system

Abstract

The system according to this embodiment aims to provide individualized instruction and learning curricula based on students' level of understanding and progress. [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 student learning data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates individual instruction and learning curricula based on the analysis results obtained by the analysis unit. The provision unit provides the curriculum and instruction generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, individualized guidance and provision of learning curricula based on the understanding level and progress of students have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individualized guidance and learning curricula based on the understanding level and progress of students.

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 student learning data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the analysis unit. The provision unit provides the curriculum and instruction generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide individualized instruction and learning curricula based on students' level of understanding and progress. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls 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 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that analyzes students' understanding and progress and provides individualized instruction and learning curricula. This AI agent system collects students' learning data in real time and analyzes their understanding and progress. Next, based on the analysis results, it provides the most suitable learning curriculum and instruction for each student. This reduces the burden on teachers and enables learning support tailored to each student. For example, the AI ​​agent system collects data such as questions answered by students, assignments submitted, and learning progress. For example, it collects the correct answer rate of questions answered online by students and the evaluation results of submitted assignments. This allows teachers to grasp the students' understanding and progress. Next, the AI ​​agent system analyzes the collected data. The AI ​​agent system uses natural language processing (NLP) to analyze the answers entered by students and uses machine learning to analyze and predict learning progress. For example, it determines the student's understanding based on the correct answer rate of questions answered by students and the evaluation results of submitted assignments. This allows teachers to grasp the students' learning situation in detail. Furthermore, based on the analysis results, the AI ​​agent system generates individualized instruction and learning curricula. The AI ​​agent system provides optimal teaching methods and practice problems according to each student's level of understanding and progress. For example, for areas where understanding is weak, it provides additional practice problems and instruction to deepen understanding. It also provides a curriculum to adjust the learning pace for students who are falling behind. This enables personalized learning support tailored to each student. Furthermore, the AI ​​agent system also provides feedback and suggestions to teachers. For example, it reports students' understanding and progress to teachers and suggests necessary improvements to teaching methods and curricula. This reduces the burden on teachers and enables more effective instruction. This system allows for real-time monitoring of students' understanding and progress, enabling the provision of individualized instruction and learning curricula. This reduces the burden on teachers and allows for learning support tailored to each student. It also enables effective learning support even in remote learning environments, realizing instruction that matches each student's learning pace and level of understanding.This allows the AI ​​agent system to analyze students' understanding and progress, and provide individualized instruction and learning curricula, thereby reducing the burden on teachers and providing learning support tailored to each student.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects student learning data. The collection unit collects data such as questions answered by students, assignments submitted, and learning progress. The collection unit collects data such as the correct answer rate for questions answered online by students and the evaluation results of submitted assignments. The collection unit can also acquire data from a learning management system (LMS), for example. The collection unit can also monitor students' learning behavior in real time using sensors, for example. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes student input answers using natural language processing (NLP), for example. The analysis unit analyzes and predicts learning progress using machine learning algorithms, for example. The analysis unit can also grasp data trends using statistical analysis, for example. The analysis unit can also group students' understanding levels using clustering technology, for example. The generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the analysis unit. The generation unit provides, for example, optimal teaching methods and practice problems according to the student's level of understanding and progress. The generation unit provides, for example, additional practice problems for areas where the student's understanding is low. The generation unit provides, for example, a curriculum to adjust the learning pace for students who are falling behind. The generation unit can also, for example, automatically generate a curriculum tailored to the student's learning situation using AI. The generation unit can also, for example, suggest teaching methods tailored to the student's level of understanding using AI. The delivery unit provides the curriculum and instruction generated by the generation unit. The delivery unit provides the curriculum and instruction to students, for example, through an online platform. The delivery unit provides, for example, feedback and suggestions to teachers. The delivery unit can also, for example, monitor the student's learning situation in real time using AI and provide instruction at the appropriate time. The delivery unit can also, for example, automatically generate feedback tailored to the student's learning situation using AI. As a result, the AI ​​agent system according to the embodiment can analyze the student's level of understanding and progress, and provide individualized instruction and learning curricula, thereby reducing the burden on teachers and providing learning support tailored to each student.

