system

Abstract

The system according to this embodiment aims to provide optimal educational content tailored to the individuality and proficiency level of each student. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about the individuality and proficiency level of the students. The analysis unit analyzes the information collected by the collection unit and identifies the "cause of inability to solve" and the "optimal path to understanding the solution." The generation unit generates optimal educational content based on the information identified by the analysis unit. The provision unit provides the educational content generated by the generation unit to the students.

Description

Technical Field

[0006]

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, 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 education according to the individuality and proficiency of each student, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal educational content according to the individuality and proficiency of each student.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about students' individuality and proficiency levels. The analysis unit analyzes the information collected by the collection unit and identifies the "causes of inability to solve" and the "optimal path to understanding the solution." The generation unit generates optimal educational content based on the information identified by the analysis unit. The provision unit provides the educational content generated by the generation unit to the students. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal educational content tailored to the individuality and proficiency level of each student. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM ३०, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personalized education system according to an embodiment of the present invention is a system that generates instructional content tailored to the individuality and proficiency level of each student. This personalized education system detects the individuality and proficiency level of each student and generates optimal educational content based on that information. The generated educational content is provided to the student to promote their understanding. With this system, the AI ​​automatically generates the "cause of the problem" and the "optimal path to understanding the solution" which differ for each student, thereby deepening the student's understanding. For example, if a student cannot solve a particular problem, the reason will differ for each student. The AI ​​detects the cause and generates a path to understanding the optimal solution. This allows students to learn at their own pace. The AI ​​also considers the student's interests and motivation and provides the optimal teaching method. For example, when solving a math problem, if the student likes baseball, the AI ​​will use baseball-related examples to promote understanding. Furthermore, the AI ​​monitors the student's learning progress in real time and automatically generates teaching materials as needed. For example, if a student stumbles in a particular field, the AI ​​generates teaching materials specialized for that field and provides them to the student. This allows students to learn efficiently. This system is particularly effective in online education, maximizing learning effectiveness by providing each student with the most suitable educational content. For example, at the beginning of an online lesson, AI tests the student's current level of understanding and generates optimal learning materials based on the results. If a student has difficulty understanding something during the lesson, the AI ​​identifies the cause and generates additional materials. At the end of the lesson, a review test is administered to check the student's comprehension. In this way, by utilizing AI, personalized education tailored to each student can be realized, thereby enhancing learning effectiveness. As a result, the personalized education system can provide each student with the most suitable educational content and maximize learning effectiveness.

[0029] The personalized education system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about students' individuality and proficiency levels. For example, the collection unit can collect students' personality traits and learning styles. The collection unit can also collect students' test scores and assignment completion rates. Furthermore, the collection unit can also collect information about students' interests and motivations. For example, the collection unit can collect students' survey results and behavioral data. The analysis unit analyzes the information collected by the collection unit and identifies the "causes of inability to solve" and the "optimal path to understanding the solution." For example, the analysis unit identifies the causes of students' lack of understanding, misunderstandings, or calculation errors based on the collected information. The analysis unit can also identify paths to understanding the optimal solution, such as step-by-step explanations and provision of related knowledge. The generation unit generates optimal educational content based on the information identified by the analysis unit. For example, the generation unit can generate individualized teaching materials or adaptive learning content. The generation unit can also generate optimal teaching materials based on the analysis results. The provisioning unit provides students with educational content generated by the generation unit. The provisioning unit can provide educational content by methods such as online distribution or distribution of printed materials. The provisioning unit can also provide students with the generated teaching materials. In this way, the personalized education system according to the embodiment can maximize learning effectiveness by providing educational content tailored to the individuality and proficiency level of each student. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and provisioning unit may be performed using AI, for example, or without AI. For example, the collection unit can input information about students' individuality and proficiency level into the AI ​​and have the AI ​​collect the information. The analysis unit can input the collected information into the AI ​​and have the AI ​​identify the "cause of the inability to solve" and the "optimal path to understanding the solution." The generation unit can input the analysis results into the AI ​​and have the AI ​​generate the optimal educational content. The provisioning unit can input the generated educational content into the AI ​​and have the AI ​​provide the educational content.

[0030] The data collection department gathers information about students' personalities and proficiency levels. Specifically, it conducts psychological tests and questionnaires to understand students' personality traits and learning styles, and stores the results in a database. It also regularly records students' test scores and assignment completion rates to track their learning progress in detail. Furthermore, it analyzes behavioral data during learning and activity history on online platforms to gather information about students' interests and motivation. For example, it monitors the number of clicks, time spent, and eye movements during learning sessions to understand what kind of materials students are interested in and when their concentration wavers. This data is transmitted in real time to a cloud-based database and centrally managed by the data collection department. Based on this information, the data collection department creates learning profiles for each student and uses them as foundational data for individualized educational plans. In addition, the data collection department can integrate additional information provided by external educational institutions and parents to build a more comprehensive dataset. This allows the data collection department to gain a detailed understanding of diverse aspects of students and efficiently collect the foundational data necessary to provide individualized educational content.

[0031] The analysis unit analyzes the information collected by the data collection unit to identify the "reasons why students cannot solve problems" and the "optimal path to understanding the solution." Specifically, it uses AI to analyze the collected data and identify the causes of students' lack of understanding, misunderstandings, calculation errors, etc. For example, it uses natural language processing technology to analyze students' answers and comments to identify where misunderstandings occur. It also uses machine learning algorithms to analyze students' past learning history and test results to extract their level of understanding and error patterns for specific problems. Furthermore, the analysis unit identifies the path to understanding the optimal solution, such as providing step-by-step explanations and related knowledge. For example, the AI ​​automatically generates a learning path from basic concepts to applied problems according to the student's level of understanding and proposes the optimal learning order. In this way, the analysis unit can provide the optimal learning path tailored to each student's learning needs and support efficient learning. In addition, the analysis unit can utilize past data and statistical information to predict long-term learning trends and performance fluctuations, which can be used to plan future learning. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term learning management and performance optimization, thereby improving the reliability and effectiveness of the entire system.

[0032] The generation unit generates optimal educational content based on the information identified by the analysis unit. Specifically, it uses AI to generate personalized learning materials and adaptive learning content. For example, it uses natural language generation technology to automatically generate explanatory texts and questions tailored to each student's level of understanding and learning style. It can also use image recognition technology to generate visually easy-to-understand diagrams and illustrations. Furthermore, the generation unit reconfigures existing educational resources and customizes them to meet student needs in order to generate optimal learning materials based on the analysis results. For example, it can generate learning materials that combine relevant video content and interactive simulations to reinforce specific concepts. The generation unit can store this educational content in the cloud and update it in real time as needed. This allows the generation unit to quickly and efficiently generate and deliver optimal educational content tailored to the learning needs of each individual student. In addition, the generation unit can evaluate the effectiveness of the generated educational content and build a feedback loop for continuous improvement. This allows the generation unit to always utilize the latest information and technology to provide high-quality educational content and maximize learning effectiveness.

[0033] The delivery department provides students with educational content generated by the generation department. Specifically, educational content is delivered through methods such as online distribution and printed materials. For example, generated materials can be delivered directly to students' devices via an online platform, providing an interactive learning experience. Alternatively, materials can be distributed as printed materials, allowing students to continue learning even in offline environments. In addition to providing students with generated materials, the delivery department monitors their learning progress in real time and provides additional support and feedback as needed. For example, it tracks students' learning progress on the online platform and provides additional materials or supplementary explanations if a student's understanding of a particular problem is low. Furthermore, the delivery department can build a comprehensive support system by collaborating with parents and educational institutions to share students' learning progress and achievements. This allows the delivery department to provide optimal educational content tailored to each student's learning needs and maximize learning effectiveness. Moreover, the delivery department can collect user feedback and continuously improve the accuracy and effectiveness of the content provided. For example, it can analyze students' reactions and results to the learning content and reflect them in the generation of future content. This allows the service provider to constantly utilize the latest information and technology to deliver high-quality educational content and maximize learning effectiveness.

