions based on that. It is also possible to use the generation AI to analyze and understand the student's abilities and adjust the difficulty of the questions and problems. Furthermore, it is possible to provide students who want to get a perfect score with highly difficult questions, and students who want to clarify what they understand and what they don't understand with questions of an appropriate level of difficulty.

[0050] (Example 2) The educational support system according to an embodiment of the present invention utilizes generative AI to create and update individual learning curricula, generate questions to check comprehension, and adjust the difficulty level of questions and problems. As a result, the educational support system can bring about a wide range of effects, such as promoting individual learning, improving comprehension, increasing motivation to learn, and supporting teachers.

[0051] The education support system according to the embodiment includes a learning history analysis unit, a curriculum generation unit, a question generation unit, and a difficulty level adjustment unit. The learning history analysis unit analyzes learning histories. For example, the learning history analysis unit collects and analyzes data such as students' study time, learning content, and grades. The learning history analysis unit can also analyze students' learning patterns using data mining technology. The learning history analysis unit can also analyze students' learning tendencies using statistical analysis technology. The curriculum generation unit generates an individualized learning curriculum based on the data analyzed by the learning history analysis unit. For example, the curriculum generation unit creates an optimal learning curriculum based on the students' learning goals and learning plans. The curriculum generation unit can also dynamically update the curriculum based on the students' learning history using a generation AI. The curriculum generation unit can also adjust the selection of learning materials and the distribution of learning content. The question generation unit generates questions for checking comprehension levels based on the curriculum generated by the curriculum generation unit. For example, the question generation unit creates questions of an appropriate level of difficulty depending on the students' level of comprehension. The question generation unit can also use the generation AI to analyze students' past response data and adjust the difficulty of questions to aim for an 80% to 90% correct answer rate. The question generation unit can also generate various types of questions, such as multiple-choice questions and written questions. The difficulty adjustment unit adjusts the difficulty of the questions generated by the question generation unit. For example, the difficulty adjustment unit analyzes students' learning history and response data and adjusts the difficulty of questions based on the analysis. The difficulty adjustment unit can also use the generation AI to analyze and understand students' qualifications and adjust the difficulty of questions and problems. The difficulty adjustment unit can also provide high-difficulty questions for students who want to achieve perfect scores and questions of an appropriate level for students who want to clarify what they understand and what they don't understand. This enables the education support system according to the embodiment to create and update individual learning curricula, generate questions to check comprehension, and adjust the difficulty of questions and problems. For example, the generation AI can create an optimal learning curriculum and update the learning content based on the student's learning history and level of comprehension. The generation AI can create questions of an appropriate level of difficulty according to the student's level of comprehension, thereby enhancing their motivation to learn.The generative AI analyzes and understands students' qualities and adjusts the difficulty of questions and problems to maximize learning effectiveness.

[0052] The learning history analysis unit can analyze a student's learning style and create an optimal learning curriculum based on that analysis. For example, the learning history analysis unit evaluates visual, auditory, and tactile elements so that the generation AI can analyze the student's learning style. For example, it can provide materials that make extensive use of diagrams and graphs to a student who prefers visual learning, and materials that include a lot of audio commentary to a student who prefers auditory learning. The learning history analysis unit also uses past learning data and survey results to analyze a student's learning style. For example, it can create a curriculum that makes extensive use of visual notes for a student who prefers visual learning, and one that includes a lot of experiments and hands-on activities for a student who prefers tactile learning. The learning history analysis unit also allows the generation AI to analyze a student's learning style in real time and dynamically adjust the learning curriculum. For example, if a student shows high concentration on visual materials during learning, the system can increase the visual elements in subsequent curriculum. This allows the system to provide an optimal curriculum tailored to each student's learning style.