[0030] The data collection unit collects student learning data. Specifically, it collects data such as the questions students answer, the assignments they submit, and their learning progress. For example, it collects the correct answer rate for questions students answer online and the evaluation results of submitted assignments. This allows the data collection unit to understand students' learning status in detail. Furthermore, the data collection unit can also obtain data from the Learning Management System (LMS). The LMS is a system that centrally manages students' learning activities, and the data collection unit can obtain students' learning history and performance data from this system. This allows the data collection unit to understand students' learning status in more detail based on their learning history and performance data. In addition, the data collection unit can monitor students' learning behavior in real time using sensors. For example, sensors can be installed on devices that students use while learning to monitor their posture, eye movements, and level of concentration. This allows the data collection unit to understand students' learning behavior in real time and evaluate the efficiency and concentration of their learning. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it uses natural language processing (NLP) to analyze student inputs. By using NLP technology, it can automatically understand the content of students' responses and grasp the accuracy rate and trends in incorrect answers. Furthermore, the analysis unit uses machine learning algorithms to analyze and predict learning progress. For example, based on students' past learning data, it can predict future learning progress and create appropriate learning plans. It can also grasp data trends using statistical analysis. For example, it can statistically analyze students' performance data to grasp trends in understanding in specific subjects or fields. Furthermore, it can group students' understanding levels using clustering technology. This allows the analysis unit to create groups according to students' understanding levels and optimize individualized instruction and curricula. By utilizing these technologies, the analysis unit can analyze students' learning situations in detail and build a foundation for providing optimal learning support to each student. In addition, the analysis unit can utilize past data and statistical information to evaluate long-term learning effectiveness and perform trend analysis. For example, past learning data can be used to evaluate the effectiveness of specific learning methods and materials, helping to improve future learning plans. This allows the analysis unit to not only grasp the situation in real time but also to evaluate and improve long-term learning effectiveness, thereby improving the reliability and effectiveness of the entire system.

[0032] The generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the analysis unit. Specifically, it provides optimal teaching methods and practice problems according to the student's level of understanding and progress. For example, it provides additional practice problems for areas where understanding is low, and provides a curriculum to adjust the learning pace for students who are falling behind. The generation unit can also automatically generate curricula tailored to the student's learning situation using AI. For example, the AI ​​generates and provides an optimal learning curriculum based on the student's past learning data and current learning situation. The AI ​​can also suggest teaching methods according to the student's level of understanding. For example, it provides more advanced problems to students with high levels of understanding and basic problems to students with low levels of understanding. In this way, the generation unit can provide optimal learning support for each individual student. Furthermore, the generation unit can continuously improve the generated curricula and teaching methods. For example, it evaluates the effectiveness of the curriculum and teaching methods based on student feedback and makes corrections as needed. In this way, the generation unit can always provide highly accurate learning support based on the latest information and improve the overall effectiveness of the system.

[0033] The provisioning unit provides the curriculum and instruction generated by the generation unit. Specifically, it provides the curriculum and instruction to students through an online platform. For example, on the online platform, students can check the curriculum and receive instruction according to their learning progress. The provisioning unit also provides feedback and suggestions to teachers. For example, based on the students' learning status, it can suggest improvements to teaching methods and additional instruction to teachers. Furthermore, the provisioning unit can use AI to monitor students' learning status in real time and provide instruction at the appropriate time. For example, if a student encounters difficulties while learning, the AI ​​will automatically provide appropriate instruction to support the student's learning. The provisioning unit can also use AI to automatically generate feedback according to the student's learning status. For example, the AI ​​will automatically generate and provide feedback to the student for questions that the student has answered. This allows the provisioning unit to quickly provide appropriate instruction to each student and maximize learning effectiveness. Furthermore, the provisioning unit can collect user feedback and continuously improve the accuracy and effectiveness of the instruction content. For example, it can review and improve the instruction content based on feedback from students who have received instruction. In addition, the provisioning unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through online platform notifications but also via email and messaging apps. This allows the service provider to deliver instruction quickly and reliably to students, maximizing learning effectiveness.

[0034] The service provider includes a suggestion unit that provides feedback and suggestions to teachers. For example, the service provider can report to teachers on students' understanding and progress. For example, the service provider can suggest improvements to teaching methods and curriculum to teachers. For example, the service provider can use AI to automatically generate feedback to teachers based on students' learning progress. For example, the service provider can use AI to automatically generate suggestions for teaching methods to teachers based on students' learning progress. By providing feedback and suggestions to teachers, the service provider reduces their burden and enables more effective instruction.

[0035] The generation unit includes a practice problem provision unit that provides additional practice problems for areas where students have a low level of understanding. For example, the generation unit provides additional practice problems for areas where students have a low level of understanding. The generation unit can also, for example, use AI to automatically generate practice problems according to the student's level of understanding. The generation unit can also, for example, use AI to adjust the difficulty level of the practice problems according to the student's level of understanding. The generation unit can also, for example, use AI to adjust the frequency of practice problems presented according to the student's level of understanding. This allows students to deepen their understanding by providing additional practice problems for areas where they have a low level of understanding.