[0034] The personalized education system further includes a monitoring unit that monitors learning progress in real time and automatically generates learning materials as needed. The monitoring unit can monitor learning progress, for example, in seconds or minutes. Based on conditions such as delays in learning progress or failure to complete specific tasks, the monitoring unit can automatically generate learning materials as needed. For example, if a student is struggling in a particular area, the monitoring unit can generate and provide learning materials tailored to that area. This supports efficient learning by automatically generating learning materials according to learning progress. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input student learning progress data into AI and have the AI ​​perform the automatic generation of learning materials.

[0035] The personalized education system further includes a testing unit that conducts review tests to check students' understanding. The testing unit can check students' understanding based on evaluation criteria such as test scores and the percentage of correct answers to questions. By conducting review tests, the testing unit can check students' understanding and improve learning effectiveness. For example, the testing unit can conduct a review test at the end of a lesson to check students' understanding. This allows the testing unit to check students' understanding and improve learning effectiveness. Some or all of the above processing in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input student test data into AI and have the AI ​​perform the understanding check.

[0036] The data collection unit can collect information about students' interests and motivations. For example, the data collection unit can collect student survey results and behavioral data. By collecting information about students' interests and motivations, the data collection unit can provide more effective educational content. For example, the data collection unit can generate optimal educational content based on students' interests and motivations. This allows for the provision of more effective educational content by collecting information about students' interests and motivations. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about students' interests and motivations into AI and have the AI ​​perform the information collection.

[0037] The analysis unit can consider students' interests and motivations based on the collected information. For example, the analysis unit can consider students' interests and motivations based on the collected information in ways such as weighting or prioritizing. By considering students' interests and motivations, the analysis unit can enhance learning effectiveness. For example, the analysis unit can generate optimal educational content based on students' interests and motivations. This enhances learning effectiveness by considering students' interests and motivations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the consideration of interests and motivations.

[0038] The generation unit can generate optimal learning materials based on the analysis results. The generation unit can, for example, generate personalized learning materials or adaptive learning content. By generating optimal learning materials based on the analysis results, the generation unit can facilitate student understanding. For example, the generation unit can generate optimal learning materials to deepen student understanding based on the analysis results. In this way, student understanding can be facilitated by generating optimal learning materials based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI and have the AI ​​perform the generation of optimal learning materials.

[0039] The distribution unit can provide the generated learning materials to students. The distribution unit can provide the learning materials by methods such as online distribution or distribution of printed materials. By providing the generated learning materials to students, the distribution unit can enhance learning effectiveness. For example, by providing the generated learning materials to students, the distribution unit can facilitate student understanding. This enhances learning effectiveness by providing the generated learning materials to students. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the generated learning materials into AI and have the AI ​​perform the task of providing the learning materials.

[0040] The data collection unit can analyze a student's past learning history and select the optimal information collection method. For example, the data collection unit can focus on collecting information about areas where the student has struggled in the past. The data collection unit can also supplementarily collect information about areas where the student excels. The data collection unit can also collect information from the student's learning history that corresponds to their learning progress. This allows the optimal information collection method to be selected by analyzing the student's past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's learning history data into AI and have the AI ​​select the information collection method.

[0041] The data collection unit can filter information based on the student's current learning environment and areas of interest. For example, if a student is studying at home, the data collection unit can collect information suitable for home study. If a student is studying at school, the data collection unit can also collect information aligned with the school curriculum. The data collection unit can also collect information that will interest the student based on their areas of interest. This allows for the collection of more appropriate information by filtering information based on the student's learning environment and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input student learning environment data and areas of interest data into AI and have the AI ​​perform the information filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the student's geographical location during information gathering. For example, if a student is in a specific region, the data collection unit can collect information related to that region. If a student is traveling, the data collection unit can also collect information related to their travel destination. If a student is participating in a specific event, the data collection unit can also collect information related to that event. This allows for the priority collection of highly relevant information by considering the student's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location information into an AI and have the AI ​​perform the information collection.

[0043] The data collection unit can analyze students' social media activity and collect relevant information during the information gathering process. For example, the data collection unit can collect information related to topics that students have shown interest in on social media. The data collection unit can also collect information based on the content of posts from accounts that students follow. The data collection unit can also collect information related to groups and communities that students participate in. This allows relevant information to be collected by analyzing students' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input students' social media data into AI and have the AI ​​perform the information collection.

[0044] The analysis unit can improve the accuracy of its analysis by considering the relationships between students. For example, the analysis unit can improve the accuracy of its analysis by comparing the learning progress of students. The analysis unit can also perform analysis while considering the cooperative relationships between students. The analysis unit can also perform analysis while considering the competitive relationships between students. In this way, the accuracy of the analysis can be improved by considering the relationships between students. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input student relationship data into AI and have the AI ​​perform the improvement of the analysis accuracy.

[0045] The analysis unit can perform analysis while considering the student's attribute information. For example, the analysis unit can perform analysis while considering the student's age and gender. The analysis unit can also perform analysis while considering the student's learning style. The analysis unit can also perform analysis while considering the student's interests and concerns. This allows for more appropriate analysis by considering the student's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the student's attribute information into AI and have the AI ​​perform the analysis.

[0046] The analysis unit can perform analysis while considering the geographical distribution of students. For example, if a student is in a specific region, the analysis unit can analyze data related to that region. If a student is traveling, the analysis unit can also analyze data related to their travel destination. If a student is participating in a specific event, the analysis unit can also analyze data related to that event. This allows for more appropriate analysis by considering the geographical distribution of students. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of students into an AI and have the AI ​​perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit performs the analysis by referring to literature related to the topic being analyzed. The analysis unit can also improve the accuracy of its analysis by comparing the results with relevant literature. The analysis unit can also perform the analysis by referring to the latest research results during the analysis process. This allows for improved accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into AI and have AI perform the task of improving the accuracy of the analysis.

[0048] The generation unit can adjust the level of detail in the learning materials based on the importance of the learning content during material generation. For example, the generation unit can generate materials with detailed explanations for important learning content. For less important learning content, the generation unit can also generate materials with concise explanations. The generation unit can also adjust the level of detail in the materials in stages according to the importance of the learning content. This allows for deeper learning of important content by adjusting the level of detail in the materials based on the importance of the learning content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content importance data into AI and have the AI ​​perform the adjustment of the level of detail in the learning materials.

[0049] The generation unit can apply different generation algorithms depending on the learning category when generating learning materials. For example, the generation unit can apply an algorithm that makes extensive use of mathematical formulas and graphs when generating mathematics materials. The generation unit can also apply an algorithm that makes extensive use of timelines and maps when generating history materials. The generation unit can also apply an algorithm that makes extensive use of experimental videos and simulations when generating science materials. By applying the most suitable generation algorithm for each learning category, more effective learning materials can be provided. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning category data into AI and have the AI ​​execute the application of the generation algorithm.

[0050] The generation unit can determine the priority of learning materials based on the submission deadlines for the learning content when generating the materials. For example, the generation unit can prioritize generating materials for learning content with an approaching submission deadline. The generation unit can also postpone generating materials for learning content with a distant submission deadline. The generation unit can also adjust the priority of materials in stages according to the submission deadline. This allows for efficient learning by determining the priority of materials based on the submission deadlines for the learning content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content submission deadline data into AI and have the AI ​​determine the priority of the materials.

