system

Abstract

The system according to this embodiment aims to improve learning effectiveness by providing individualized learning plans tailored to each student's learning progress. [Solution] The system according to the embodiment comprises a learning progress analysis unit, a learning plan proposal unit, a quiz generation unit, a learning support unit, and a material customization unit. The learning progress analysis unit analyzes the student's learning progress. The learning plan proposal unit proposes an individual learning plan based on the results analyzed by the learning progress analysis unit. The quiz generation unit generates an interactive quiz or game based on the learning plan proposed by the learning plan proposal unit. The learning support unit supports learning through the quiz or game generated by the quiz generation unit. The material customization unit automatically customizes materials and resources based on the learning plan proposed by the learning plan proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide an individual learning plan according to the learning progress of students, and there is room for improvement in enhancing the learning effect.

[0005] The system according to the embodiment aims to provide an individual learning plan according to the learning progress of students and improve the learning effect.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a learning progress analysis unit, a learning plan proposal unit, a quiz generation unit, a learning support unit, and a material customization unit. The learning progress analysis unit analyzes the student's learning progress. The learning plan proposal unit proposes an individual learning plan based on the results analyzed by the learning progress analysis unit. The quiz generation unit generates an interactive quiz or game based on the learning plan proposed by the learning plan proposal unit. The learning support unit supports learning through the quiz or game generated by the quiz generation unit. The material customization unit automatically customizes materials and resources based on the learning plan proposed by the learning plan proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide individualized learning plans tailored to each student's learning progress, thereby improving learning effectiveness. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system that analyzes a student's learning progress and areas of difficulty, and proposes an individualized learning plan. This system supports learning through interactive quizzes and games and has the function of automatically customizing learning materials and resources. For example, the learning support system has a learning progress analysis unit that analyzes a student's learning progress. This unit analyzes data from the student's learning history and test results to identify each student's strengths and weaknesses. For example, it identifies which areas a student struggles with based on past test results and homework submission status. Next, the learning support system has a learning plan proposal unit that proposes an individualized learning plan based on the results analyzed by the learning progress analysis unit. This unit proposes an optimal learning plan for each student based on the identified areas of difficulty. For example, for a student who struggles with mathematics, it proposes a step-by-step learning plan from basic to advanced mathematics. Furthermore, the learning support system has a quiz generation unit that generates interactive quizzes and games based on the learning plan proposed by the learning plan proposal unit. This unit generates quizzes and games based on the student's interests. For example, for a student interested in history, it generates quizzes and games related to history. There is also a learning support unit that supports learning through the generated quizzes and games. This section supports students in learning in an enjoyable way through generated quizzes and games. For example, it provides a system where students earn points for correctly answering quizzes, and receive rewards once a certain number of points are accumulated. Finally, the learning support system has a material customization section that automatically customizes teaching materials and resources based on the learning plan proposed by the learning plan proposal section. This section suggests appropriate teaching materials and resources based on the student's learning progress and level of understanding. For example, for a student who struggles with English reading, it suggests reading practice problems and reference books. This system enables individualized learning support tailored to each student, improving learning effectiveness. The learning support system also has a function that estimates the student's emotions and adjusts the method of analyzing learning progress based on the estimated emotions. For example, if a student is feeling stressed, it suggests a learning plan that helps them relax.This allows the learning support system to efficiently analyze students' learning progress and areas of difficulty, and propose individualized learning plans.

[0029] The learning support system according to this embodiment comprises a learning progress analysis unit, a learning plan proposal unit, a quiz generation unit, a learning support unit, and a teaching material customization unit. The learning progress analysis unit analyzes the learning progress of students. The learning progress analysis unit analyzes data such as students' learning history and test results to identify each student's strengths and weaknesses. For example, the learning progress analysis unit identifies which areas students struggle with based on past test results and homework submission status. The learning plan proposal unit proposes individual learning plans based on the results analyzed by the learning progress analysis unit. For example, the learning plan proposal unit proposes an optimal learning plan for each student based on the identified areas of difficulty. For example, the learning plan proposal unit proposes a step-by-step learning plan from basic to advanced mathematics for students who struggle with mathematics. The quiz generation unit generates interactive quizzes or games based on the learning plan proposed by the learning plan proposal unit. The quiz generation unit generates quizzes and games based on students' interests. For example, the quiz generation unit generates quizzes and games related to history for students who are interested in history. The Learning Support Unit supports learning through quizzes and games generated by the Quiz Generation Unit. For example, the Learning Support Unit helps students learn in an enjoyable way through the generated quizzes and games. For example, the Learning Support Unit provides a system where students earn points for correctly answering quizzes, and receive rewards once a certain number of points are accumulated. The Material Customization Unit automatically customizes materials and resources based on the learning plan proposed by the Learning Plan Proposal Unit. For example, the Material Customization Unit proposes appropriate materials and resources based on the student's learning progress and level of understanding. For example, the Material Customization Unit proposes reading practice problems and reference books for students who struggle with English reading. As a result, the learning support system according to the embodiment can efficiently analyze the student's learning progress and areas of difficulty, and propose individualized learning plans.

[0030] The Learning Progress Analysis Department analyzes students' learning progress in detail. This department collects data on students' learning history and test results, and analyzes this data using advanced algorithms. For example, it identifies each student's strengths and weaknesses based on past test results, homework submission status, and activity history on online learning platforms. Specifically, the Learning Progress Analysis Department analyzes in detail which areas students score highly and which areas they score low, visualizing their learning progress. It can also use AI to analyze students' learning patterns and speed, and predict future learning progress. For example, for a student who consistently scores low on math tests, it can identify a lack of understanding of specific mathematical concepts or problem types and analyze the cause. Furthermore, the Learning Progress Analysis Department also takes into account students' learning styles and learning environments, providing data to address individual learning needs. This allows the Learning Progress Analysis Department to gain a detailed understanding of each student's learning situation and provide a foundation for individualized learning support.

[0031] The Learning Plan Proposal Department proposes the most suitable learning plan for each student based on the analysis results obtained by the Learning Progress Analysis Department. This department takes into account identified areas of difficulty and strengths, and creates customized learning plans to address individual learning needs. For example, for a student who struggles with mathematics, it proposes a step-by-step learning plan from basic to advanced levels, and provides practice problems that focus on specific concepts and problem types. The Learning Plan Proposal Department also considers the student's learning style and environment to propose the most suitable learning method. For example, for a student who prefers visual learning, it proposes a learning plan that makes extensive use of visual aids and video materials, and for a student who prefers auditory learning, it proposes a learning plan that utilizes audio materials and podcasts. Furthermore, the Learning Plan Proposal Department can dynamically adjust the learning plan according to the student's learning progress. For example, for a student who has cleared a specific task, it proposes a new task to move on to the next step, and for a student who is falling behind, it proposes additional support and remedial lessons. In this way, the Learning Plan Proposal Department can provide each student with the most suitable learning plan and support effective learning.

[0032] The quiz generation unit generates interactive quizzes and games based on the learning plans proposed by the learning plan proposal unit. This unit creates customized quizzes and games according to the student's interests and learning needs. For example, it generates quizzes and games about history for students interested in history, and quizzes and games to reinforce basic mathematical concepts for students who struggle with math. The quiz generation unit uses AI to analyze students' learning history and interests and automatically generates the most suitable quiz and game content. For example, it generates quizzes that focus on questions students have answered incorrectly in the past or areas they struggle with, making learning enjoyable for students. The quiz generation unit can also dynamically adjust the difficulty and format of the quizzes. For example, it can increase the difficulty of the quizzes or provide different quiz formats according to the student's learning progress to maximize learning effectiveness. Furthermore, the quiz generation unit can collect student feedback and continuously improve the content and format of the quizzes. This allows the quiz generation unit to provide an optimal learning experience for each student and support effective learning.