[0036] The generation unit includes a curriculum adjustment unit that provides a curriculum for adjusting the learning pace. For example, the generation unit provides a curriculum for adjusting the learning pace for students who are falling behind. The generation unit can also automatically generate a curriculum according to the student's learning pace using AI. For example, the generation unit can also adjust the progress speed of the curriculum according to the student's learning pace using AI. For example, the generation unit can also adjust the content of the curriculum according to the student's learning pace using AI. This makes it possible to provide learning support tailored to each student by providing a curriculum for adjusting the learning pace.

[0037] The data collection unit analyzes students' past learning history and selects the optimal data collection method. For example, the data collection unit prioritizes collecting question formats in which students have previously achieved high correct answer rates. For example, the data collection unit focuses on collecting question formats in which students have previously struggled. For example, the data collection unit analyzes from students' past learning history to identify periods of heightened concentration and collects data during those periods. This allows the optimal data collection method to be selected by analyzing students' 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 students' past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit filters learning data based on the student's current learning environment and areas of interest. For example, the data collection unit prioritizes collecting problems in areas of interest to the student. For example, it collects problems that require concentration when the student is studying in a quiet environment. For example, it collects problems that require collaboration when the student is studying in a group. By filtering the data based on the student's current learning environment and areas of interest, more relevant data can be collected. 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 a generating AI and have the generating AI perform the data filtering.

[0039] The data collection unit prioritizes the collection of highly relevant data, taking into account the student's geographical location when collecting learning data. For example, when a student is at school, the data collection unit prioritizes the collection of data related to the school curriculum. For example, when a student is at home, the data collection unit prioritizes the collection of data suitable for home study. For example, when a student is in the library, the data collection unit prioritizes the collection of data that should be studied in a quiet environment. This allows for the priority collection of highly relevant data 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 data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0040] The data collection unit analyzes students' social media activity and collects relevant data when collecting learning data. For example, the data collection unit collects data related to topics that students have shown interest in on social media. For example, the data collection unit collects data based on learning content that students have shared on social media. For example, the data collection unit collects data based on information about educational accounts that students follow on social media. This allows relevant data 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 without AI. For example, the data collection unit can input students' social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0041] The analysis unit adjusts the level of detail of the analysis based on the importance of the training data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the training data, a more appropriate analysis can be performed. 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 importance data of the training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit applies different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit applies a numerical analysis algorithm to mathematical data. For example, the analysis unit applies a natural language processing algorithm to language data. For example, the analysis unit applies an experimental results analysis algorithm to science data. By applying different analysis algorithms depending on the category of the training data, more appropriate analysis can be performed. 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 category data of the training data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0043] The analysis unit determines the priority of analysis based on the submission timing of the training data. For example, the analysis unit prioritizes the analysis of data with an approaching submission deadline. For example, the analysis unit postpones the analysis of data with a distant submission deadline. For example, the analysis unit performs special analysis on data whose submission deadline has passed. By determining the priority of analysis based on the submission timing of the training data, more appropriate analysis can be performed. 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 training data submission timing data into a generating AI and have the generating AI perform the determination of analysis priority.

[0044] The analysis unit adjusts the order of analysis based on the relevance of the training data during the analysis. For example, the analysis unit prioritizes analyzing highly relevant data. For example, it postpones analyzing less relevant data. For example, it performs analysis on data with a moderate level of relevance in an appropriate order. By adjusting the order of analysis based on the relevance of the training data, more appropriate analysis can be performed. 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 relevance data of the training data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The generation unit adjusts the level of detail of the generated curriculum based on the importance of the training data during generation. For example, the generation unit generates a detailed curriculum for data with high importance. For example, the generation unit generates a simplified curriculum for data with low importance. For example, the generation unit generates a curriculum with an appropriate level of detail for data with moderate importance. By adjusting the level of detail of the generated curriculum based on the importance of the training data, more appropriate curricula and instruction can be provided. 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 training data importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated curriculum.

[0046] The generation unit applies different generation algorithms depending on the category of the training data during generation. For example, the generation unit applies a numerical analysis algorithm to mathematical data. For example, the generation unit applies a natural language processing algorithm to language data. For example, the generation unit applies an experimental results analysis algorithm to science data. By applying different generation algorithms depending on the category of the training data, it is possible to provide more appropriate curricula and instruction. 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 the category data of the training data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0047] The generation unit determines the generation priority based on the submission timing of the training data. For example, the generation unit prioritizes generating data with an approaching submission deadline. For example, it postpones generating data with a distant submission deadline. For example, the generation unit generates a special curriculum for data whose submission deadline has passed. This allows for the provision of more appropriate curricula and instruction by determining the generation priority based on the submission timing of the training data. 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 training data submission timing data into a generation AI and have the generation AI determine the generation priority.