[0051] The generation unit can adjust the order of learning materials based on the relevance of the learning content during material generation. For example, the generation unit can generate materials consecutively for highly relevant learning content. For less relevant learning content, the generation unit can also generate materials with intervals in between. The generation unit can also adjust the order of materials in stages according to the relevance of the learning content. This allows for more effective learning by adjusting the order of materials based on the relevance of the learning content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the relevance of learning content into AI and have AI perform the adjustment of the order of materials.

[0052] The delivery unit can select the optimal delivery method by referring to the student's past learning history when providing learning materials. For example, the delivery unit may prioritize delivery methods that the student has preferred to use in the past. The delivery unit can also select the optimal delivery method based on the student's learning history. The delivery unit can also analyze the student's learning history and select the most effective delivery method. This allows the delivery unit to select the optimal delivery method by referring to the student's past learning history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the student's learning history data into AI and have the AI ​​perform the selection of the delivery method.

[0053] The delivery unit can customize the delivery methods based on the student's current learning situation when providing learning materials. For example, if a student is concentrating, the delivery unit can select delivery methods to maintain concentration. If a student is tired, the delivery unit can also select delivery methods to encourage refreshment. If a student is excited, the delivery unit can also select delivery methods to help them regain composure. By customizing the delivery methods based on the student's current learning situation, it is possible to support more effective learning. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input student learning situation data into AI and have the AI ​​perform the customization of the delivery methods.

[0054] The distribution unit can select the optimal distribution method when providing educational materials, taking into account the student's geographical location. For example, if a student is in a specific region, the distribution unit can provide materials related to that region. If a student is traveling, the distribution unit can also provide materials related to their travel destination. If a student is participating in a specific event, the distribution unit can also provide materials related to that event. This allows the distribution unit to select the optimal distribution method by considering the student's geographical location. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the student's geographical location information into AI and have the AI ​​select the distribution method.

[0055] The content delivery unit can analyze students' social media activity and propose delivery methods when providing educational materials. For example, the content delivery unit can provide materials related to topics that students have shown interest in on social media. The content delivery unit can also provide materials based on the content of posts from accounts that students follow. The content delivery unit can also provide materials related to groups and communities that students participate in. This allows the content delivery unit to propose the most suitable delivery method by analyzing students' social media activity. Some or all of the above processing in the content delivery unit may be performed using AI, for example, or not using AI. For example, the content delivery unit can input students' social media data into AI and have the AI ​​propose delivery methods.

[0056] The monitoring unit can optimize its monitoring algorithm by referring to past learning data during monitoring. For example, the monitoring unit optimizes the monitoring algorithm based on the student's past learning data. The monitoring unit can also set optimal monitoring criteria from the student's learning history. The monitoring unit can also analyze the student's learning data to improve the accuracy of monitoring. This allows the monitoring algorithm to be optimized by referring to past learning data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past learning data into AI and have the AI ​​perform the optimization of the monitoring algorithm.

[0057] The monitoring unit can adjust the frequency of monitoring according to the student's learning progress. For example, if a student's learning progress is behind, the monitoring unit can increase the frequency of monitoring. If a student's learning progress is on track, the monitoring unit can also decrease the frequency of monitoring. The monitoring unit can also adjust the frequency of monitoring in stages according to the student's learning progress. This allows for more effective monitoring by adjusting the frequency of monitoring according to the student's learning progress. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input student learning progress data into AI and have the AI ​​adjust the frequency of monitoring.

[0058] The monitoring unit can determine monitoring priorities based on students' learning history during monitoring. For example, the monitoring unit can prioritize monitoring important learning content based on students' learning history. The monitoring unit can also analyze students' learning history and set monitoring priorities. The monitoring unit can also adjust monitoring priorities in stages based on students' learning history. This allows for more effective monitoring by determining monitoring priorities based on students' learning history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input student learning history data into AI and have the AI ​​perform the determination of monitoring priorities.

[0059] The monitoring unit can improve the accuracy of monitoring by referring to relevant data during monitoring. For example, the monitoring unit performs monitoring by referring to data related to the topic being monitored. The monitoring unit can also improve the accuracy of monitoring by comparing the monitoring results with relevant data. The monitoring unit can also perform monitoring by referring to the latest research results during monitoring. This allows for improved monitoring accuracy by referring to relevant data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant data into AI and have AI perform the improvement of monitoring accuracy.

[0060] The testing department can select the optimal test content by referring to students' past test results when administering a test. For example, the testing department can select the optimal test content based on students' past test results. The testing department can also select the optimal test content from students' test history. The testing department can also analyze students' test results and select the most effective test content. In this way, the optimal test content can be selected by referring to students' past test results. Some or all of the above processes in the testing department may be performed using AI, for example, or without AI. For example, the testing department can input students' past test result data into AI and have the AI ​​perform the selection of test content.

[0061] The testing unit can customize the difficulty level of a test based on the student's current learning progress when administering the test. For example, if a student is advanced in their learning, the testing unit can increase the difficulty level. If a student is behind in their learning, the testing unit can also ease the difficulty level. The testing unit can also adjust the difficulty level of the test in stages according to the student's learning progress. This allows for more appropriate testing by customizing the difficulty level based on the student's current learning progress. Some or all of the above processes in the testing unit may be performed using AI, for example, or not. For example, the testing unit can input student learning progress data into an AI and have the AI ​​perform the customization of the test difficulty level.

[0062] The testing department can select the optimal testing method when administering a test, taking into account the students' geographical location. For example, if a student is in a specific region, the testing department can administer a test relevant to that region. If a student is traveling, the testing department can administer a test relevant to their travel destination. If a student is participating in a specific event, the testing department can administer a test relevant to that event. This allows the testing department to select the optimal testing method by considering the students' geographical location. Some or all of the above processing in the testing department may be performed using AI, for example, or not. For example, the testing department can input the students' geographical location information into an AI and have the AI ​​select the testing method.

[0063] The testing department can analyze students' social media activity and propose test content when administering a test. For example, the testing department can conduct tests related to topics that students have shown interest in on social media. The testing department can also conduct tests based on the content of posts from accounts that students follow. The testing department can also conduct tests related to groups and communities that students participate in. In this way, by analyzing students' social media activity, the testing department can propose the most suitable test content. Some or all of the above processes in the testing department may be performed using AI, for example, or not. For example, the testing department can input students' social media data into an AI and have the AI ​​propose test content.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The analysis unit can suggest the optimal learning method based on each student's learning style. For example, it can suggest materials that make extensive use of diagrams and graphs for visual learners, materials that make extensive use of audio and video for auditory learners, and practical materials that allow tactile learners to learn by actually using their hands for hands-on activities. By suggesting the optimal learning method tailored to each student's learning style, the system can enhance learning effectiveness.

[0066] The monitoring unit can monitor students' learning progress in real time and automatically adjust learning plans as needed. For example, if a student is ahead of schedule, the learning plan can be accelerated. Conversely, if a student is behind schedule, the learning plan can be reviewed and adjusted to ensure progress. Furthermore, if a student is struggling in a particular area, supplementary materials tailored to that area can be provided. This allows for flexible adjustment of learning plans according to learning progress, supporting efficient learning.

[0067] The testing department can conduct regular quizzes to check students' understanding. For example, a quiz can be given at the end of each week's lessons, and the results can be used to assess understanding. Furthermore, unit tests can be administered upon completion of specific units to check overall understanding. Finally, a comprehensive test can be administered at the end of the semester to assess overall understanding. This allows for regular testing to monitor students' understanding and improve learning effectiveness.

[0068] The data collection unit can analyze students' learning trends based on their learning history. For example, it can analyze past test results and assignment submission records to identify areas where students excel and areas where they struggle. It can also analyze learning progress to understand the pace at which students are learning. Furthermore, it can analyze changes in students' learning styles and motivation to suggest optimal learning methods. In this way, by analyzing learning trends based on students' learning history, more effective learning support can be provided.