[0053] The learning history analysis unit can analyze a student's daily rhythm and peak concentration times to propose an optimal study schedule. For example, the learning history analysis unit collects daily activity data and identifies peak concentration times so that the generation AI can analyze the student's daily rhythm. For example, it assigns more difficult tasks to morning people in the morning, and tasks requiring more concentration in the evening for night people. The learning history analysis unit also uses data from wearable devices to analyze a student's peak concentration times. For example, it identifies times when students are most likely to concentrate based on their heart rate and activity level, and assigns important learning content to those times. The generation AI also analyzes a student's daily rhythm in real time and dynamically adjusts the study schedule. For example, if concentration tends to decline on certain days or at certain times of the day, it assigns lighter tasks or review material to those times. This allows the system to provide an optimal study schedule tailored to the student's daily rhythm and concentration level.

[0054] The learning history analysis unit can use the emotion estimation function to monitor a student's emotions while studying in real time and suggest taking a break if the student feels stressed or fatigued. For example, the learning history analysis unit uses the emotion estimation function to analyze the student's facial expressions and voice to detect signs of stress or fatigue. For example, it estimates emotions from facial expressions and tone of voice and suggests taking a break if stress increases. The learning history analysis unit also monitors a student's emotions in real time and suggests activities to relax when the student feels stressed or fatigued. For example, it instructs the student to take deep breaths or do light stretches. The learning history analysis unit also dynamically adjusts the learning curriculum based on the emotion estimation data. For example, it switches to a less difficult task if stress increases, and then returns to a more difficult task when the student is relaxed. This reduces the student's stress and fatigue and improves learning efficiency.

[0055] The curriculum generation unit can create a curriculum that integrates home learning and school learning based on the home environment and parents' educational policies. For example, the curriculum generation unit uses a generation AI to analyze the home environment and parents' educational policies and create a curriculum that integrates home learning and school learning based on that. For example, the curriculum can reflect the subjects and learning methods that parents prioritize. The curriculum generation unit also collects home environment data and creates a curriculum that balances home learning and school learning. For example, it takes into account study time and resources at home and provides tasks that complement the content learned at school. The curriculum generation unit also takes into account parents' educational policies and dynamically adjusts the curriculum that integrates home learning and school learning. For example, it incorporates learning methods and materials recommended by parents into the curriculum, seamlessly integrating home and school learning. This makes it possible to provide an optimal curriculum that suits the home environment and parents' educational policies.

[0056] The curriculum generation unit can create a group learning curriculum with a common learning theme for students of different grades or subjects. For example, the generation AI analyzes students of different grades or subjects and creates a group learning curriculum with a common learning theme. For example, students of different grades collaborate to learn basic mathematical concepts as a common theme. The curriculum generation unit also creates a project-based curriculum with a common learning theme for students of different grades or subjects. For example, students of different grades collaborate to learn through science experiments or research projects. The curriculum generation unit also analyzes the learning data of students of different grades or subjects and dynamically adjusts the curriculum with a common learning theme. For example, if there are differences in understanding of a particular theme, individual supplementary materials are provided. This allows students of different grades or subjects to collaborate to learn.

[0057] The curriculum generation unit can use the emotion estimation function to suggest special assignments or rewards to increase a student's motivation to learn when their motivation to learn drops. For example, the curriculum generation unit uses the emotion estimation function to suggest special assignments when a student's motivation to learn drops. For example, it provides assignments related to a topic that piques the student's interest or a hobby to increase their motivation to learn. The curriculum generation unit also monitors the student's emotions in real time and suggests rewards when their motivation to learn drops. For example, it sets small goals that give the student a sense of accomplishment and provides praise or badges when the goals are achieved. The curriculum generation unit also dynamically adjusts special assignments or rewards to increase a student's motivation to learn when their motivation to learn drops, based on the emotion estimation data. For example, it provides assignments that incorporate game elements when their motivation to learn drops. This makes it possible to maintain and increase the student's motivation to learn.