[0048] The generation unit adjusts the generation order based on the relevance of the training data during generation. For example, the generation unit prioritizes generating highly relevant data. For example, it postpones generating less relevant data. For example, it generates data with a moderate level of relevance in an appropriate order. By adjusting the generation order based on the relevance of the training data, it is possible to provide a more appropriate curriculum and instruction. 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 the relevance data of the training data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0049] The provisioning unit adjusts the level of detail provided based on the importance of the training data at the time of provision. For example, the provisioning unit provides a detailed curriculum for data of high importance. For example, the provisioning unit provides a simplified curriculum for data of low importance. For example, the provisioning unit provides a curriculum with an appropriate level of detail for data of medium importance. In this way, by adjusting the level of detail provided based on the importance of the training data, more appropriate curricula and instruction can be provided. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without using AI. For example, the provisioning unit can input training data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0050] The data provider applies different provisioning algorithms depending on the category of the learning data at the time of provision. For example, the data provider applies a numerical analysis algorithm to mathematical data. For example, the data provider applies a natural language processing algorithm to language data. For example, the data provider applies an experimental results analysis algorithm to science data. By applying different provisioning algorithms depending on the category of the learning data, more appropriate curricula and instruction can be provided. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input the category data of the learning data into a generating AI and have the generating AI execute the application of different provisioning algorithms.

[0051] The provisioning unit determines the priority of provision based on the submission timing of the learning data. For example, the provisioning unit prioritizes providing data with an approaching submission deadline. For example, the provisioning unit postpones providing data with a distant submission deadline. For example, the provisioning unit provides a special curriculum for data whose submission deadline has passed. This allows for the provision of more appropriate curricula and instruction by determining the priority of provision based on the submission timing of the learning data. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input learning data submission timing data into a generating AI and have the generating AI perform the determination of provision priority.

[0052] The data delivery unit adjusts the order of delivery based on the relevance of the training data. For example, the delivery unit prioritizes providing highly relevant data. For example, the delivery unit postpones providing less relevant data. For example, the delivery unit provides data of moderate relevance in an appropriate order. By adjusting the order of delivery based on the relevance of the training data, it is possible to provide a more appropriate curriculum and instruction. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance data of the training data into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0053] The proposal unit adjusts the level of detail of its proposals based on the importance of the training data. For example, the proposal unit provides detailed proposals for highly important data, simplified proposals for less important data, and proposals with a moderate level of detail for moderately important data. By adjusting the level of detail of the proposals based on the importance of the training data, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance data of the training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0054] The proposal unit determines the priority of proposals based on the submission timing of the training data. For example, the proposal unit prioritizes proposals for data with approaching submission deadlines. For example, it postpones proposals for data with distant submission deadlines. For example, the proposal unit makes special proposals for data whose submission deadlines have passed. By prioritizing proposals based on the submission timing of the training data, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input training data submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0055] The practice problem provider adjusts the level of detail of the practice problems based on the importance of the training data when providing them. For example, the practice problem provider provides detailed practice problems for data with high importance. For example, the practice problem provider provides simple practice problems for data with low importance. For example, the practice problem provider provides practice problems with an appropriate level of detail for data with moderate importance. By adjusting the level of detail of the practice problems based on the importance of the training data, more appropriate practice problems can be provided. Some or all of the above processing in the practice problem provider may be performed using AI, for example, or without using AI. For example, the practice problem provider can input the importance data of the training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the practice problems.

[0056] The practice problem provider determines the priority of practice problems based on the submission timing of the training data when providing them. For example, the practice problem provider prioritizes providing data with an approaching submission deadline. For example, it postpones providing data with a distant submission deadline. For example, it provides special practice problems for data whose submission deadline has passed. In this way, by determining the priority of practice problems based on the submission timing of the training data, more appropriate practice problems can be provided. Some or all of the above processing in the practice problem provider may be performed using AI, for example, or without AI. For example, the practice problem provider can input training data submission timing data into a generating AI and have the generating AI perform the determination of practice problem priorities.

[0057] The curriculum adjustment unit adjusts the level of detail in the curriculum based on the importance of the learning data during curriculum adjustment. For example, the curriculum adjustment unit provides a detailed curriculum for data with high importance. For example, the curriculum adjustment unit provides a simplified curriculum for data with low importance. For example, the curriculum adjustment unit provides a curriculum with an appropriate level of detail for data with moderate importance. In this way, a more appropriate curriculum can be provided by adjusting the level of detail in the curriculum based on the importance of the learning data. Some or all of the above processing in the curriculum adjustment unit may be performed using AI, for example, or without using AI. For example, the curriculum adjustment unit can input learning data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the curriculum.