[0069] The analysis unit can predict future learning plans based on students' learning history. For example, it can analyze past learning data to predict future learning progress. It can also predict learning progress in specific subjects and create learning plans tailored to those subjects. Furthermore, it can predict changes in students' learning styles and motivations and suggest optimal learning methods. By predicting future learning plans, it is possible to provide more effective learning support.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection department collects information about students' personalities and proficiency levels. For example, it collects information about students' personality traits, learning styles, test scores, assignment completion, interests, and motivation. The data collection department also collects student survey results and behavioral data. Step 2: The analysis unit analyzes the information collected by the collection unit to identify the "causes of inability to solve" and the "optimal path to understanding the solution." For example, it identifies causes such as students' lack of understanding, misunderstandings, or calculation errors, and identifies the optimal path to understanding the solution, such as providing step-by-step explanations and related knowledge. Step 3: The generation unit generates optimal educational content based on the information identified by the analysis unit. For example, it generates personalized learning materials and adaptive learning content, and generates the optimal learning materials based on the analysis results. Step 4: The delivery unit provides the educational content generated by the generation unit to the students. For example, the educational content is provided through methods such as online distribution or distribution of printed materials.

[0072] (Example of form 2) The personalized education system according to an embodiment of the present invention is a system that generates instructional content tailored to the individuality and proficiency level of each student. This personalized education system detects the individuality and proficiency level of each student and generates optimal educational content based on that information. The generated educational content is provided to the student to promote their understanding. With this system, the AI ​​automatically generates the "cause of the problem" and the "optimal path to understanding the solution" which differ for each student, thereby deepening the student's understanding. For example, if a student cannot solve a particular problem, the reason will differ for each student. The AI ​​detects the cause and generates a path to understanding the optimal solution. This allows students to learn at their own pace. The AI ​​also considers the student's interests and motivation and provides the optimal teaching method. For example, when solving a math problem, if the student likes baseball, the AI ​​will use baseball-related examples to promote understanding. Furthermore, the AI ​​monitors the student's learning progress in real time and automatically generates teaching materials as needed. For example, if a student stumbles in a particular field, the AI ​​generates teaching materials specialized for that field and provides them to the student. This allows students to learn efficiently. This system is particularly effective in online education, maximizing learning effectiveness by providing each student with the most suitable educational content. For example, at the beginning of an online lesson, AI tests the student's current level of understanding and generates optimal learning materials based on the results. If a student has difficulty understanding something during the lesson, the AI ​​identifies the cause and generates additional materials. At the end of the lesson, a review test is administered to check the student's comprehension. In this way, by utilizing AI, personalized education tailored to each student can be realized, thereby enhancing learning effectiveness. As a result, the personalized education system can provide each student with the most suitable educational content and maximize learning effectiveness.

[0073] The personalized education system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about students' individuality and proficiency levels. For example, the collection unit can collect students' personality traits and learning styles. The collection unit can also collect students' test scores and assignment completion rates. Furthermore, the collection unit can also collect information about students' interests and motivations. For example, the collection unit can collect students' survey results and behavioral data. The analysis unit analyzes the information collected by the collection unit and identifies the "causes of inability to solve" and the "optimal path to understanding the solution." For example, the analysis unit identifies the causes of students' lack of understanding, misunderstandings, or calculation errors based on the collected information. The analysis unit can also identify paths to understanding the optimal solution, such as step-by-step explanations and provision of related knowledge. The generation unit generates optimal educational content based on the information identified by the analysis unit. For example, the generation unit can generate individualized teaching materials or adaptive learning content. The generation unit can also generate optimal teaching materials based on the analysis results. The provisioning unit provides students with educational content generated by the generation unit. The provisioning unit can provide educational content by methods such as online distribution or distribution of printed materials. The provisioning unit can also provide students with the generated teaching materials. In this way, the personalized education system according to the embodiment can maximize learning effectiveness by providing educational content tailored to the individuality and proficiency level of each student. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and provisioning unit may be performed using AI, for example, or without AI. For example, the collection unit can input information about students' individuality and proficiency level into the AI ​​and have the AI ​​collect the information. The analysis unit can input the collected information into the AI ​​and have the AI ​​identify the "cause of the inability to solve" and the "optimal path to understanding the solution." The generation unit can input the analysis results into the AI ​​and have the AI ​​generate the optimal educational content. The provisioning unit can input the generated educational content into the AI ​​and have the AI ​​provide the educational content.

[0074] The data collection department gathers information about students' personalities and proficiency levels. Specifically, it conducts psychological tests and questionnaires to understand students' personality traits and learning styles, and stores the results in a database. It also regularly records students' test scores and assignment completion rates to track their learning progress in detail. Furthermore, it analyzes behavioral data during learning and activity history on online platforms to gather information about students' interests and motivation. For example, it monitors the number of clicks, time spent, and eye movements during learning sessions to understand what kind of materials students are interested in and when their concentration wavers. This data is transmitted in real time to a cloud-based database and centrally managed by the data collection department. Based on this information, the data collection department creates learning profiles for each student and uses them as foundational data for individualized educational plans. In addition, the data collection department can integrate additional information provided by external educational institutions and parents to build a more comprehensive dataset. This allows the data collection department to gain a detailed understanding of diverse aspects of students and efficiently collect the foundational data necessary to provide individualized educational content.

[0075] The analysis unit analyzes the information collected by the data collection unit to identify the "reasons why students cannot solve problems" and the "optimal path to understanding the solution." Specifically, it uses AI to analyze the collected data and identify the causes of students' lack of understanding, misunderstandings, calculation errors, etc. For example, it uses natural language processing technology to analyze students' answers and comments to identify where misunderstandings occur. It also uses machine learning algorithms to analyze students' past learning history and test results to extract their level of understanding and error patterns for specific problems. Furthermore, the analysis unit identifies the path to understanding the optimal solution, such as providing step-by-step explanations and related knowledge. For example, the AI ​​automatically generates a learning path from basic concepts to applied problems according to the student's level of understanding and proposes the optimal learning order. In this way, the analysis unit can provide the optimal learning path tailored to each student's learning needs and support efficient learning. In addition, the analysis unit can utilize past data and statistical information to predict long-term learning trends and performance fluctuations, which can be used to plan future learning. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term learning management and performance optimization, thereby improving the reliability and effectiveness of the entire system.

[0076] The generation unit generates optimal educational content based on the information identified by the analysis unit. Specifically, it uses AI to generate personalized learning materials and adaptive learning content. For example, it uses natural language generation technology to automatically generate explanatory texts and questions tailored to each student's level of understanding and learning style. It can also use image recognition technology to generate visually easy-to-understand diagrams and illustrations. Furthermore, the generation unit reconfigures existing educational resources and customizes them to meet student needs in order to generate optimal learning materials based on the analysis results. For example, it can generate learning materials that combine relevant video content and interactive simulations to reinforce specific concepts. The generation unit can store this educational content in the cloud and update it in real time as needed. This allows the generation unit to quickly and efficiently generate and deliver optimal educational content tailored to the learning needs of each individual student. In addition, the generation unit can evaluate the effectiveness of the generated educational content and build a feedback loop for continuous improvement. This allows the generation unit to always utilize the latest information and technology to provide high-quality educational content and maximize learning effectiveness.