[0033] The Learning Support Department supports students' learning through quizzes and games generated by the Quiz Generation Department. This department provides an interactive learning experience to make learning enjoyable for students. For example, it increases students' motivation to learn by providing a system where they earn points for correctly answering quizzes, and receive rewards once they accumulate a certain number of points. The Learning Support Department also provides real-time feedback to students as they learn through quizzes and games. For example, if a student answers a quiz incorrectly, it provides the correct answer and an explanation to help them deepen their understanding. Furthermore, the Learning Support Department monitors students' learning progress and provides additional support and resources as needed. For example, it provides additional practice problems and reference materials to students who struggle in a particular area to enhance the effectiveness of their learning. The Learning Support Department can also collect student feedback and continuously improve the learning experience. In this way, the Learning Support Department can provide optimal learning support to each student and support effective learning.

[0034] The curriculum customization department automatically customizes learning materials and resources based on the learning plan proposed by the learning plan proposal department. This customization department suggests the most suitable materials and resources according to the student's learning progress and level of understanding. For example, it suggests reading practice problems and reference books for students who struggle with English reading, and more advanced problem sets and reference books for students who excel at applied math problems. The curriculum customization department uses AI to analyze students' learning history and interests and automatically select the most suitable materials and resources. For example, it analyzes the effectiveness of materials and resources that students have used in the past and suggests the most effective materials. The curriculum customization department also considers the student's learning style and learning environment to provide the most suitable materials and resources. For example, it suggests materials that heavily utilize visual aids and video materials for students who prefer visual learning, and materials that utilize audio materials and podcasts for students who prefer auditory learning. Furthermore, the curriculum customization department can collect student feedback and continuously improve the content of the materials and resources. As a result, the curriculum customization department can provide the most suitable materials and resources for each student and support effective learning.

[0035] The learning progress analysis unit can analyze a student's past learning history in detail and extract specific learning patterns. For example, the learning progress analysis unit can analyze a student's past test results and extract learning patterns in specific subjects or topics. For example, the learning progress analysis unit can analyze a student's homework submission status and identify patterns of submission frequency and late submissions. For example, the learning progress analysis unit can analyze a student's online learning activities and extract patterns of study time and frequency. This allows for the extraction of specific learning patterns by analyzing past learning history in detail, enabling individualized learning support. Some or all of the above processes in the learning progress analysis unit may be performed using AI, for example, or not. For example, the learning progress analysis unit can input student learning history data into a generating AI and have the generating AI perform the extraction of learning patterns.

[0036] The learning progress analysis unit can monitor students' learning progress in real time and provide immediate feedback. For example, while a student is taking an online test, the learning progress analysis unit can monitor their answer status in real time and provide immediate feedback. For example, while a student is taking an online class, the learning progress analysis unit can monitor their learning progress in real time and provide feedback according to their level of understanding. For example, while a student is studying independently, the learning progress analysis unit can monitor their progress through a learning app and provide immediate advice. This improves learning effectiveness by monitoring learning progress in real time and providing immediate feedback. Some or all of the above processes in the learning progress analysis unit may be performed using AI, for example, or without AI. For example, the learning progress analysis unit can input learning data collected in real time into a generating AI and have the generating AI generate immediate feedback.

[0037] The learning progress analysis unit can improve the accuracy of its analysis by considering students' lifestyle data. For example, the learning progress analysis unit can consider students' sleep patterns and reflect them in the analysis results of their learning progress. For example, the learning progress analysis unit can consider students' eating habits and analyze their learning progress based on their energy levels. For example, the learning progress analysis unit can consider students' exercise habits and analyze their learning progress based on their physical condition. In this way, the accuracy of the learning progress analysis is improved by considering lifestyle data. Some or all of the above processing in the learning progress analysis unit may be performed using AI, for example, or without AI. For example, the learning progress analysis unit can input students' lifestyle data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0038] The Learning Progress Analysis Unit can evaluate students' learning motivation by referring to their social media activity. For example, the Learning Progress Analysis Unit can analyze students' social media posts to evaluate changes in their motivation to learn. For example, the Learning Progress Analysis Unit can analyze students' social media friendships to evaluate their impact on learning. For example, the Learning Progress Analysis Unit can analyze the frequency of students' social media activity to evaluate their level of concentration on learning. This allows for the evaluation of learning motivation and the provision of appropriate support by referring to social media activity. Some or all of the above processing in the Learning Progress Analysis Unit may be performed using AI, for example, or without AI. For example, the Learning Progress Analysis Unit can input students' social media data into a generating AI and have the generating AI perform the motivation evaluation.

[0039] The learning plan proposal unit can select the optimal plan by referring to the student's past learning achievements when proposing a learning plan. For example, the learning plan proposal unit can refer to the student's past test results and propose a learning plan that strengthens their weaknesses. For example, the learning plan proposal unit can refer to the student's homework submission status and propose a learning plan that improves the frequency of submissions. For example, the learning plan proposal unit can refer to the student's online learning activities and propose a plan that optimizes study time. In this way, the optimal learning plan can be proposed by referring to past learning achievements. Some or all of the above processing in the learning plan proposal unit may be performed using AI, for example, or without AI. For example, the learning plan proposal unit can input the student's learning achievement data into a generating AI and have the generating AI select the optimal plan.

[0040] The learning plan proposal unit can provide different plans depending on the student's learning style when proposing a learning plan. For example, the learning plan proposal unit may propose a learning plan that makes extensive use of visual materials to a visual learner. For example, the learning plan proposal unit may propose a learning plan that makes extensive use of audio materials to an auditory learner. For example, the learning plan proposal unit may propose a learning plan that includes practical activities to an experiential learner. By providing plans tailored to the learning style, learning effectiveness is improved. Some or all of the above processing in the learning plan proposal unit may be performed using AI, for example, or without AI. For example, the learning plan proposal unit can input student learning style data into a generating AI and have the generating AI perform the task of providing different plans.

[0041] The learning plan proposal unit can suggest optimal learning resources by considering the student's geographical location when proposing a learning plan. For example, if the student is at home, the learning plan proposal unit will suggest online resources that can be used at home. For example, if the student is at school, the learning plan proposal unit will suggest the school library or resource center. For example, if the student is on the move, the learning plan proposal unit will suggest resources that can be used on a mobile device. In this way, by considering geographical location information, the optimal learning resources can be suggested. Some or all of the above processing in the learning plan proposal unit may be performed using AI, for example, or without AI. For example, the learning plan proposal unit can input the student's geographical location data into a generating AI and have the generating AI perform the task of suggesting optimal resources.

[0042] The learning plan proposal unit can customize the content of a learning plan based on the student's interests and preferences when proposing a plan. For example, if a student is interested in science, the learning plan proposal unit will propose a plan that includes many science-related topics. For example, if a student is interested in history, the learning plan proposal unit will propose a plan that includes many history-related topics. For example, if a student is interested in art, the learning plan proposal unit will propose a plan that includes many art-related topics. By providing a plan based on interests and preferences, it is possible to increase motivation to learn. Some or all of the above processing in the learning plan proposal unit may be performed using AI, for example, or not using AI. For example, the learning plan proposal unit can input student interest data into a generating AI and have the generating AI perform the plan customization.

[0043] The quiz generation unit can select the most suitable questions by referring to the student's past quiz results when generating quizzes. For example, the quiz generation unit may re-present questions that the student answered incorrectly in the past. For example, the quiz generation unit may present many questions in areas where the student is strong. For example, the quiz generation unit may present many questions in areas where the student is weak. In this way, by referring to past quiz results, the optimal questions can be provided. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input the student's quiz result data into a generation AI and have the generation AI select the optimal questions.