[0058] The curriculum adjustment unit determines curriculum priorities based on the submission timing of learning data during curriculum adjustment. For example, the curriculum adjustment unit prioritizes providing data with approaching submission deadlines. For example, the curriculum adjustment unit postpones data with distant submission deadlines. For example, the curriculum adjustment unit provides a special curriculum for data whose submission deadlines have passed. This allows for the provision of a more appropriate curriculum by determining curriculum priorities based on the submission timing of learning data. Some or all of the above processing in the curriculum adjustment unit may be performed using AI, for example, or without AI. For example, the curriculum adjustment unit can input learning data submission timing data into a generating AI and have the generating AI perform the determination of curriculum priorities.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The analysis unit can apply different analysis methods to student learning data based on its content. For example, numerical analysis methods can be applied to mathematical data, and natural language processing methods to language data. Experimental result analysis methods can also be applied to science data. This enables optimal analysis tailored to the content of the learning data, resulting in more accurate analysis results.

[0061] The data collection unit can adjust its data collection methods based on the student's learning environment when collecting student learning data. For example, if a student is studying at home, it can collect data suitable for home study; if a student is studying at school, it can collect data related to the school curriculum. Furthermore, if a student is studying in the library, it can collect data that should be studied in a quiet environment. This enables optimal data collection tailored to each student's learning environment.

[0062] The generation unit can adjust the curriculum content based on students' learning styles when generating a curriculum based on their learning data. For example, it can provide a curriculum that makes extensive use of diagrams and graphs for students with a visual learning style, and a curriculum that makes extensive use of audio and video for students with an auditory learning style. It can also provide a curriculum that includes many practical tasks for students with an experiential learning style. This allows for the provision of an optimal curriculum tailored to each student's learning style.

[0063] The delivery department can adjust the delivery method of generated curricula and instruction based on the student's learning history. For example, it can prioritize providing question formats in which the student has previously achieved high correct answer rates, and focus on providing instruction in question formats in which the student struggled. It can also analyze from the student's past learning history which times of day are when their concentration is highest, and deliver the curriculum and instruction during those times. This enables the delivery method to be optimized based on the student's learning history.

[0064] The data collection unit can analyze students' social media activity and collect relevant data when gathering student learning data. For example, it can collect data related to topics students have shown interest in on social media and collect data based on learning content they have shared on social media. It can also collect data based on information from educational accounts that students follow on social media. This enables optimal data collection based on students' social media activity.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects student learning data. For example, it collects data such as questions answered by students, assignments submitted, and learning progress. The data collection unit collects data such as the correct answer rate for questions answered online and the evaluation results of submitted assignments. It can also acquire data from the Learning Management System (LMS) and monitor students' learning behavior in real time using sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses natural language processing (NLP) to analyze student inputs and machine learning algorithms to analyze and predict learning progress. Furthermore, it can use statistical analysis to understand data trends and clustering techniques to group students by their level of understanding. Step 3: The generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the analysis unit. For example, it provides optimal teaching methods and practice problems according to the student's level of understanding and progress, and provides additional practice problems for areas where understanding is weak. For students who are falling behind, it provides a curriculum to adjust the learning pace, and can also use AI to automatically generate curricula and teaching methods that are tailored to the student's learning situation. Step 4: The delivery unit provides the curriculum and instruction generated by the generation unit. For example, it provides the curriculum and instruction to students through an online platform and provides feedback and suggestions to teachers. Furthermore, it can use AI to monitor students' learning progress in real time, provide instruction at the appropriate time, and automatically generate feedback tailored to the students' learning progress.

[0067] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes students' understanding and progress and provides individualized instruction and learning curricula. This AI agent system collects students' learning data in real time and analyzes their understanding and progress. Next, based on the analysis results, it provides the most suitable learning curriculum and instruction for each student. This reduces the burden on teachers and enables learning support tailored to each student. For example, the AI ​​agent system collects data such as questions answered by students, assignments submitted, and learning progress. For example, it collects the correct answer rate of questions answered online by students and the evaluation results of submitted assignments. This allows teachers to grasp the students' understanding and progress. Next, the AI ​​agent system analyzes the collected data. The AI ​​agent system uses natural language processing (NLP) to analyze the answers entered by students and uses machine learning to analyze and predict learning progress. For example, it determines the student's understanding based on the correct answer rate of questions answered by students and the evaluation results of submitted assignments. This allows teachers to grasp the students' learning situation in detail. Furthermore, based on the analysis results, the AI ​​agent system generates individualized instruction and learning curricula. The AI ​​agent system provides optimal teaching methods and practice problems according to each student's level of understanding and progress. For example, for areas where understanding is weak, it provides additional practice problems and instruction to deepen understanding. It also provides a curriculum to adjust the learning pace for students who are falling behind. This enables personalized learning support tailored to each student. Furthermore, the AI ​​agent system also provides feedback and suggestions to teachers. For example, it reports students' understanding and progress to teachers and suggests necessary improvements to teaching methods and curricula. This reduces the burden on teachers and enables more effective instruction. This system allows for real-time monitoring of students' understanding and progress, enabling the provision of individualized instruction and learning curricula. This reduces the burden on teachers and allows for learning support tailored to each student. It also enables effective learning support even in remote learning environments, realizing instruction that matches each student's learning pace and level of understanding.This allows the AI ​​agent system to analyze students' understanding and progress, and provide individualized instruction and learning curricula, thereby reducing the burden on teachers and providing learning support tailored to each student.