[0077] The delivery department provides students with educational content generated by the generation department. Specifically, educational content is delivered through methods such as online distribution and printed materials. For example, generated materials can be delivered directly to students' devices via an online platform, providing an interactive learning experience. Alternatively, materials can be distributed as printed materials, allowing students to continue learning even in offline environments. In addition to providing students with generated materials, the delivery department monitors their learning progress in real time and provides additional support and feedback as needed. For example, it tracks students' learning progress on the online platform and provides additional materials or supplementary explanations if a student's understanding of a particular problem is low. Furthermore, the delivery department can build a comprehensive support system by collaborating with parents and educational institutions to share students' learning progress and achievements. This allows the delivery department to provide optimal educational content tailored to each student's learning needs and maximize learning effectiveness. Moreover, the delivery department can collect user feedback and continuously improve the accuracy and effectiveness of the content provided. For example, it can analyze students' reactions and results to the learning content and reflect them in the generation of future content. This allows the service provider to constantly utilize the latest information and technology to deliver high-quality educational content and maximize learning effectiveness.

[0078] The personalized education system further includes a monitoring unit that monitors learning progress in real time and automatically generates learning materials as needed. The monitoring unit can monitor learning progress, for example, in seconds or minutes. Based on conditions such as delays in learning progress or failure to complete specific tasks, the monitoring unit can automatically generate learning materials as needed. For example, if a student is struggling in a particular area, the monitoring unit can generate and provide learning materials tailored to that area. This supports efficient learning by automatically generating learning materials according to learning progress. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input student learning progress data into AI and have the AI ​​perform the automatic generation of learning materials.

[0079] The personalized education system further includes a testing unit that conducts review tests to check students' understanding. The testing unit can check students' understanding based on evaluation criteria such as test scores and the percentage of correct answers to questions. By conducting review tests, the testing unit can check students' understanding and improve learning effectiveness. For example, the testing unit can conduct a review test at the end of a lesson to check students' understanding. This allows the testing unit to check students' understanding and improve learning effectiveness. Some or all of the above processing in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input student test data into AI and have the AI ​​perform the understanding check.

[0080] The data collection unit can collect information about students' interests and motivations. For example, the data collection unit can collect student survey results and behavioral data. By collecting information about students' interests and motivations, the data collection unit can provide more effective educational content. For example, the data collection unit can generate optimal educational content based on students' interests and motivations. This allows for the provision of more effective educational content by collecting information about students' interests and motivations. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about students' interests and motivations into AI and have the AI ​​perform the information collection.

[0081] The analysis unit can consider students' interests and motivations based on the collected information. For example, the analysis unit can consider students' interests and motivations based on the collected information in ways such as weighting or prioritizing. By considering students' interests and motivations, the analysis unit can enhance learning effectiveness. For example, the analysis unit can generate optimal educational content based on students' interests and motivations. This enhances learning effectiveness by considering students' interests and motivations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the consideration of interests and motivations.

[0082] The generation unit can generate optimal learning materials based on the analysis results. The generation unit can, for example, generate personalized learning materials or adaptive learning content. By generating optimal learning materials based on the analysis results, the generation unit can facilitate student understanding. For example, the generation unit can generate optimal learning materials to deepen student understanding based on the analysis results. In this way, student understanding can be facilitated by generating optimal learning materials based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI and have the AI ​​perform the generation of optimal learning materials.

[0083] The distribution unit can provide the generated learning materials to students. The distribution unit can provide the learning materials by methods such as online distribution or distribution of printed materials. By providing the generated learning materials to students, the distribution unit can enhance learning effectiveness. For example, by providing the generated learning materials to students, the distribution unit can facilitate student understanding. This enhances learning effectiveness by providing the generated learning materials to students. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the generated learning materials into AI and have the AI ​​perform the task of providing the learning materials.

[0084] The data collection unit can estimate a student's emotions and adjust the type of information it collects based on the estimated emotions. For example, if a student is stressed, the data collection unit will prioritize collecting information that helps them relax. If a student is excited, the data collection unit can also collect information that helps them concentrate. If a student is tired, the data collection unit can also collect information related to rest and refreshment. By adjusting the type of information based on the student's emotions, more appropriate information can be collected. 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 processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input student emotion data into a generative AI and have the generative AI adjust the type of information to collect.

[0085] The data collection unit can analyze a student's past learning history and select the optimal information collection method. For example, the data collection unit can focus on collecting information about areas where the student has struggled in the past. The data collection unit can also supplementarily collect information about areas where the student excels. The data collection unit can also collect information from the student's learning history that corresponds to their learning progress. This allows the optimal information collection method to be selected by analyzing the student's past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's learning history data into AI and have the AI ​​select the information collection method.

[0086] The data collection unit can filter information based on the student's current learning environment and areas of interest. For example, if a student is studying at home, the data collection unit can collect information suitable for home study. If a student is studying at school, the data collection unit can also collect information aligned with the school curriculum. The data collection unit can also collect information that will interest the student based on their areas of interest. This allows for the collection of more appropriate information by filtering information based on the student's learning environment and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input student learning environment data and areas of interest data into AI and have the AI ​​perform the information filtering.

[0087] The data collection unit can estimate a student's emotions and prioritize the information to collect based on the estimated emotions. For example, if a student is feeling anxious, the data collection unit will prioritize collecting information that provides reassurance. If a student is excited, the data collection unit may also prioritize collecting information that helps them regain their composure. If a student is focused, the data collection unit may also prioritize collecting information that is helpful for learning. This allows for more effective information collection by prioritizing information based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input student emotion data into a generative AI and have the generative AI determine the priority of information.

[0088] The data collection unit can prioritize the collection of highly relevant information by considering the student's geographical location during information gathering. For example, if a student is in a specific region, the data collection unit can collect information related to that region. If a student is traveling, the data collection unit can also collect information related to their travel destination. If a student is participating in a specific event, the data collection unit can also collect information related to that event. This allows for the priority collection of highly relevant information by considering the student's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location information into an AI and have the AI ​​perform the information collection.

[0089] The data collection unit can analyze students' social media activity and collect relevant information during the information gathering process. For example, the data collection unit can collect information related to topics that students have shown interest in on social media. The data collection unit can also collect information based on the content of posts from accounts that students follow. The data collection unit can also collect information related to groups and communities that students participate in. This allows relevant information to be collected by analyzing students' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input students' social media data into AI and have the AI ​​perform the information collection.

[0090] The analysis unit can estimate a student's emotions and adjust the analysis criteria based on the estimated emotions. For example, if a student is stressed, the analysis unit may relax the analysis criteria. If a student is relaxed, the analysis unit may tighten the analysis criteria. If a student is focused, the analysis unit may perform a more detailed analysis. This allows for a more appropriate analysis by adjusting the analysis criteria based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI adjust the analysis criteria.

[0091] The analysis unit can improve the accuracy of its analysis by considering the relationships between students. For example, the analysis unit can improve the accuracy of its analysis by comparing the learning progress of students. The analysis unit can also perform analysis while considering the cooperative relationships between students. The analysis unit can also perform analysis while considering the competitive relationships between students. In this way, the accuracy of the analysis can be improved by considering the relationships between students. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input student relationship data into AI and have the AI ​​perform the improvement of the analysis accuracy.

[0092] The analysis unit can perform analysis while considering the student's attribute information. For example, the analysis unit can perform analysis while considering the student's age and gender. The analysis unit can also perform analysis while considering the student's learning style. The analysis unit can also perform analysis while considering the student's interests and concerns. This allows for more appropriate analysis by considering the student's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the student's attribute information into AI and have the AI ​​perform the analysis.

[0093] The analysis unit can estimate a student's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if a student is feeling anxious, the analysis unit can prioritize displaying analysis results that provide a sense of security. If a student is excited, the analysis unit can also prioritize displaying analysis results that help them regain their composure. If a student is concentrating, the analysis unit can also prioritize displaying analysis results that are helpful for learning. By adjusting the display order of analysis results based on the student's emotions, more effective information can be provided. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI adjust the display order of the analysis results.