[0044] The quiz generation unit can provide different quiz formats depending on the student's learning progress when generating quizzes. For example, if the student is a beginner, the quiz generation unit provides a multiple-choice quiz. For example, if the student is an intermediate learner, the quiz generation unit provides a written response quiz. For example, if the student is an advanced learner, the quiz generation unit provides a quiz that includes application problems. By providing quiz formats that match the learning progress, the learning effect is improved. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input student learning progress data into a generation AI and have the generation AI perform the task of providing different quiz formats.

[0045] The quiz generation unit can generate highly relevant questions by considering the student's geographical location information during quiz generation. For example, if a student is in a specific region, the quiz generation unit will present questions related to that region. For example, if a student is traveling, the quiz generation unit will present questions related to their travel destination. For example, if a student is at school, the quiz generation unit will present questions related to the school curriculum. In this way, by considering geographical location information, highly relevant questions can be provided. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input the student's geographical location data into a generation AI and have the generation AI perform the generation of highly relevant questions.

[0046] The quiz generation unit can generate engaging questions by referencing students' social media activity during quiz generation. For example, the quiz generation unit can ask questions related to topics that students have shown interest in on social media. For example, the quiz generation unit can ask questions related to accounts that students follow on social media. For example, the quiz generation unit can ask questions related to content that students have shared on social media. In this way, engaging questions can be provided by referencing social media activity. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input students' social media data into a generation AI and have the generation AI perform the generation of engaging questions.

[0047] The learning support unit can select the optimal support method by referring to the student's past learning history when providing learning support. For example, the learning support unit can refer to the student's past test results and provide support methods to reinforce weaknesses. For example, the learning support unit can refer to the student's homework submission status and provide support methods to improve submission frequency. For example, the learning support unit can refer to the student's online learning activities and provide support methods to optimize learning time. In this way, the optimal support method can be provided by referring to past learning history. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the student's learning history data into a generating AI and have the generating AI select the optimal support method.

[0048] The learning support unit can provide the optimal support method when providing learning support, taking into account the student's device information. For example, if the student is using a smartphone, the learning support unit will provide a mobile-optimized support method. For example, if the student is using a tablet, the learning support unit will provide a large-screen-optimized support method. For example, if the student is using a personal computer, the learning support unit will provide a desktop-optimized support method. In this way, the optimal support method can be provided by taking device information into account. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the student's device information into a generating AI and have the generating AI perform the task of providing the optimal support method.

[0049] The curriculum customization unit can select the most suitable curriculum materials by referring to students' past learning achievements during the customization process. For example, the curriculum customization unit can refer to students' past test results and provide materials that reinforce their weaknesses. For example, the curriculum customization unit can refer to students' homework submission status and provide materials that improve their submission frequency. For example, the curriculum customization unit can refer to students' online learning activities and provide materials that optimize their learning time. In this way, the system can provide the most suitable curriculum materials by referring to past learning achievements. Some or all of the above-described processes in the curriculum customization unit may be performed using AI, for example, or without AI. For example, the curriculum customization unit can input student learning achievement data into a generating AI and have the generating AI select the most suitable curriculum materials.

[0050] The material customization unit can provide different learning materials depending on the student's learning style during the customization process. For example, the material customization unit can provide visually-oriented materials to visual learners, audio-oriented materials to auditory learners, and experiential learners to materials that include practical activities. By providing materials tailored to each student's learning style, learning effectiveness is improved. Some or all of the above-described processes in the material customization unit may be performed using AI, for example, or without AI. For example, the material customization unit can input student learning style data into a generating AI and have the generating AI provide different learning materials.

[0051] The curriculum customization unit can suggest the most suitable curriculum materials by considering the student's geographical location during the customization process. For example, if a student is at home, the curriculum customization unit can suggest online materials that can be used at home. If a student is at school, the curriculum customization unit can suggest the school library or resource center. If a student is on the move, the curriculum customization unit can suggest materials that can be used on a mobile device. In this way, the most suitable curriculum materials can be provided by considering geographical location. Some or all of the above processing in the curriculum customization unit may be performed using AI, for example, or without AI. For example, the curriculum customization unit can input the student's geographical location data into a generating AI and have the generating AI suggest the most suitable curriculum materials.

[0052] The curriculum customization unit can customize the content of learning materials based on students' interests and preferences. For example, if a student is interested in science, the curriculum customization unit will provide science-related materials. For example, if a student is interested in history, the curriculum customization unit will provide history-related materials. For example, if a student is interested in art, the curriculum customization unit will provide art-related materials. By providing materials based on students' interests and preferences, it is possible to increase their motivation to learn. Some or all of the above-described processes in the curriculum customization unit may be performed using AI, for example, or without AI. For example, the curriculum customization unit can input student interest data into a generating AI and have the generating AI perform the customization of the learning materials.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The learning support system can also include a dashboard that visually displays students' learning progress. This dashboard displays students' learning progress and achievements in graphs and charts, making them easy to understand visually. For example, based on the results of the learning progress analysis unit, the progress of each subject can be displayed in a bar graph. Furthermore, the content of the learning plan proposal unit can be displayed in a timeline format, allowing students to quickly understand how they should proceed with their studies. Additionally, the results of quizzes generated by the quiz generation unit can be displayed in a pie chart, visually showing the correct answer rate and the trends in incorrect answers. This allows students to intuitively understand their learning situation and increase their motivation to learn.

[0055] The learning support system can also be equipped with an audio feedback unit that provides audio feedback on students' learning progress. This audio feedback unit provides auditory information as well as visual information by conveying the results of the learning progress analysis unit to the student via audio. For example, it can explain the suggestions from the learning plan proposal unit via audio and guide the student on how to proceed with their studies. It can also provide audio feedback on the results of quizzes generated by the quiz generation unit, telling the student whether their answer was correct or incorrect. Furthermore, it can provide audio support from the learning support unit, offering audio guides to help students relax. As a result, students can grasp their learning progress without relying on visual cues, thus improving learning effectiveness.

[0056] The learning support system can also include a comparative analysis unit that compares students' learning progress with that of other students. Based on the results from the learning progress analysis unit, this unit allows students to understand their position in comparison to other students in the same class or grade. For example, students can compare the content of the learning plan proposal unit with that of other students to see how far along their learning plan is. They can also compare the results of quizzes generated by the quiz generation unit with those of other students to compare their accuracy rate and the types of questions they answered incorrectly. Furthermore, they can compare the support provided by the learning support unit with that of other students to evaluate how effective their learning support is. This allows students to objectively understand their learning situation through comparison with other students and increase their motivation to learn.

[0057] The learning support system can also include a predictive analysis unit that forecasts students' learning progress. Based on the results of the learning progress analysis unit, this unit predicts future learning progress and suggests how students should proceed with their studies. For example, based on the suggestions from the learning plan proposal unit, it can predict future learning progress and indicate which subjects should be prioritized. It can also predict the difficulty level of future quizzes based on the results of quizzes generated by the quiz generation unit and provide quizzes of appropriate difficulty. Furthermore, based on the support provided by the learning support unit, it can predict future learning support methods and provide the most suitable support methods. This allows for more effective learning support by predicting future learning progress.