[0068] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects student learning data. The collection unit collects data such as questions answered by students, assignments submitted, and learning progress. The collection unit collects data such as the correct answer rate for questions answered online by students and the evaluation results of submitted assignments. The collection unit can also acquire data from a learning management system (LMS), for example. The collection unit can also monitor students' learning behavior in real time using sensors, for example. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes student input answers using natural language processing (NLP), for example. The analysis unit analyzes and predicts learning progress using machine learning algorithms, for example. The analysis unit can also grasp data trends using statistical analysis, for example. The analysis unit can also group students' understanding levels using clustering technology, for example. The generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the analysis unit. The generation unit provides, for example, optimal teaching methods and practice problems according to the student's level of understanding and progress. The generation unit provides, for example, additional practice problems for areas where the student's understanding is low. The generation unit provides, for example, a curriculum to adjust the learning pace for students who are falling behind. The generation unit can also, for example, automatically generate a curriculum tailored to the student's learning situation using AI. The generation unit can also, for example, suggest teaching methods tailored to the student's level of understanding using AI. The delivery unit provides the curriculum and instruction generated by the generation unit. The delivery unit provides the curriculum and instruction to students, for example, through an online platform. The delivery unit provides, for example, feedback and suggestions to teachers. The delivery unit can also, for example, monitor the student's learning situation in real time using AI and provide instruction at the appropriate time. The delivery unit can also, for example, automatically generate feedback tailored to the student's learning situation using AI. As a result, the AI ​​agent system according to the embodiment can analyze the student's level of understanding and progress, and provide individualized instruction and learning curricula, thereby reducing the burden on teachers and providing learning support tailored to each student.

[0069] The data collection unit collects student learning data. Specifically, it collects data such as the questions students answer, the assignments they submit, and their learning progress. For example, it collects the correct answer rate for questions students answer online and the evaluation results of submitted assignments. This allows the data collection unit to understand students' learning status in detail. Furthermore, the data collection unit can also obtain data from the Learning Management System (LMS). The LMS is a system that centrally manages students' learning activities, and the data collection unit can obtain students' learning history and performance data from this system. This allows the data collection unit to understand students' learning status in more detail based on their learning history and performance data. In addition, the data collection unit can monitor students' learning behavior in real time using sensors. For example, sensors can be installed on devices that students use while learning to monitor their posture, eye movements, and level of concentration. This allows the data collection unit to understand students' learning behavior in real time and evaluate the efficiency and concentration of their learning. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0070] The analysis unit analyzes the data collected by the data collection unit. Specifically, it uses natural language processing (NLP) to analyze student inputs. By using NLP technology, it can automatically understand the content of students' responses and grasp the accuracy rate and trends in incorrect answers. Furthermore, the analysis unit uses machine learning algorithms to analyze and predict learning progress. For example, based on students' past learning data, it can predict future learning progress and create appropriate learning plans. It can also grasp data trends using statistical analysis. For example, it can statistically analyze students' performance data to grasp trends in understanding in specific subjects or fields. Furthermore, it can group students' understanding levels using clustering technology. This allows the analysis unit to create groups according to students' understanding levels and optimize individualized instruction and curricula. By utilizing these technologies, the analysis unit can analyze students' learning situations in detail and build a foundation for providing optimal learning support to each student. In addition, the analysis unit can utilize past data and statistical information to evaluate long-term learning effectiveness and perform trend analysis. For example, past learning data can be used to evaluate the effectiveness of specific learning methods and materials, helping to improve future learning plans. This allows the analysis unit to not only grasp the situation in real time but also to evaluate and improve long-term learning effectiveness, thereby improving the reliability and effectiveness of the entire system.

[0071] The generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the analysis unit. Specifically, it provides optimal teaching methods and practice problems according to the student's level of understanding and progress. For example, it provides additional practice problems for areas where understanding is low, and provides a curriculum to adjust the learning pace for students who are falling behind. The generation unit can also automatically generate curricula tailored to the student's learning situation using AI. For example, the AI ​​generates and provides an optimal learning curriculum based on the student's past learning data and current learning situation. The AI ​​can also suggest teaching methods according to the student's level of understanding. For example, it provides more advanced problems to students with high levels of understanding and basic problems to students with low levels of understanding. In this way, the generation unit can provide optimal learning support for each individual student. Furthermore, the generation unit can continuously improve the generated curricula and teaching methods. For example, it evaluates the effectiveness of the curriculum and teaching methods based on student feedback and makes corrections as needed. In this way, the generation unit can always provide highly accurate learning support based on the latest information and improve the overall effectiveness of the system.