[0058] The question generation unit can analyze a student's past error patterns, identify the cause of the error, and generate questions to help the student overcome that. For example, the question generation unit uses a generation AI to analyze a student's past error patterns and identify the cause of the error. For example, it detects a misunderstanding of a specific concept or calculation method and generates questions to help the student overcome that misunderstanding. The question generation unit also analyzes a student's error patterns and generates questions that include supplementary explanations based on the cause of the error. For example, it provides questions to re-explain the concept that caused the error and confirm that understanding. The question generation unit also analyzes a student's error patterns in real time, identify the cause of the error, and generate questions to help the student overcome it immediately. For example, it provides a related supplementary question immediately after an incorrect answer to deepen understanding. This makes it possible to identify the cause of a student's error and provide questions to help the student overcome it.

[0059] The question generation unit can analyze the student's learning progress in real time and update the questions for checking comprehension as appropriate. In the question generation unit, for example, a generation AI analyzes the student's learning progress in real time and dynamically updates the questions for checking comprehension. For example, new questions are provided at an appropriate time depending on the progress of the learning content. The question generation unit also builds a system that appropriately updates the questions for checking comprehension based on the student's learning progress data. For example, when a specific unit is completed, questions related to that unit are generated. In addition, the question generation unit monitors the student's learning progress in real time using a generation AI and dynamically adjusts the questions for checking comprehension. For example, if the student's learning progress is slow, more basic questions are asked, and if the student's progress is fast, more applied questions are asked. This makes it possible to provide questions for checking comprehension according to the student's learning progress.

[0060] The question generation unit can use the emotion estimation function to suggest a question format that has a relaxing effect to reduce the pressure students feel when asking questions. The question generation unit, for example, uses the emotion estimation function to suggest a question format that has a relaxing effect to reduce the pressure students feel when asking questions. For example, it provides multiple-choice questions so that students can answer in a relaxed state. The question generation unit also monitors students' emotions in real time and suggests a question format that has a relaxing effect when students feel pressured. For example, it provides humorous questions or background music to create a relaxed atmosphere. The question generation unit also dynamically adjusts the question format that has a relaxing effect when students feel pressured based on the emotion estimation data. For example, when pressure increases, it provides simple questions or questions that have a relaxing effect. This allows students to answer questions in a relaxed state.

[0061] The question generation unit generates cross-curriculum questions that integrate knowledge from different departments and subjects, making it possible to check the overall level of understanding. For example, the generation AI in the question generation unit integrates knowledge from different departments and subjects to generate cross-curriculum questions. For example, it creates questions that combine knowledge of mathematics and science to check the overall level of understanding. The question generation unit also generates project-based questions that integrate knowledge from different departments and subjects. For example, it provides questions that mathematically analyze the results of science experiments to check the overall level of understanding. The question generation unit also analyzes learning data from different departments and subjects and dynamically generates cross-curriculum questions. For example, it provides questions that analyze historical events from a scientific perspective to check the overall level of understanding. This makes it possible to provide questions that integrate knowledge from different departments and subjects and check the overall level of understanding.

[0062] The question generation unit generates questions related to students' interests and hobbies, making the learning content feel more familiar. For example, the question generation unit uses a generation AI to analyze students' interests and hobbies and generate questions related to them. For example, a student who likes sports is provided with sports-related math questions. The question generation unit also generates questions based on students' hobbies and interests, making the learning content feel more familiar. For example, a student who likes music is provided with science questions related to music. The question generation unit also uses a generation AI to analyze students' interests and hobbies in real time and dynamically generate questions based on the analysis. For example, questions related to the latest movies or games are provided, making the learning content feel more familiar. This provides questions related to students' interests and hobbies, making the learning content feel more familiar.

[0063] The question generation unit uses the emotion estimation function to generate questions related to topics that interest students most, thereby increasing their motivation to learn. For example, the question generation unit uses the emotion estimation function to identify topics that interest students most and generate questions related to those topics. For example, questions related to topics with high emotion scores are provided to increase motivation to learn. The question generation unit also monitors students' emotions in real time and dynamically generates questions related to topics of interest. For example, if a particular topic has a high emotion score, questions related to that topic are provided. The question generation unit also generates questions related to topics that interest students most based on emotion estimation data, thereby increasing their motivation to learn. For example, questions related to topics with high emotion scores are provided to make learning feel fun. This allows questions related to topics that interest students most to be provided and increase motivation to learn.