[0058] The learning support system can also include a report generation unit that reports on students' learning progress. This report generation unit periodically generates reports on learning progress based on the results of the learning progress analysis unit and provides them to students and their guardians. For example, it can generate monthly reports based on the suggestions from the learning plan proposal unit, reporting on learning progress and achievement levels. It can also generate weekly reports based on the results of quizzes generated by the quiz generation unit, reporting on the quiz accuracy rate and trends in incorrect answers. Furthermore, it can generate daily reports based on the support provided by the learning support unit, reporting on the effectiveness of learning support. This allows for monitoring learning progress through regular reports and reviewing learning plans.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The Learning Progress Analysis Department analyzes students' learning progress. For example, the Learning Progress Analysis Department analyzes data such as students' learning history and test results to identify each student's strengths and weaknesses. For instance, based on past test results and homework submission status, the Learning Progress Analysis Department identifies which areas students struggle with. Step 2: The Learning Plan Proposal Department proposes individual learning plans based on the results analyzed by the Learning Progress Analysis Department. For example, the Learning Plan Proposal Department proposes the most suitable learning plan for each student based on identified areas of difficulty. For instance, for a student who struggles with mathematics, the Learning Plan Proposal Department would propose a step-by-step learning plan covering mathematics from basic to advanced levels. Step 3: The quiz generation unit generates interactive quizzes or games based on the learning plan proposed by the learning plan proposal unit. The quiz generation unit generates quizzes or games based on the student's interests, for example. For example, for a student interested in history, the quiz generation unit will generate quizzes or games about history. Step 4: The learning support unit supports learning through quizzes and games generated by the quiz generation unit. For example, the learning support unit helps students learn in an enjoyable way through the generated quizzes and games. For instance, the learning support unit provides a system where students earn points for answering quizzes correctly, and receive rewards once they accumulate a certain number of points. Step 5: The Material Customization Department automatically customizes materials and resources based on the learning plan proposed by the Learning Plan Proposal Department. For example, the Material Customization Department suggests appropriate materials and resources based on the student's learning progress and level of understanding. For instance, for a student who struggles with English reading, the Material Customization Department will suggest reading practice exercises and reference books.

[0061] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that analyzes a student's learning progress and areas of difficulty, and proposes an individualized learning plan. This system supports learning through interactive quizzes and games and has the function of automatically customizing learning materials and resources. For example, the learning support system has a learning progress analysis unit that analyzes a student's learning progress. This unit analyzes data from the student's learning history and test results to identify each student's strengths and weaknesses. For example, it identifies which areas a student struggles with based on past test results and homework submission status. Next, the learning support system has a learning plan proposal unit that proposes an individualized learning plan based on the results analyzed by the learning progress analysis unit. This unit proposes an optimal learning plan for each student based on the identified areas of difficulty. For example, for a student who struggles with mathematics, it proposes a step-by-step learning plan from basic to advanced mathematics. Furthermore, the learning support system has a quiz generation unit that generates interactive quizzes and games based on the learning plan proposed by the learning plan proposal unit. This unit generates quizzes and games based on the student's interests. For example, for a student interested in history, it generates quizzes and games related to history. There is also a learning support unit that supports learning through the generated quizzes and games. This section supports students in learning in an enjoyable way through generated quizzes and games. For example, it provides a system where students earn points for correctly answering quizzes, and receive rewards once a certain number of points are accumulated. Finally, the learning support system has a material customization section that automatically customizes teaching materials and resources based on the learning plan proposed by the learning plan proposal section. This section suggests appropriate teaching materials and resources based on the student's learning progress and level of understanding. For example, for a student who struggles with English reading, it suggests reading practice problems and reference books. This system enables individualized learning support tailored to each student, improving learning effectiveness. The learning support system also has a function that estimates the student's emotions and adjusts the method of analyzing learning progress based on the estimated emotions. For example, if a student is feeling stressed, it suggests a learning plan that helps them relax.This allows the learning support system to efficiently analyze students' learning progress and areas of difficulty, and propose individualized learning plans.

[0062] The learning support system according to this embodiment comprises a learning progress analysis unit, a learning plan proposal unit, a quiz generation unit, a learning support unit, and a teaching material customization unit. The learning progress analysis unit analyzes the learning progress of students. The learning progress analysis unit analyzes data such as students' learning history and test results to identify each student's strengths and weaknesses. For example, the learning progress analysis unit identifies which areas students struggle with based on past test results and homework submission status. The learning plan proposal unit proposes individual learning plans based on the results analyzed by the learning progress analysis unit. For example, the learning plan proposal unit proposes an optimal learning plan for each student based on the identified areas of difficulty. For example, the learning plan proposal unit proposes a step-by-step learning plan from basic to advanced mathematics for students who struggle with mathematics. The quiz generation unit generates interactive quizzes or games based on the learning plan proposed by the learning plan proposal unit. The quiz generation unit generates quizzes and games based on students' interests. For example, the quiz generation unit generates quizzes and games related to history for students who are interested in history. The Learning Support Unit supports learning through quizzes and games generated by the Quiz Generation Unit. For example, the Learning Support Unit helps students learn in an enjoyable way through the generated quizzes and games. For example, the Learning Support Unit provides a system where students earn points for correctly answering quizzes, and receive rewards once a certain number of points are accumulated. The Material Customization Unit automatically customizes materials and resources based on the learning plan proposed by the Learning Plan Proposal Unit. For example, the Material Customization Unit proposes appropriate materials and resources based on the student's learning progress and level of understanding. For example, the Material Customization Unit proposes reading practice problems and reference books for students who struggle with English reading. As a result, the learning support system according to the embodiment can efficiently analyze the student's learning progress and areas of difficulty, and propose individualized learning plans.

[0063] The Learning Progress Analysis Department analyzes students' learning progress in detail. This department collects data on students' learning history and test results, and analyzes this data using advanced algorithms. For example, it identifies each student's strengths and weaknesses based on past test results, homework submission status, and activity history on online learning platforms. Specifically, the Learning Progress Analysis Department analyzes in detail which areas students score highly and which areas they score low, visualizing their learning progress. It can also use AI to analyze students' learning patterns and speed, and predict future learning progress. For example, for a student who consistently scores low on math tests, it can identify a lack of understanding of specific mathematical concepts or problem types and analyze the cause. Furthermore, the Learning Progress Analysis Department also takes into account students' learning styles and learning environments, providing data to address individual learning needs. This allows the Learning Progress Analysis Department to gain a detailed understanding of each student's learning situation and provide a foundation for individualized learning support.

[0064] The Learning Plan Proposal Department proposes the most suitable learning plan for each student based on the analysis results obtained by the Learning Progress Analysis Department. This department takes into account identified areas of difficulty and strengths, and creates customized learning plans to address individual learning needs. For example, for a student who struggles with mathematics, it proposes a step-by-step learning plan from basic to advanced levels, and provides practice problems that focus on specific concepts and problem types. The Learning Plan Proposal Department also considers the student's learning style and environment to propose the most suitable learning method. For example, for a student who prefers visual learning, it proposes a learning plan that makes extensive use of visual aids and video materials, and for a student who prefers auditory learning, it proposes a learning plan that utilizes audio materials and podcasts. Furthermore, the Learning Plan Proposal Department can dynamically adjust the learning plan according to the student's learning progress. For example, for a student who has cleared a specific task, it proposes a new task to move on to the next step, and for a student who is falling behind, it proposes additional support and remedial lessons. In this way, the Learning Plan Proposal Department can provide each student with the most suitable learning plan and support effective learning.

[0065] The quiz generation unit generates interactive quizzes and games based on the learning plans proposed by the learning plan proposal unit. This unit creates customized quizzes and games according to the student's interests and learning needs. For example, it generates quizzes and games about history for students interested in history, and quizzes and games to reinforce basic mathematical concepts for students who struggle with math. The quiz generation unit uses AI to analyze students' learning history and interests and automatically generates the most suitable quiz and game content. For example, it generates quizzes that focus on questions students have answered incorrectly in the past or areas they struggle with, making learning enjoyable for students. The quiz generation unit can also dynamically adjust the difficulty and format of the quizzes. For example, it can increase the difficulty of the quizzes or provide different quiz formats according to the student's learning progress to maximize learning effectiveness. Furthermore, the quiz generation unit can collect student feedback and continuously improve the content and format of the quizzes. This allows the quiz generation unit to provide an optimal learning experience for each student and support effective learning.