[0072] The provisioning unit provides the curriculum and instruction generated by the generation unit. Specifically, it provides the curriculum and instruction to students through an online platform. For example, on the online platform, students can check the curriculum and receive instruction according to their learning progress. The provisioning unit also provides feedback and suggestions to teachers. For example, based on the students' learning status, it can suggest improvements to teaching methods and additional instruction to teachers. Furthermore, the provisioning unit can use AI to monitor students' learning status in real time and provide instruction at the appropriate time. For example, if a student encounters difficulties while learning, the AI ​​will automatically provide appropriate instruction to support the student's learning. The provisioning unit can also use AI to automatically generate feedback according to the student's learning status. For example, the AI ​​will automatically generate and provide feedback to the student for questions that the student has answered. This allows the provisioning unit to quickly provide appropriate instruction to each student and maximize learning effectiveness. Furthermore, the provisioning unit can collect user feedback and continuously improve the accuracy and effectiveness of the instruction content. For example, it can review and improve the instruction content based on feedback from students who have received instruction. In addition, the provisioning unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through online platform notifications but also via email and messaging apps. This allows the service provider to deliver instruction quickly and reliably to students, maximizing learning effectiveness.

[0073] The service provider includes a suggestion unit that provides feedback and suggestions to teachers. For example, the service provider can report to teachers on students' understanding and progress. For example, the service provider can suggest improvements to teaching methods and curriculum to teachers. For example, the service provider can use AI to automatically generate feedback to teachers based on students' learning progress. For example, the service provider can use AI to automatically generate suggestions for teaching methods to teachers based on students' learning progress. By providing feedback and suggestions to teachers, the service provider reduces their burden and enables more effective instruction.

[0074] The generation unit includes a practice problem provision unit that provides additional practice problems for areas where students have a low level of understanding. For example, the generation unit provides additional practice problems for areas where students have a low level of understanding. The generation unit can also, for example, use AI to automatically generate practice problems according to the student's level of understanding. The generation unit can also, for example, use AI to adjust the difficulty level of the practice problems according to the student's level of understanding. The generation unit can also, for example, use AI to adjust the frequency of practice problems presented according to the student's level of understanding. This allows students to deepen their understanding by providing additional practice problems for areas where they have a low level of understanding.

[0075] The generation unit includes a curriculum adjustment unit that provides a curriculum for adjusting the learning pace. For example, the generation unit provides a curriculum for adjusting the learning pace for students who are falling behind. The generation unit can also automatically generate a curriculum according to the student's learning pace using AI. For example, the generation unit can also adjust the progress speed of the curriculum according to the student's learning pace using AI. For example, the generation unit can also adjust the content of the curriculum according to the student's learning pace using AI. This makes it possible to provide learning support tailored to each student by providing a curriculum for adjusting the learning pace.

[0076] The data collection unit estimates the student's emotions and adjusts the timing of data collection based on the estimated emotions. For example, the data collection unit increases the frequency of data collection when the student is concentrating. For example, the data collection unit decreases the frequency of data collection when the student is tired. For example, when the student is stressed, the data collection unit allows time for relaxation before collecting data. By adjusting the timing of data collection according to the student's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 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 perform emotion estimation.

[0077] The data collection unit analyzes students' past learning history and selects the optimal data collection method. For example, the data collection unit prioritizes collecting question formats in which students have previously achieved high correct answer rates. For example, the data collection unit focuses on collecting question formats in which students have previously struggled. For example, the data collection unit analyzes from students' past learning history to identify periods of heightened concentration and collects data during those periods. This allows the optimal data collection method to be selected by analyzing students' 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 students' past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0078] The data collection unit filters learning data based on the student's current learning environment and areas of interest. For example, the data collection unit prioritizes collecting problems in areas of interest to the student. For example, it collects problems that require concentration when the student is studying in a quiet environment. For example, it collects problems that require collaboration when the student is studying in a group. By filtering the data based on the student's current learning environment and areas of interest, more relevant data can be collected. 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 a generating AI and have the generating AI perform the data filtering.

[0079] The data collection unit estimates the students' emotions and determines the priority of data to collect based on the estimated emotions. For example, when a student is relaxed, the data collection unit prioritizes collecting data on difficult problems. For example, when a student is tired, the data collection unit prioritizes collecting data on easy problems. For example, when a student is excited, the data collection unit prioritizes collecting data on interesting problems. This allows for the collection of more appropriate data by prioritizing the data to be collected according to the students' 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 above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input student emotion data into a generative AI and have the generative AI determine the priority of the data.