[0094] The analysis unit can perform analysis while considering the geographical distribution of students. For example, if a student is in a specific region, the analysis unit can analyze data related to that region. If a student is traveling, the analysis unit can also analyze data related to their travel destination. If a student is participating in a specific event, the analysis unit can also analyze data related to that event. This allows for more appropriate analysis by considering the geographical distribution of students. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of students into an AI and have the AI ​​perform the analysis.

[0095] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit performs the analysis by referring to literature related to the topic being analyzed. The analysis unit can also improve the accuracy of its analysis by comparing the results with relevant literature. The analysis unit can also perform the analysis by referring to the latest research results during the analysis process. This allows for improved accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into AI and have AI perform the task of improving the accuracy of the analysis.

[0096] The generation unit can estimate students' emotions and adjust the presentation of the generated materials based on the estimated emotions. For example, if a student is relaxed, the generation unit can generate materials that proceed at a relaxed pace. If a student is in a hurry, the generation unit can also generate materials that emphasize the shortest route. If a student is excited, the generation unit can also generate materials with visually stimulating effects. This allows for the provision of more effective materials by adjusting the presentation based on students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input student emotion data into the generation AI and have the generation AI adjust the presentation of the materials.

[0097] The generation unit can adjust the level of detail in the learning materials based on the importance of the learning content during material generation. For example, the generation unit can generate materials with detailed explanations for important learning content. For less important learning content, the generation unit can also generate materials with concise explanations. The generation unit can also adjust the level of detail in the materials in stages according to the importance of the learning content. This allows for deeper learning of important content by adjusting the level of detail in the materials based on the importance of the learning content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content importance data into AI and have the AI ​​perform the adjustment of the level of detail in the learning materials.

[0098] The generation unit can apply different generation algorithms depending on the learning category when generating learning materials. For example, the generation unit can apply an algorithm that makes extensive use of mathematical formulas and graphs when generating mathematics materials. The generation unit can also apply an algorithm that makes extensive use of timelines and maps when generating history materials. The generation unit can also apply an algorithm that makes extensive use of experimental videos and simulations when generating science materials. By applying the most suitable generation algorithm for each learning category, more effective learning materials can be provided. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning category data into AI and have the AI ​​execute the application of the generation algorithm.

[0099] The generation unit can estimate a student's emotions and adjust the length of the generated learning materials based on the estimated emotions. For example, if a student is in a hurry, the generation unit can generate short, concise materials. If a student is relaxed, the generation unit can also generate longer materials with detailed explanations. If a student is excited, the generation unit can also generate materials with visually stimulating effects. This allows for more effective learning by adjusting the length of the materials based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input student emotion data into a generation AI and have the generation AI adjust the length of the learning materials.

[0100] The generation unit can determine the priority of learning materials based on the submission deadlines for the learning content when generating the materials. For example, the generation unit can prioritize generating materials for learning content with an approaching submission deadline. The generation unit can also postpone generating materials for learning content with a distant submission deadline. The generation unit can also adjust the priority of materials in stages according to the submission deadline. This allows for efficient learning by determining the priority of materials based on the submission deadlines for the learning content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learning content submission deadline data into AI and have the AI ​​determine the priority of the materials.

[0101] The generation unit can adjust the order of learning materials based on the relevance of the learning content during material generation. For example, the generation unit can generate materials consecutively for highly relevant learning content. For less relevant learning content, the generation unit can also generate materials with intervals in between. The generation unit can also adjust the order of materials in stages according to the relevance of the learning content. This allows for more effective learning by adjusting the order of materials based on the relevance of the learning content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the relevance of learning content into AI and have AI perform the adjustment of the order of materials.

[0102] The delivery unit can estimate students' emotions and adjust the way materials are delivered based on the estimated emotions. For example, if a student is relaxed, the delivery unit can deliver materials at a relaxed pace. If a student is in a hurry, the delivery unit can deliver materials quickly. If a student is excited, the delivery unit can deliver materials with visually stimulating effects. This allows for more effective learning by adjusting the delivery method of materials based on students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input student emotion data into a generative AI and have the generative AI adjust the delivery method of the materials.

[0103] The delivery unit can select the optimal delivery method by referring to the student's past learning history when providing learning materials. For example, the delivery unit may prioritize delivery methods that the student has preferred to use in the past. The delivery unit can also select the optimal delivery method based on the student's learning history. The delivery unit can also analyze the student's learning history and select the most effective delivery method. This allows the delivery unit to select the optimal delivery method by referring to the student's past learning history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the student's learning history data into AI and have the AI ​​perform the selection of the delivery method.

[0104] The delivery unit can customize the delivery methods based on the student's current learning situation when providing learning materials. For example, if a student is concentrating, the delivery unit can select delivery methods to maintain concentration. If a student is tired, the delivery unit can also select delivery methods to encourage refreshment. If a student is excited, the delivery unit can also select delivery methods to help them regain composure. By customizing the delivery methods based on the student's current learning situation, it is possible to support more effective learning. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input student learning situation data into AI and have the AI ​​perform the customization of the delivery methods.

[0105] The distribution unit can estimate students' emotions and adjust the order in which learning materials are provided based on the estimated emotions. For example, if a student is feeling anxious, the distribution unit may prioritize providing materials that provide a sense of security. If a student is excited, the distribution unit may also prioritize providing materials that help them regain their composure. If a student is focused, the distribution unit may also prioritize providing materials that are helpful for learning. By adjusting the order in which learning materials are provided based on students' emotions, more effective learning can be supported. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit may input student emotion data into a generative AI and have the generative AI adjust the order in which learning materials are provided.

[0106] The distribution unit can select the optimal distribution method when providing educational materials, taking into account the student's geographical location. For example, if a student is in a specific region, the distribution unit can provide materials related to that region. If a student is traveling, the distribution unit can also provide materials related to their travel destination. If a student is participating in a specific event, the distribution unit can also provide materials related to that event. This allows the distribution unit to select the optimal distribution method by considering the student's geographical location. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the student's geographical location information into AI and have the AI ​​select the distribution method.

[0107] The content delivery unit can analyze students' social media activity and propose delivery methods when providing educational materials. For example, the content delivery unit can provide materials related to topics that students have shown interest in on social media. The content delivery unit can also provide materials based on the content of posts from accounts that students follow. The content delivery unit can also provide materials related to groups and communities that students participate in. This allows the content delivery unit to propose the most suitable delivery method by analyzing students' social media activity. Some or all of the above processing in the content delivery unit may be performed using AI, for example, or not using AI. For example, the content delivery unit can input students' social media data into AI and have the AI ​​propose delivery methods.

[0108] The monitoring unit can estimate a student's emotions and adjust monitoring standards based on the estimated emotions. For example, if a student is stressed, the monitoring unit may relax the monitoring standards. If a student is relaxed, the monitoring unit may tighten the monitoring standards. If a student is focused, the monitoring unit may perform more detailed monitoring. This allows for more appropriate monitoring by adjusting monitoring standards based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input student emotion data into a generative AI and have the generative AI adjust the monitoring standards.

[0109] The monitoring unit can optimize its monitoring algorithm by referring to past learning data during monitoring. For example, the monitoring unit optimizes the monitoring algorithm based on the student's past learning data. The monitoring unit can also set optimal monitoring criteria from the student's learning history. The monitoring unit can also analyze the student's learning data to improve the accuracy of monitoring. This allows the monitoring algorithm to be optimized by referring to past learning data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past learning data into AI and have the AI ​​perform the optimization of the monitoring algorithm.