[0066] The Learning Support Department supports students' learning through quizzes and games generated by the Quiz Generation Department. This department provides an interactive learning experience to make learning enjoyable for students. For example, it increases students' motivation to learn by providing a system where they earn points for correctly answering quizzes, and receive rewards once they accumulate a certain number of points. The Learning Support Department also provides real-time feedback to students as they learn through quizzes and games. For example, if a student answers a quiz incorrectly, it provides the correct answer and an explanation to help them deepen their understanding. Furthermore, the Learning Support Department monitors students' learning progress and provides additional support and resources as needed. For example, it provides additional practice problems and reference materials to students who struggle in a particular area to enhance the effectiveness of their learning. The Learning Support Department can also collect student feedback and continuously improve the learning experience. In this way, the Learning Support Department can provide optimal learning support to each student and support effective learning.

[0067] The curriculum customization department automatically customizes learning materials and resources based on the learning plan proposed by the learning plan proposal department. This customization department suggests the most suitable materials and resources according to the student's learning progress and level of understanding. For example, it suggests reading practice problems and reference books for students who struggle with English reading, and more advanced problem sets and reference books for students who excel at applied math problems. The curriculum customization department uses AI to analyze students' learning history and interests and automatically select the most suitable materials and resources. For example, it analyzes the effectiveness of materials and resources that students have used in the past and suggests the most effective materials. The curriculum customization department also considers the student's learning style and learning environment to provide the most suitable materials and resources. For example, it suggests materials that heavily utilize visual aids and video materials for students who prefer visual learning, and materials that utilize audio materials and podcasts for students who prefer auditory learning. Furthermore, the curriculum customization department can collect student feedback and continuously improve the content of the materials and resources. As a result, the curriculum customization department can provide the most suitable materials and resources for each student and support effective learning.

[0068] The learning progress analysis unit can estimate a student's emotions and adjust the learning progress analysis method based on the estimated emotions. For example, if a student is stressed, the learning progress analysis unit will adopt a learning progress analysis method that promotes relaxation. For example, if a student is focused, the learning progress analysis unit will perform a detailed analysis and provide specific feedback. For example, if a student is tired, the learning progress analysis unit will use a simplified analysis method to reduce the burden. By adjusting the learning progress analysis method according to the student's emotions, more appropriate learning support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning progress analysis unit may be performed using AI, for example, or without AI. For example, the learning progress analysis unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0069] The learning progress analysis unit can analyze a student's past learning history in detail and extract specific learning patterns. For example, the learning progress analysis unit can analyze a student's past test results and extract learning patterns in specific subjects or topics. For example, the learning progress analysis unit can analyze a student's homework submission status and identify patterns of submission frequency and late submissions. For example, the learning progress analysis unit can analyze a student's online learning activities and extract patterns of study time and frequency. This allows for the extraction of specific learning patterns by analyzing past learning history in detail, enabling individualized learning support. Some or all of the above processes in the learning progress analysis unit may be performed using AI, for example, or not. For example, the learning progress analysis unit can input student learning history data into a generating AI and have the generating AI perform the extraction of learning patterns.

[0070] The learning progress analysis unit can monitor students' learning progress in real time and provide immediate feedback. For example, while a student is taking an online test, the learning progress analysis unit can monitor their answer status in real time and provide immediate feedback. For example, while a student is taking an online class, the learning progress analysis unit can monitor their learning progress in real time and provide feedback according to their level of understanding. For example, while a student is studying independently, the learning progress analysis unit can monitor their progress through a learning app and provide immediate advice. This improves learning effectiveness by monitoring learning progress in real time and providing immediate feedback. Some or all of the above processes in the learning progress analysis unit may be performed using AI, for example, or without AI. For example, the learning progress analysis unit can input learning data collected in real time into a generating AI and have the generating AI generate immediate feedback.

[0071] The learning progress analysis unit can estimate a student's emotions and adjust the order in which it displays the learning progress analysis results based on the estimated emotions. For example, if a student is stressed, the learning progress analysis unit will display positive results first to increase motivation. If a student is relaxed, the learning progress analysis unit will display detailed analysis results in a sequential manner. If a student is in a hurry, the learning progress analysis unit will display important results first to provide information efficiently. In this way, by adjusting the display order of analysis results according to the student's emotions, learning motivation can be increased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning progress analysis unit may be performed using AI, for example, or without AI. For example, the learning progress analysis unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The learning progress analysis unit can improve the accuracy of its analysis by considering students' lifestyle data. For example, the learning progress analysis unit can consider students' sleep patterns and reflect them in the analysis results of their learning progress. For example, the learning progress analysis unit can consider students' eating habits and analyze their learning progress based on their energy levels. For example, the learning progress analysis unit can consider students' exercise habits and analyze their learning progress based on their physical condition. In this way, the accuracy of the learning progress analysis is improved by considering lifestyle data. Some or all of the above processing in the learning progress analysis unit may be performed using AI, for example, or without AI. For example, the learning progress analysis unit can input students' lifestyle data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0073] The Learning Progress Analysis Unit can evaluate students' learning motivation by referring to their social media activity. For example, the Learning Progress Analysis Unit can analyze students' social media posts to evaluate changes in their motivation to learn. For example, the Learning Progress Analysis Unit can analyze students' social media friendships to evaluate their impact on learning. For example, the Learning Progress Analysis Unit can analyze the frequency of students' social media activity to evaluate their level of concentration on learning. This allows for the evaluation of learning motivation and the provision of appropriate support by referring to social media activity. Some or all of the above processing in the Learning Progress Analysis Unit may be performed using AI, for example, or without AI. For example, the Learning Progress Analysis Unit can input students' social media data into a generating AI and have the generating AI perform the motivation evaluation.

[0074] The learning plan suggestion unit can estimate a student's emotions and adjust the content of the learning plan based on the estimated emotions. For example, if a student is feeling stressed, the learning plan suggestion unit will suggest a relaxing learning plan. For example, if a student is concentrating, the learning plan suggestion unit will suggest a challenging learning plan. For example, if a student is tired, the learning plan suggestion unit will suggest a learning plan that includes breaks. By adjusting the content of the learning plan according to the student's emotions, more effective learning support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning plan suggestion unit may be performed using AI, for example, or without AI. For example, the learning plan suggestion unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The learning plan proposal unit can select the optimal plan by referring to the student's past learning achievements when proposing a learning plan. For example, the learning plan proposal unit can refer to the student's past test results and propose a learning plan that strengthens their weaknesses. For example, the learning plan proposal unit can refer to the student's homework submission status and propose a learning plan that improves the frequency of submissions. For example, the learning plan proposal unit can refer to the student's online learning activities and propose a plan that optimizes study time. In this way, the optimal learning plan can be proposed by referring to past learning achievements. Some or all of the above processing in the learning plan proposal unit may be performed using AI, for example, or without AI. For example, the learning plan proposal unit can input the student's learning achievement data into a generating AI and have the generating AI select the optimal plan.

[0076] The learning plan proposal unit can provide different plans depending on the student's learning style when proposing a learning plan. For example, the learning plan proposal unit may propose a learning plan that makes extensive use of visual materials to a visual learner. For example, the learning plan proposal unit may propose a learning plan that makes extensive use of audio materials to an auditory learner. For example, the learning plan proposal unit may propose a learning plan that includes practical activities to an experiential learner. By providing plans tailored to the learning style, learning effectiveness is improved. Some or all of the above processing in the learning plan proposal unit may be performed using AI, for example, or without AI. For example, the learning plan proposal unit can input student learning style data into a generating AI and have the generating AI perform the task of providing different plans.