[0064] The difficulty adjustment unit can analyze a student's response speed and adjust the difficulty of the questions in real time according to the response speed. For example, the generation AI analyzes a student's response speed and adjusts the difficulty of the questions in real time according to the response speed. For example, it provides more difficult questions to students who respond quickly and provides less difficult questions to students who respond slowly. The difficulty adjustment unit also builds a system that dynamically adjusts the difficulty of questions based on student response speed data. For example, if the response speed exceeds a certain standard, it increases the difficulty of the next question. The difficulty adjustment unit also monitors a student's response speed in real time using the generation AI and dynamically adjusts the difficulty of the questions. For example, if the response speed is slow, it adjusts the difficulty by providing hints, etc. This makes it possible to adjust the difficulty of questions in real time according to the student's response speed.

[0065] The difficulty adjustment unit can generate questions that are related to past learning content based on the student's learning history and adjust the difficulty of the questions. For example, the difficulty adjustment unit uses a generation AI to analyze the student's learning history and generate questions that are related to past learning content. For example, the difficulty adjustment unit provides questions for reviewing content that has been learned in the past and adjusts the difficulty. The difficulty adjustment unit also builds a system that generates highly relevant questions and adjusts the difficulty based on the student's learning history data. For example, the difficulty adjustment unit provides questions that help the student advance to the next step based on past learning content. The difficulty adjustment unit also uses a generation AI to analyze the student's learning history in real time and dynamically generate questions that are related to past learning content. For example, the difficulty adjustment unit provides questions that help the student review content that they struggled with in the past and adjusts the difficulty. This makes it possible to adjust the difficulty of questions based on the student's learning history.

[0066] The difficulty adjustment unit can use the emotion estimation function to adjust the difficulty level to reduce the frustration felt by students with problems. The difficulty adjustment unit, for example, uses the emotion estimation function to detect the frustration felt by students with problems and adjust the difficulty level. For example, if frustration increases, it provides problems with a lower level of difficulty. The difficulty adjustment unit also monitors students' emotions in real time and adjusts the difficulty level to reduce frustration. For example, if the emotion score is low, it adjusts the difficulty level by providing hints, etc. The difficulty adjustment unit also builds a system that dynamically adjusts the difficulty level when students feel frustrated based on the emotion estimation data. For example, it provides easy problems when frustration increases, allowing students to relax. This reduces the frustration felt by students with problems.

[0067] The difficulty adjustment unit can adjust the difficulty of questions from an international perspective by referring to the educational curricula of different cultures and regions. In the difficulty adjustment unit, for example, the generation AI analyzes the educational curricula of different cultures and regions and adjusts the difficulty of questions from an international perspective. For example, the difficulty of questions is set by referring to the educational standards of different countries. The difficulty adjustment unit also builds a system that adjusts the difficulty of questions based on the educational curricula of different cultures and regions. For example, the difficulty of questions is adjusted based on international educational standards. In addition, the difficulty adjustment unit analyzes educational data from different cultures and regions in real time by the generation AI and dynamically adjusts the difficulty of questions from an international perspective. For example, the difficulty of questions is set based on the educational curricula of different countries. This makes it possible to adjust the difficulty of questions from an international perspective.

[0068] The difficulty adjustment unit can generate questions for acquiring necessary skills based on the student's future career goals and adjust the difficulty of the questions. For example, the difficulty adjustment unit uses a generation AI to analyze the student's future career goals and generate questions for acquiring necessary skills based on the goals. For example, a student aiming to become an engineer is provided with questions related to programming. The difficulty adjustment unit also builds a system that generates questions for acquiring necessary skills and adjusts the difficulty based on the student's career goal data. For example, a student aiming to work in the medical field is provided with questions related to medicine. The difficulty adjustment unit also analyzes the student's career goals in real time and dynamically generates questions for acquiring necessary skills. For example, questions for acquiring skills related to the future career are provided and the difficulty is adjusted. This makes it possible to adjust the difficulty of the questions based on the student's future career goals.