[0110] The monitoring unit can adjust the frequency of monitoring according to the student's learning progress. For example, if a student's learning progress is behind, the monitoring unit can increase the frequency of monitoring. If a student's learning progress is on track, the monitoring unit can also decrease the frequency of monitoring. The monitoring unit can also adjust the frequency of monitoring in stages according to the student's learning progress. This allows for more effective monitoring by adjusting the frequency of monitoring according to the student's learning progress. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input student learning progress data into AI and have the AI ​​adjust the frequency of monitoring.

[0111] The monitoring unit can estimate a student's emotions and adjust how the monitoring results are displayed based on the estimated emotions. For example, if a student is feeling anxious, the monitoring unit can prioritize displaying monitoring results that provide reassurance. If a student is excited, the monitoring unit can also prioritize displaying monitoring results that help them regain their composure. If a student is focused, the monitoring unit can also prioritize displaying monitoring results that are helpful for learning. By adjusting how the monitoring results are displayed based on the student's emotions, more effective information can be provided. 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 monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input student emotion data into a generative AI and have the generative AI adjust how the monitoring results are displayed.

[0112] The monitoring unit can determine monitoring priorities based on students' learning history during monitoring. For example, the monitoring unit can prioritize monitoring important learning content based on students' learning history. The monitoring unit can also analyze students' learning history and set monitoring priorities. The monitoring unit can also adjust monitoring priorities in stages based on students' learning history. This allows for more effective monitoring by determining monitoring priorities based on students' learning history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input student learning history data into AI and have the AI ​​perform the determination of monitoring priorities.

[0113] The monitoring unit can improve the accuracy of monitoring by referring to relevant data during monitoring. For example, the monitoring unit performs monitoring by referring to data related to the topic being monitored. The monitoring unit can also improve the accuracy of monitoring by comparing the monitoring results with relevant data. The monitoring unit can also perform monitoring by referring to the latest research results during monitoring. This allows for improved monitoring accuracy by referring to relevant data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant data into AI and have AI perform the improvement of monitoring accuracy.

[0114] The testing unit can estimate students' emotions and adjust the test content based on those emotions. For example, if a student is stressed, the testing unit can ease the difficulty of the test. If a student is relaxed, the testing unit can increase the difficulty of the test. If a student is focused, the testing unit can conduct a more detailed test. This allows for more appropriate testing by adjusting the test content based on students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input student emotion data into a generative AI and have the generative AI adjust the test content.

[0115] The testing department can select the optimal test content by referring to students' past test results when administering a test. For example, the testing department can select the optimal test content based on students' past test results. The testing department can also select the optimal test content from students' test history. The testing department can also analyze students' test results and select the most effective test content. In this way, the optimal test content can be selected by referring to students' past test results. Some or all of the above processes in the testing department may be performed using AI, for example, or without AI. For example, the testing department can input students' past test result data into AI and have the AI ​​perform the selection of test content.

[0116] The testing unit can customize the difficulty level of a test based on the student's current learning progress when administering the test. For example, if a student is advanced in their learning, the testing unit can increase the difficulty level. If a student is behind in their learning, the testing unit can also ease the difficulty level. The testing unit can also adjust the difficulty level of the test in stages according to the student's learning progress. This allows for more appropriate testing by customizing the difficulty level based on the student's current learning progress. Some or all of the above processes in the testing unit may be performed using AI, for example, or not. For example, the testing unit can input student learning progress data into an AI and have the AI ​​perform the customization of the test difficulty level.

[0117] The testing unit can estimate students' emotions and adjust the order in which tests are administered based on those estimated emotions. For example, if a student is feeling anxious, the testing unit can prioritize tests that provide reassurance. If a student is excited, the testing unit can also prioritize tests that help them regain their composure. If a student is focused, the testing unit can also prioritize tests that are helpful for learning. By adjusting the order of tests based on students' emotions, a more effective test can be conducted. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using AI, or not using AI. For example, the testing unit can input student emotion data into a generative AI and have the generative AI adjust the order in which tests are administered.

[0118] The testing department can select the optimal testing method when administering a test, taking into account the students' geographical location. For example, if a student is in a specific region, the testing department can administer a test relevant to that region. If a student is traveling, the testing department can administer a test relevant to their travel destination. If a student is participating in a specific event, the testing department can administer a test relevant to that event. This allows the testing department to select the optimal testing method by considering the students' geographical location. Some or all of the above processing in the testing department may be performed using AI, for example, or not. For example, the testing department can input the students' geographical location information into an AI and have the AI ​​select the testing method.

[0119] The testing department can analyze students' social media activity and propose test content when administering a test. For example, the testing department can conduct tests related to topics that students have shown interest in on social media. The testing department can also conduct tests based on the content of posts from accounts that students follow. The testing department can also conduct tests related to groups and communities that students participate in. In this way, by analyzing students' social media activity, the testing department can propose the most suitable test content. Some or all of the above processes in the testing department may be performed using AI, for example, or not. For example, the testing department can input students' social media data into an AI and have the AI ​​propose test content.

[0120] The monitoring unit can estimate a student's emotions and adjust monitoring standards based on the estimated emotions. For example, if a student is stressed, the monitoring unit may relax the monitoring standards. If a student is relaxed, the monitoring unit may tighten the monitoring standards. If a student is focused, the monitoring unit may perform more detailed monitoring. This allows for more appropriate monitoring by adjusting monitoring standards based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input student emotion data into a generative AI and have the generative AI adjust the monitoring standards.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The analysis unit can suggest the optimal learning method based on each student's learning style. For example, it can suggest materials that make extensive use of diagrams and graphs for visual learners, materials that make extensive use of audio and video for auditory learners, and practical materials that allow tactile learners to learn by actually using their hands for hands-on activities. By suggesting the optimal learning method tailored to each student's learning style, the system can enhance learning effectiveness.

[0123] The monitoring unit can monitor students' learning progress in real time and automatically adjust learning plans as needed. For example, if a student is ahead of schedule, the learning plan can be accelerated. Conversely, if a student is behind schedule, the learning plan can be reviewed and adjusted to ensure progress. Furthermore, if a student is struggling in a particular area, supplementary materials tailored to that area can be provided. This allows for flexible adjustment of learning plans according to learning progress, supporting efficient learning.

[0124] The testing department can conduct regular quizzes to check students' understanding. For example, a quiz can be given at the end of each week's lessons, and the results can be used to assess understanding. Furthermore, unit tests can be administered upon completion of specific units to check overall understanding. Finally, a comprehensive test can be administered at the end of the semester to assess overall understanding. This allows for regular testing to monitor students' understanding and improve learning effectiveness.

[0125] The data collection unit can analyze students' learning trends based on their learning history. For example, it can analyze past test results and assignment submission records to identify areas where students excel and areas where they struggle. It can also analyze learning progress to understand the pace at which students are learning. Furthermore, it can analyze changes in students' learning styles and motivation to suggest optimal learning methods. In this way, by analyzing learning trends based on students' learning history, more effective learning support can be provided.

[0126] The analysis unit can predict future learning plans based on students' learning history. For example, it can analyze past learning data to predict future learning progress. It can also predict learning progress in specific subjects and create learning plans tailored to those subjects. Furthermore, it can predict changes in students' learning styles and motivations and suggest optimal learning methods. By predicting future learning plans, it is possible to provide more effective learning support.

[0127] The data collection unit can estimate students' emotions and adjust the learning environment based on those estimates. For example, if a student is stressed, it can provide a relaxing environment. If a student is excited, it can provide an environment that enhances their concentration. Furthermore, if a student is tired, it can provide an environment for rest and refreshment. By adjusting the learning environment based on students' emotions, it can support more effective learning.