[0077] The learning plan suggestion unit can estimate a student's emotions and prioritize learning plans based on those emotions. For example, if a student is stressed, the learning plan suggestion unit will prioritize relaxing tasks. If a student is focused, the learning plan suggestion unit will prioritize more difficult tasks. If a student is tired, the learning plan suggestion unit will prioritize easier tasks. By prioritizing learning plans according to a student's emotions, it is possible to increase their motivation to learn. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning plan suggestion unit may be performed using AI or not. For example, the learning plan suggestion unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The learning plan proposal unit can suggest optimal learning resources by considering the student's geographical location when proposing a learning plan. For example, if the student is at home, the learning plan proposal unit will suggest online resources that can be used at home. For example, if the student is at school, the learning plan proposal unit will suggest the school library or resource center. For example, if the student is on the move, the learning plan proposal unit will suggest resources that can be used on a mobile device. In this way, by considering geographical location information, the optimal learning resources can be suggested. Some or all of the above processing in the learning plan proposal unit may be performed using AI, for example, or without AI. For example, the learning plan proposal unit can input the student's geographical location data into a generating AI and have the generating AI perform the task of suggesting optimal resources.

[0079] The learning plan proposal unit can customize the content of a learning plan based on the student's interests and preferences when proposing a plan. For example, if a student is interested in science, the learning plan proposal unit will propose a plan that includes many science-related topics. For example, if a student is interested in history, the learning plan proposal unit will propose a plan that includes many history-related topics. For example, if a student is interested in art, the learning plan proposal unit will propose a plan that includes many art-related topics. By providing a plan based on interests and preferences, it is possible to increase motivation to learn. Some or all of the above processing in the learning plan proposal unit may be performed using AI, for example, or not using AI. For example, the learning plan proposal unit can input student interest data into a generating AI and have the generating AI perform the plan customization.

[0080] The quiz generation unit can estimate the student's emotions and adjust the difficulty of the quiz based on the estimated emotions. For example, if a student is stressed, the quiz generation unit provides an easy quiz. For example, if a student is focused, the quiz generation unit provides a difficult quiz. For example, if a student is relaxed, the quiz generation unit provides a quiz of moderate difficulty. By adjusting the difficulty of the quiz according to the student's emotions, the learning effect is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input student facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0081] The quiz generation unit can select the most suitable questions by referring to the student's past quiz results when generating quizzes. For example, the quiz generation unit may re-present questions that the student answered incorrectly in the past. For example, the quiz generation unit may present many questions in areas where the student is strong. For example, the quiz generation unit may present many questions in areas where the student is weak. In this way, by referring to past quiz results, the optimal questions can be provided. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input the student's quiz result data into a generation AI and have the generation AI select the optimal questions.

[0082] The quiz generation unit can provide different quiz formats depending on the student's learning progress when generating quizzes. For example, if the student is a beginner, the quiz generation unit provides a multiple-choice quiz. For example, if the student is an intermediate learner, the quiz generation unit provides a written response quiz. For example, if the student is an advanced learner, the quiz generation unit provides a quiz that includes application problems. By providing quiz formats that match the learning progress, the learning effect is improved. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input student learning progress data into a generation AI and have the generation AI perform the task of providing different quiz formats.

[0083] The quiz generation unit can estimate the student's emotions and adjust the order of quiz questions based on the estimated emotions. For example, if the student is stressed, the quiz generation unit will present easy questions first. If the student is focused, the quiz generation unit will present difficult questions first. If the student is relaxed, the quiz generation unit will present questions in a random order. By adjusting the order of quiz questions according to the student's emotions, it is possible to increase their motivation to learn. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the quiz generation unit may be performed using AI, or not using AI. For example, the quiz generation unit can input student facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The quiz generation unit can generate highly relevant questions by considering the student's geographical location information during quiz generation. For example, if a student is in a specific region, the quiz generation unit will present questions related to that region. For example, if a student is traveling, the quiz generation unit will present questions related to their travel destination. For example, if a student is at school, the quiz generation unit will present questions related to the school curriculum. In this way, by considering geographical location information, highly relevant questions can be provided. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input the student's geographical location data into a generation AI and have the generation AI perform the generation of highly relevant questions.

[0085] The quiz generation unit can generate engaging questions by referencing students' social media activity during quiz generation. For example, the quiz generation unit can ask questions related to topics that students have shown interest in on social media. For example, the quiz generation unit can ask questions related to accounts that students follow on social media. For example, the quiz generation unit can ask questions related to content that students have shared on social media. In this way, engaging questions can be provided by referencing social media activity. Some or all of the above processing in the quiz generation unit may be performed using AI, for example, or without AI. For example, the quiz generation unit can input students' social media data into a generation AI and have the generation AI perform the generation of engaging questions.

[0086] The learning support unit can estimate a student's emotions and adjust its learning support methods based on those emotions. For example, if a student is stressed, the learning support unit can provide a relaxing support method. For example, if a student is focused, the learning support unit can provide a challenging support method. For example, if a student is tired, the learning support unit can provide a support method that includes breaks. By adjusting the learning support method according to the student's emotions, learning effectiveness is improved. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The learning support unit can select the optimal support method by referring to the student's past learning history when providing learning support. For example, the learning support unit can refer to the student's past test results and provide support methods to reinforce weaknesses. For example, the learning support unit can refer to the student's homework submission status and provide support methods to improve submission frequency. For example, the learning support unit can refer to the student's online learning activities and provide support methods to optimize learning time. In this way, the optimal support method can be provided by referring to past learning history. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the student's learning history data into a generating AI and have the generating AI select the optimal support method.

[0088] The learning support unit can estimate a student's emotions and prioritize learning support based on those emotions. For example, if a student is stressed, the learning support unit will prioritize relaxing support. If a student is focused, the learning support unit will prioritize challenging support. If a student is tired, the learning support unit will prioritize easy support. By prioritizing learning support according to the student's emotions, it is possible to increase their motivation to learn. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning support unit may be performed using AI or not. For example, the learning support unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The learning support unit can provide the optimal support method when providing learning support, taking into account the student's device information. For example, if the student is using a smartphone, the learning support unit will provide a mobile-optimized support method. For example, if the student is using a tablet, the learning support unit will provide a large-screen-optimized support method. For example, if the student is using a personal computer, the learning support unit will provide a desktop-optimized support method. In this way, the optimal support method can be provided by taking device information into account. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the student's device information into a generating AI and have the generating AI perform the task of providing the optimal support method.

[0090] The material customization unit can estimate students' emotions and adjust the content of the materials based on the estimated emotions. For example, if a student is feeling stressed, the material customization unit can provide relaxing materials. For example, if a student is concentrating, the material customization unit can provide challenging materials. For example, if a student is tired, the material customization unit can provide easy materials. By adjusting the content of the materials according to the student's emotions, the learning effect is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the material customization unit may be performed using AI, for example, or without AI. For example, the material customization unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The curriculum customization unit can select the most suitable curriculum materials by referring to students' past learning achievements during the customization process. For example, the curriculum customization unit can refer to students' past test results and provide materials that reinforce their weaknesses. For example, the curriculum customization unit can refer to students' homework submission status and provide materials that improve their submission frequency. For example, the curriculum customization unit can refer to students' online learning activities and provide materials that optimize their learning time. In this way, the system can provide the most suitable curriculum materials by referring to past learning achievements. Some or all of the above-described processes in the curriculum customization unit may be performed using AI, for example, or without AI. For example, the curriculum customization unit can input student learning achievement data into a generating AI and have the generating AI select the most suitable curriculum materials.

[0092] The material customization unit can provide different learning materials depending on the student's learning style during the customization process. For example, the material customization unit can provide visually-oriented materials to visual learners, audio-oriented materials to auditory learners, and experiential learners to materials that include practical activities. By providing materials tailored to each student's learning style, learning effectiveness is improved. Some or all of the above-described processes in the material customization unit may be performed using AI, for example, or without AI. For example, the material customization unit can input student learning style data into a generating AI and have the generating AI provide different learning materials.