[0069] The difficulty level adjustment unit uses the emotion estimation function to provide questions of a difficulty level that gives students the greatest sense of accomplishment, thereby maintaining their motivation to learn. The difficulty level adjustment unit, for example, uses the emotion estimation function to identify questions of a difficulty level that gives students the greatest sense of accomplishment and provides them. For example, questions with a high emotion score may be provided preferentially to maintain motivation to learn. The difficulty level adjustment unit also monitors students' emotions in real time and dynamically provides questions of a difficulty level that gives students a sense of accomplishment. For example, questions with a high emotion score may be provided to increase motivation to learn. The difficulty level adjustment unit also builds a system that provides questions of a difficulty level that gives students the greatest sense of accomplishment and maintains motivation to learn, based on emotion estimation data. For example, questions with a high emotion score may be provided to make learning fun. This allows questions of a difficulty level that gives students the greatest sense of accomplishment to be provided, thereby maintaining motivation to learn.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The education support system can further include a health management unit that monitors students' health. For example, the health management unit collects data from wearable devices and analyzes students' heart rates and sleep patterns. This makes it possible to adjust study schedules based on their health status. For example, for students who have consistently experienced sleep deprivation, the unit can adjust their schedules to reduce their study load. The health management unit can also conduct regular health checks and suggest breaks and exercise as needed. This makes it possible to support effective learning while maintaining students' health.

[0072] The education support system can further include a communication support unit for improving students' social skills. The communication support unit supports, for example, group discussions and presentation practice. This allows students to increase opportunities to learn collaboratively with other students. For example, it can provide an online discussion forum, creating a space for students to exchange opinions. The communication support unit can also include a feedback function to support the improvement of students' communication skills. This can improve students' social skills and enhance the quality of their learning.

[0073] The education support system can further include a creative support section to foster students' creativity. The creative support section supports creative activities such as art, music, and programming. This provides students with opportunities to demonstrate their interests and talents. For example, it can hold online art contests or music recitals, providing a venue for students to present their work. The creative support section can also promote project-based learning and support projects in which students put their ideas into practice. This fosters students' creativity and allows them to experience the joy of learning.

[0074] The education support system can further include a career support section that supports students' career education. The career support section provides, for example, work experience and internship opportunities, allowing students to have a concrete image of their future careers. For example, the section can provide online job introductions and collaborative programs with companies, giving students the opportunity to experience actual workplaces. The career support section can also provide career counseling and propose career plans based on students' interests and aptitudes. This supports students' career education and helps them clarify their future goals.

[0075] The education support system may further include a time management support unit for improving students' self-management skills. The time management support unit, for example, manages students' study schedules and suggests ways to use their time efficiently. This allows students to acquire the ability to effectively manage their own time. For example, it provides functions for prioritizing study tasks and creating timelines. The time management support unit can also send regular reminders to support students in progressing with their studies as planned. This improves students' self-management skills and increases learning efficiency.

[0076] The education support system can also estimate the student's emotions and adjust the learning environment based on the estimated emotions. For example, if a student is feeling stressed, the emotion estimation function can be used to provide a relaxing environment. Specifically, the background of the learning screen can be changed or relaxing music can be played. Also, if a student wants to improve their concentration, an environment that makes it easier to concentrate can be provided. For example, the background can be simplified and music that improves concentration can be played. This makes it possible to provide an optimal learning environment that suits the student's emotions and improve learning efficiency.