[0128] The analysis unit can estimate students' emotions and adjust the difficulty level of learning content based on those emotions. For example, if a student is stressed, the difficulty level of the learning content can be reduced. If a student is relaxed, the difficulty level can be increased. Furthermore, if a student is focused, more detailed learning content can be provided. In this way, by adjusting the difficulty level of learning content based on students' emotions, it is possible to support more effective learning.

[0129] The generation unit can estimate the student's emotions and adjust the format of the learning content based on those emotions. For example, if a student is relaxed, it can provide visually relaxing content. If a student is excited, it can provide visually stimulating content. Furthermore, if a student is focused, it can provide content that includes detailed explanations. By adjusting the format of learning content based on the student's emotions, it can support more effective learning.

[0130] The content delivery system can estimate students' emotions and adjust the timing of content delivery based on those estimates. For example, if a student is stressed, content can be delivered at a time that promotes relaxation. If a student is excited, content can be delivered at a time that enhances their concentration. Furthermore, if a student is tired, content can be delivered after a break or refreshment. By adjusting the timing of content delivery based on students' emotions, the system can support more effective learning.

[0131] The monitoring unit can estimate students' emotions and adjust the feedback method for learning progress based on those estimated emotions. For example, if a student is feeling anxious, it can provide reassuring feedback. If a student is excited, it can provide feedback to help them regain their composure. Furthermore, if a student is focused, it can provide detailed feedback. By adjusting the feedback method for learning progress based on students' emotions, it can support more effective learning.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The data collection department collects information about students' personalities and proficiency levels. For example, it collects information about students' personality traits, learning styles, test scores, assignment completion, interests, and motivation. The data collection department also collects student survey results and behavioral data. Step 2: The analysis unit analyzes the information collected by the collection unit to identify the "causes of inability to solve" and the "optimal path to understanding the solution." For example, it identifies causes such as students' lack of understanding, misunderstandings, or calculation errors, and identifies the optimal path to understanding the solution, such as providing step-by-step explanations and related knowledge. Step 3: The generation unit generates optimal educational content based on the information identified by the analysis unit. For example, it generates personalized learning materials and adaptive learning content, and generates the optimal learning materials based on the analysis results. Step 4: The delivery unit provides the educational content generated by the generation unit to the students. For example, the educational content is provided through methods such as online distribution or distribution of printed materials.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, monitoring unit, and testing unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect information on the student's personality and proficiency level, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to identify the "cause of inability to solve" and the "optimal path to understanding the solution." The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates optimal educational content based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the smart device 14, and provides the generated educational content to the student. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and monitors learning progress in real time and automatically generates teaching materials as needed. The testing section is implemented, for example, by the specific processing unit 290 of the data processing device 12, which conducts review tests to confirm the students' level of understanding. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, monitoring unit, and testing unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect information on the student's personality and proficiency level, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected information to identify the "cause of inability to solve" and the "optimal path to understanding the solution." The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates optimal educational content based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the generated educational content to the student. The monitoring unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and monitors learning progress in real time and automatically generates teaching materials as needed. The testing section is implemented, for example, by the specific processing unit 290 of the data processing device 12, which conducts review tests to confirm the students' level of understanding. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, monitoring unit, and testing unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information on the student's personality and proficiency level, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to identify the "cause of inability to solve" and the "optimal path to understanding the solution." The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates optimal educational content based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, and provides the generated educational content to the student. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and monitors learning progress in real time and automatically generates teaching materials as needed. The testing section is implemented, for example, by the specific processing unit 290 of the data processing device 12, which conducts review tests to confirm the students' level of understanding. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, monitoring unit, and testing unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information on the students' individuality and proficiency, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to identify the "cause of inability to solve" and the "optimal path to understanding the solution." The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates optimal educational content based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the robot 414, and provides the generated educational content to the students. The monitoring unit is implemented in the specific processing unit 290 of the data processing unit 12, and monitors learning progress in real time and automatically generates teaching materials as needed. The testing section is implemented, for example, by the specific processing unit 290 of the data processing device 12, which conducts review tests to confirm the students' level of understanding. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] (Note 1) The collection department collects information about students' individuality and proficiency levels, An analysis unit analyzes the information collected by the aforementioned collection unit to identify the "cause of the inability to solve" and the "optimal path to understanding the solution," A generation unit that generates optimal educational content based on the information identified by the analysis unit, The system includes a providing unit that provides the educational content generated by the generation unit to students. A system characterized by the following features. (Note 2) It also includes a monitoring unit that monitors learning progress in real time and automatically generates learning materials as needed. The system described in Appendix 1, characterized by the features described herein. (Note 3) We will further enhance our testing department by conducting review tests to check students' understanding. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect information about students' interests and motivations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Based on the collected information, we will consider students' interests and motivations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Based on the analysis results, the optimal teaching materials are generated. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide the generated teaching materials to students. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate students' emotions and adjust the types of information we collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze students' past learning history and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, filter it based on the student's current learning environment and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates students' emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information, taking into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, analyze students' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the students' emotions and adjust the analysis criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, consider the relationships between students to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the student's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates students' emotions and adjusts the display order of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the geographical distribution of students will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is We estimate students' emotions and adjust the way we present the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is We estimate students' emotions and adjust the way we present the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating learning materials, adjust the level of detail based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating learning materials, different generation algorithms are applied depending on the learning category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates students' emotions and adjusts the length of the generated learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating learning materials, prioritize the materials based on the submission deadlines for the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating learning materials, the order of the materials is adjusted based on the relevance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates students' emotions and adjusts the method of providing learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing learning materials, the optimal delivery method is selected by referring to the student's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing learning materials, customize the delivery method based on the students' current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The system estimates students' emotions and adjusts the order in which learning materials are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing educational materials, the optimal delivery method will be selected considering the students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing educational materials, we analyze students' social media activity and propose methods for providing the materials. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned monitoring unit, Estimate students' emotions and adjust monitoring standards based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned monitoring unit, During monitoring, the monitoring algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned monitoring unit, During monitoring, adjust the frequency of monitoring according to the student's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned monitoring unit, The system estimates students' emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned monitoring unit, During monitoring, the monitoring priority is determined based on the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned monitoring unit, During monitoring, refer to relevant data to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned test unit is The system estimates students' emotions and adjusts the test content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned test unit is When administering a test, the optimal test content is selected by referring to the students' past test results. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned test unit is When administering a test, customize the difficulty level based on the students' current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned test unit is The system estimates students' emotions and adjusts the order in which tests are administered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned test unit is When administering a test, the most suitable testing method will be selected, taking into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned test unit is During the test administration, we analyze students' social media activity and propose test content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0206] 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 collection department collects information about students' individuality and proficiency levels, An analysis unit analyzes the information collected by the aforementioned collection unit to identify the optimal path for understanding the cause of the problem and the solution method, A generation unit that generates optimal educational content based on the information identified by the analysis unit, The system includes a providing unit that provides the educational content generated by the generation unit to students. A system characterized by the following features.

2. It also includes a monitoring unit that monitors learning progress in real time and automatically generates learning materials as needed. The system according to feature 1.

3. We will further enhance our testing department by conducting review tests to check students' understanding. The system according to feature 1.

4. The aforementioned collection unit is Collect information about students' interests and motivations. The system according to feature 1.

5. The aforementioned analysis unit, Based on the collected information, we will consider students' interests and motivations. The system according to feature 1.

6. The generating unit is Based on the analysis results, the optimal teaching materials are generated. The system according to feature 1.

7. The aforementioned supply unit is, Provide the generated teaching materials to students. The system according to feature 1.

8. The aforementioned collection unit is We estimate students' emotions and adjust the types of information we collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze students' past learning history and select the most suitable information gathering method. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, filter it based on the student's current learning environment and areas of interest. The system according to feature 1.

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