[0093] The material customization unit can estimate students' emotions and prioritize materials based on those emotions. For example, if a student is stressed, the material customization unit will prioritize relaxing materials. If a student is focused, the material customization unit will prioritize challenging materials. If a student is tired, the material customization unit will prioritize easy materials. By prioritizing materials according to students' emotions, it is possible to increase their motivation to learn. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the material customization unit may be performed using AI or not using AI. For example, the material customization unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The curriculum customization unit can suggest the most suitable curriculum materials by considering the student's geographical location during the customization process. For example, if a student is at home, the curriculum customization unit can suggest online materials that can be used at home. If a student is at school, the curriculum customization unit can suggest the school library or resource center. If a student is on the move, the curriculum customization unit can suggest materials that can be used on a mobile device. In this way, the most suitable curriculum materials can be provided by considering geographical location. Some or all of the above processing in the curriculum customization unit may be performed using AI, for example, or without AI. For example, the curriculum customization unit can input the student's geographical location data into a generating AI and have the generating AI suggest the most suitable curriculum materials.

[0095] The curriculum customization unit can customize the content of learning materials based on students' interests and preferences. For example, if a student is interested in science, the curriculum customization unit will provide science-related materials. For example, if a student is interested in history, the curriculum customization unit will provide history-related materials. For example, if a student is interested in art, the curriculum customization unit will provide art-related materials. By providing materials based on students' interests and preferences, it is possible to increase their motivation to learn. Some or all of the above-described processes in the curriculum customization unit may be performed using AI, for example, or without AI. For example, the curriculum customization unit can input student interest data into a generating AI and have the generating AI perform the customization of the learning materials.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The learning support system can also include a dashboard that visually displays students' learning progress. This dashboard displays students' learning progress and achievements in graphs and charts, making them easy to understand visually. For example, based on the results of the learning progress analysis unit, the progress of each subject can be displayed in a bar graph. Furthermore, the content of the learning plan proposal unit can be displayed in a timeline format, allowing students to quickly understand how they should proceed with their studies. Additionally, the results of quizzes generated by the quiz generation unit can be displayed in a pie chart, visually showing the correct answer rate and the trends in incorrect answers. This allows students to intuitively understand their learning situation and increase their motivation to learn.

[0098] The learning support system can also be equipped with an audio feedback unit that provides audio feedback on students' learning progress. This audio feedback unit provides auditory information as well as visual information by conveying the results of the learning progress analysis unit to the student via audio. For example, it can explain the suggestions from the learning plan proposal unit via audio and guide the student on how to proceed with their studies. It can also provide audio feedback on the results of quizzes generated by the quiz generation unit, telling the student whether their answer was correct or incorrect. Furthermore, it can provide audio support from the learning support unit, offering audio guides to help students relax. As a result, students can grasp their learning progress without relying on visual cues, thus improving learning effectiveness.

[0099] The learning support system can also include a comparative analysis unit that compares students' learning progress with that of other students. Based on the results from the learning progress analysis unit, this unit allows students to understand their position in comparison to other students in the same class or grade. For example, students can compare the content of the learning plan proposal unit with that of other students to see how far along their learning plan is. They can also compare the results of quizzes generated by the quiz generation unit with those of other students to compare their accuracy rate and the types of questions they answered incorrectly. Furthermore, they can compare the support provided by the learning support unit with that of other students to evaluate how effective their learning support is. This allows students to objectively understand their learning situation through comparison with other students and increase their motivation to learn.

[0100] The learning support system can also include a predictive analysis unit that forecasts students' learning progress. Based on the results of the learning progress analysis unit, this unit predicts future learning progress and suggests how students should proceed with their studies. For example, based on the suggestions from the learning plan proposal unit, it can predict future learning progress and indicate which subjects should be prioritized. It can also predict the difficulty level of future quizzes based on the results of quizzes generated by the quiz generation unit and provide quizzes of appropriate difficulty. Furthermore, based on the support provided by the learning support unit, it can predict future learning support methods and provide the most suitable support methods. This allows for more effective learning support by predicting future learning progress.

[0101] The learning support system can also include a report generation unit that reports on students' learning progress. This report generation unit periodically generates reports on learning progress based on the results of the learning progress analysis unit and provides them to students and their guardians. For example, it can generate monthly reports based on the suggestions from the learning plan proposal unit, reporting on learning progress and achievement levels. It can also generate weekly reports based on the results of quizzes generated by the quiz generation unit, reporting on the quiz accuracy rate and trends in incorrect answers. Furthermore, it can generate daily reports based on the support provided by the learning support unit, reporting on the effectiveness of learning support. This allows for monitoring learning progress through regular reports and reviewing learning plans.

[0102] The learning support system may also include an environment adjustment unit that estimates the student's emotions and adjusts the learning environment based on those emotions. This environment adjustment unit optimizes the learning environment based on the results of the learning progress analysis unit and the emotion estimation results. For example, if a student is feeling stressed, relaxing music can be played. If a student is concentrating, a quiet environment can be provided to help maintain their concentration. Furthermore, if a student is tired, a reminder can be displayed to encourage them to take a break. By adjusting the learning environment according to the student's emotions, learning effectiveness can be improved.

[0103] The learning support system may also include a goal-setting unit that estimates the student's emotions and sets learning goals based on those estimated emotions. This goal-setting unit sets appropriate learning goals for the student based on the results of the learning progress analysis unit and the emotion estimation results. For example, if a student is feeling stressed, short-term goals can be set to make it easier for them to feel a sense of accomplishment. If a student is focused, long-term goals can be set to allow them to tackle challenging tasks. Furthermore, if a student is tired, goals that include breaks can be set to allow them to learn at a comfortable pace. In this way, by setting learning goals according to the student's emotions, their motivation to learn can be increased.

[0104] The learning support system can also include a resource recommendation unit that estimates the student's emotions and recommends learning resources based on those emotions. This resource recommendation unit recommends learning resources suitable for the student based on the results of the learning progress analysis unit and the emotion estimation results. For example, if a student is feeling stressed, it can recommend relaxing videos or music. If a student is focused, it can recommend challenging textbooks or reference books. Furthermore, if a student is tired, it can recommend easy practice problems or light reading material. This improves learning effectiveness by providing appropriate learning resources according to the student's emotions.

[0105] The learning support system may also include a feedback adjustment unit that estimates the student's emotions and adjusts the feedback on learning progress based on those emotions. This feedback adjustment unit provides appropriate feedback to the student based on the results of the learning progress analysis unit and the emotion estimation results. For example, if a student is feeling stressed, positive feedback can be prioritized. If a student is focused, detailed feedback and specific areas for improvement can be provided. Furthermore, if a student is tired, simplified feedback can be provided to reduce their burden. By adjusting feedback according to the student's emotions, learning motivation can be enhanced.