[0077] The educational support system can also estimate students' emotions and adjust learning content based on the estimated emotions. For example, the emotion estimation function can be used to provide learning content related to topics that students are interested in. Specifically, it can provide educational materials that incorporate entertainment elements to help students enjoy learning. Also, if a student is tired, it can provide lighter learning content and allow time for them to refresh. This allows it to provide optimal learning content based on the student's emotions and maintain their motivation to learn.

[0078] The educational support system can also estimate students' emotions and provide feedback based on the estimated emotions. For example, if a student feels a sense of accomplishment using the emotion estimation function, it can provide praise or a badge. Specifically, it can send positive feedback when a student achieves a goal. Also, if a student feels frustrated, it can provide encouraging messages or advice. This allows it to provide appropriate feedback according to students' emotions and increase their motivation to learn.

[0079] The educational support system can also estimate a student's emotions and adjust the student's learning progress based on the estimated emotions. For example, if a student is feeling anxious, the emotion estimation function can be used to slow down the student's progress. Specifically, the learning content can be provided in small chunks, making it easier for the student to feel a sense of accomplishment. Also, if the student is confident, the system can speed up the student's progress. For example, it can provide more difficult tasks and increase opportunities to take on new challenges. This makes it possible to provide the optimal learning progress according to the student's emotions and maximize the learning effect.

[0080] The education support system can also estimate students' emotions and suggest special events to maintain their motivation to learn based on the estimated emotions. For example, if a student is losing motivation, the emotion estimation function can be used to suggest special events. Specifically, online learning contests and rewards based on achievement levels can be offered. Events incorporating game elements can also be held so that students can learn while having fun. This allows the system to provide optimal events based on students' emotions and maintain their motivation to learn.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The learning history analysis unit analyzes the learning history. For example, the learning history analysis unit collects and analyzes data such as the student's study time, learning content, and grades. It can also use data mining and statistical analysis techniques to analyze the student's learning patterns and learning tendencies. Step 2: The curriculum generation unit generates an individualized learning curriculum based on the data analyzed by the learning history analysis unit. For example, an optimal learning curriculum can be created based on the student's learning goals and learning plan, and dynamically updated using the generation AI. It can also adjust the selection of learning materials and the distribution of learning content. Step 3: The question generation unit generates questions to check comprehension based on the curriculum generated by the curriculum generation unit. For example, questions of an appropriate level of difficulty can be created based on the student's level of comprehension, and the generation AI can analyze past response data and adjust the difficulty of the questions to aim for an 80% to 90% correct answer rate. It can also generate questions in various formats, such as multiple-choice and essay-style questions. Step 4: The difficulty adjustment unit adjusts the difficulty of the questions generated by the question generation unit. For example, it analyzes the student's learning history and response data and adjusts the difficulty of the questions based on that. It is also possible to use the generation AI to analyze and understand the student's abilities and adjust the difficulty of the questions and problems. Furthermore, it is possible to provide students who want to get a perfect score with highly difficult questions, and students who want to clarify what they understand and what they don't understand with questions of an appropriate level of difficulty.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, 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 robot 414 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.

[0128] 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.

[0129] 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.

[0130] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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 learning history analysis unit that analyzes the learning history; a curriculum generation unit that generates an individual learning curriculum based on the data analyzed by the learning history analysis unit; a question generation unit that generates questions for verifying understanding based on the curriculum generated by the curriculum generation unit; a difficulty level adjustment unit that adjusts the difficulty level of the questions generated by the question generation unit. A system characterized by:

2. The learning history analysis unit Analyze students' learning styles and create optimal learning curriculum based on that.

2. The system of claim 1.

3. The learning history analysis unit Analyze students' daily rhythms and peak concentration times to suggest optimal study schedules 2. The system of claim 1.

4. The learning history analysis unit Monitor students' emotions in real time while they study and suggest breaks when they feel stressed or fatigued 2. The system of claim 1.

5. The curriculum generation unit Create a curriculum that integrates home learning and school learning based on the home environment and parents' educational preferences.

2. The system of claim 1.

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