[0106] The learning support system may also include a progress adjustment unit that estimates the student's emotions and adjusts the progress of the learning plan based on the estimated emotions. This progress adjustment unit optimizes the progress of the learning plan based on the results of the learning progress analysis unit and the emotion estimation results. For example, if a student is feeling stressed, the progress of the learning plan can be slowed down to increase the time they can relax. Conversely, if a student is concentrating, the progress of the learning plan can be sped up to allow for more efficient learning. Furthermore, if a student is tired, breaks can be added to the learning plan to allow for learning at a comfortable pace. In this way, the learning effect is improved by adjusting the progress of the learning plan according to the student's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The Learning Progress Analysis Department analyzes students' learning progress. For example, the Learning Progress Analysis Department analyzes data such as students' learning history and test results to identify each student's strengths and weaknesses. For instance, based on past test results and homework submission status, the Learning Progress Analysis Department identifies which areas students struggle with. Step 2: The Learning Plan Proposal Department proposes individual learning plans based on the results analyzed by the Learning Progress Analysis Department. For example, the Learning Plan Proposal Department proposes the most suitable learning plan for each student based on identified areas of difficulty. For instance, for a student who struggles with mathematics, the Learning Plan Proposal Department would propose a step-by-step learning plan covering mathematics from basic to advanced levels. Step 3: The quiz generation unit generates interactive quizzes or games based on the learning plan proposed by the learning plan proposal unit. The quiz generation unit generates quizzes or games based on the student's interests, for example. For example, for a student interested in history, the quiz generation unit will generate quizzes or games about history. Step 4: The learning support unit supports learning through quizzes and games generated by the quiz generation unit. For example, the learning support unit helps students learn in an enjoyable way through the generated quizzes and games. For instance, the learning support unit provides a system where students earn points for answering quizzes correctly, and receive rewards once they accumulate a certain number of points. Step 5: The Material Customization Department automatically customizes materials and resources based on the learning plan proposed by the Learning Plan Proposal Department. For example, the Material Customization Department suggests appropriate materials and resources based on the student's learning progress and level of understanding. For instance, for a student who struggles with English reading, the Material Customization Department will suggest reading practice exercises and reference books.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] For example, the learning progress analysis unit uses the camera 42 and microphone 38B of the smart device 14 to detect the student's learning status, and the control unit 46A can analyze learning history and test result data. The learning plan proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes an individualized learning plan based on the analyzed data. The quiz generation unit is implemented by the control unit 46A of the smart device 14 and generates quizzes and games based on the student's interests. The learning support unit supports learning through the quizzes and games generated using the output device 40 of the smart device 14. The teaching material customization unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes appropriate teaching materials and resources based on the student's learning progress and level of understanding. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] For example, the learning progress analysis unit uses the camera 42 and microphone 238 of the smart glasses 214 to detect the student's learning status, and the control unit 46A can analyze learning history and test result data. The learning plan proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes an individualized learning plan based on the analyzed data. The quiz generation unit is implemented by the control unit 46A of the smart glasses 214 and generates quizzes and games based on the student's interests. The learning support unit supports learning through the quizzes and games generated using the speaker 240 of the smart glasses 214. The material customization unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes appropriate materials and resources based on the student's learning progress and level of understanding. The correspondence between each unit and the device and control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] For example, the learning progress analysis unit uses the camera 42 and microphone 238 of the headset terminal 314 to detect the student's learning status, and the control unit 46A can analyze learning history and test result data. The learning plan proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes individual learning plans based on the analyzed data. The quiz generation unit is implemented by the control unit 46A of the headset terminal 314 and generates quizzes and games based on the student's interests. The learning support unit supports learning through the generated quizzes and games using the display 343 of the headset terminal 314. The teaching material customization unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes appropriate teaching materials and resources based on the student's learning progress and level of understanding. The correspondence between each unit and the device and control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] For example, the learning progress analysis unit uses the camera 42 and microphone 238 of the robot 414 to detect the student's learning status, and the control unit 46A can analyze learning history and test result data. The learning plan proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes an individual learning plan based on the analyzed data. The quiz generation unit is implemented by the control unit 46A of the robot 414 and generates quizzes and games based on the student's interests. The learning support unit supports learning through the quizzes and games generated using the speaker 240 of the robot 414. The teaching material customization unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes appropriate teaching materials and resources based on the student's learning progress and level of understanding. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The Learning Progress Analysis Department analyzes students' learning progress, A learning plan proposal unit proposes individual learning plans based on the results analyzed by the aforementioned learning progress analysis unit, A quiz generation unit generates an interactive quiz or game based on the learning plan proposed by the aforementioned learning plan proposal unit, A learning support unit that supports learning through quizzes or games generated by the quiz generation unit, The system includes a learning material customization unit that automatically customizes learning materials and resources based on the learning plan proposed by the learning plan proposal unit. A system characterized by the following features. (Note 2) The aforementioned learning progress analysis unit, We estimate students' emotions and adjust the analysis method of learning progress based on the estimated students' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning progress analysis unit, By analyzing students' past learning history in detail, specific learning patterns can be extracted. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning progress analysis unit, Monitor students' learning progress in real time and provide immediate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning progress analysis unit, The system estimates students' emotions and adjusts the order in which it displays the analysis results of their learning progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning progress analysis unit, Improving analytical accuracy based on student lifestyle data The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning progress analysis unit, Evaluate students' motivation to learn by referring to their social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning plan proposal unit, The system estimates students' emotions and adjusts the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning plan proposal unit, When proposing a learning plan, select an appropriate plan by referring to the student's past learning achievements. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning plan proposal unit, When proposing a learning plan, we offer different plans tailored to each student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning plan proposal unit, The system estimates students' emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning plan proposal unit, When proposing a learning plan, appropriate learning resources are suggested based on the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning plan proposal unit, When proposing a learning plan, customize the plan's content based on the student's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 14) The quiz generation unit, The system estimates students' emotions and adjusts the difficulty of the quiz based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The quiz generation unit, When generating quizzes, the system selects the most suitable questions by referring to students' past quiz results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The quiz generation unit, When generating quizzes, provide different quiz formats according to the students' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 17) The quiz generation unit, The system estimates students' emotions and adjusts the order of quiz questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The quiz generation unit, When generating quizzes, relevant questions are generated based on the students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The quiz generation unit, When generating quizzes, refer to students' social media activity to create engaging questions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning support unit is The system estimates students' emotions and adjusts learning support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning support unit is When providing learning support, the system selects the most suitable support method by referring to the student's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning support unit is The system estimates students' emotions and prioritizes learning support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning support unit is When providing learning support, we offer appropriate support methods based on the student's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned teaching material customization unit is The system estimates students' emotions and adjusts the content of the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned teaching material customization unit is When customizing teaching materials, the most suitable materials are selected by referring to students' past learning achievements. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned teaching material customization unit is When customizing learning materials, we provide different materials according to the students' learning styles. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned teaching material customization unit is The system estimates students' emotions and prioritizes learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned teaching material customization unit is When customizing teaching materials, we suggest appropriate materials based on students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned teaching material customization unit is When customizing teaching materials, the content is tailored based on students' interests and concerns. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Learning Progress Analysis Department analyzes students' learning progress, A learning plan proposal unit proposes individual learning plans based on the results analyzed by the aforementioned learning progress analysis unit, A quiz generation unit generates an interactive quiz or game based on the learning plan proposed by the aforementioned learning plan proposal unit, A learning support unit that supports learning through quizzes or games generated by the quiz generation unit, The system includes a learning material customization unit that automatically customizes learning materials and resources based on the learning plan proposed by the learning plan proposal unit. A system characterized by the following features.

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

3. The aforementioned learning progress analysis unit, By analyzing students' past learning history in detail, specific learning patterns can be extracted. The system according to feature 1.

4. The aforementioned learning progress analysis unit, Monitor students' learning progress in real time and provide immediate feedback. The system according to feature 1.

5. The aforementioned learning progress analysis unit, The system estimates students' emotions and adjusts the order in which it displays the analysis results of their learning progress based on those estimated emotions. The system according to feature 1.

6. The aforementioned learning progress analysis unit, Improving analytical accuracy based on student lifestyle data The system according to feature 1.

7. The aforementioned learning progress analysis unit, Evaluate students' motivation to learn by referring to their social media activity. The system according to feature 1.

8. The aforementioned learning plan proposal unit, The system estimates students' emotions and adjusts the learning plan based on those estimated emotions. The system according to feature 1.

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