[0080] The data collection unit prioritizes the collection of highly relevant data, taking into account the student's geographical location when collecting learning data. For example, when a student is at school, the data collection unit prioritizes the collection of data related to the school curriculum. For example, when a student is at home, the data collection unit prioritizes the collection of data suitable for home study. For example, when a student is in the library, the data collection unit prioritizes the collection of data that should be studied in a quiet environment. This allows for the priority collection of highly relevant data 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 data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0081] The data collection unit analyzes students' social media activity and collects relevant data when collecting learning data. For example, the data collection unit collects data related to topics that students have shown interest in on social media. For example, the data collection unit collects data based on learning content that students have shared on social media. For example, the data collection unit collects data based on information about educational accounts that students follow on social media. This allows relevant data 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 without AI. For example, the data collection unit can input students' social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0082] The analysis unit estimates the student's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, the analysis unit provides detailed analysis results when the student is relaxed. For example, the analysis unit provides concise analysis results when the student is nervous. For example, the analysis unit provides visually appealing analysis results when the student is excited. By adjusting the presentation of the analysis according to the student's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the 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 presentation of the analysis.

[0083] The analysis unit adjusts the level of detail of the analysis based on the importance of the training data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the training data, a more appropriate analysis can be performed. 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 importance data of the training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit applies different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit applies a numerical analysis algorithm to mathematical data. For example, the analysis unit applies a natural language processing algorithm to language data. For example, the analysis unit applies an experimental results analysis algorithm to science data. By applying different analysis algorithms depending on the category of the training data, more appropriate analysis can be performed. 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 category data of the training data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0085] The analysis unit estimates the student's emotions and adjusts the length of the analysis based on the estimated emotions. For example, the analysis unit provides a longer analysis result when the student is relaxed. For example, the analysis unit provides a shorter analysis result when the student is in a hurry. For example, the analysis unit provides an analysis result of an appropriate length when the student is excited. By adjusting the length of the analysis according to the student's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the 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 length of the analysis.

[0086] The analysis unit determines the priority of analysis based on the submission timing of the training data. For example, the analysis unit prioritizes the analysis of data with an approaching submission deadline. For example, the analysis unit postpones the analysis of data with a distant submission deadline. For example, the analysis unit performs special analysis on data whose submission deadline has passed. By determining the priority of analysis based on the submission timing of the training data, more appropriate analysis can be performed. 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 training data submission timing data into a generating AI and have the generating AI perform the determination of analysis priority.

[0087] The analysis unit adjusts the order of analysis based on the relevance of the training data during the analysis. For example, the analysis unit prioritizes analyzing highly relevant data. For example, it postpones analyzing less relevant data. For example, it performs analysis on data with a moderate level of relevance in an appropriate order. By adjusting the order of analysis based on the relevance of the training data, more appropriate analysis can be performed. 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 relevance data of the training data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The generation unit estimates the student's emotions and adjusts the curriculum and teaching methods it generates based on the estimated emotions. For example, the generation unit provides a detailed curriculum when the student is relaxed. For example, it provides a concise curriculum when the student is nervous. For example, it provides a visually appealing curriculum when the student is excited. This allows for the provision of more appropriate curriculum and teaching by adjusting the curriculum and teaching methods generated according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 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 curriculum and teaching methods.

[0089] The generation unit adjusts the level of detail of the generated curriculum based on the importance of the training data during generation. For example, the generation unit generates a detailed curriculum for data with high importance. For example, the generation unit generates a simplified curriculum for data with low importance. For example, the generation unit generates a curriculum with an appropriate level of detail for data with moderate importance. By adjusting the level of detail of the generated curriculum based on the importance of the training data, more appropriate curricula and instruction can be provided. 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 training data importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated curriculum.

[0090] The generation unit applies different generation algorithms depending on the category of the training data during generation. For example, the generation unit applies a numerical analysis algorithm to mathematical data. For example, the generation unit applies a natural language processing algorithm to language data. For example, the generation unit applies an experimental results analysis algorithm to science data. By applying different generation algorithms depending on the category of the training data, it is possible to provide more appropriate curricula and instruction. 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 the category data of the training data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0091] The generation unit estimates the student's emotions and adjusts the length of the curriculum and instruction generated based on the estimated emotions. For example, the generation unit provides a longer curriculum when the student is relaxed. For example, it provides a shorter curriculum when the student is in a hurry. For example, it provides a curriculum of appropriate length when the student is excited. This allows for the provision of more appropriate curriculum and instruction by adjusting the length of the curriculum and instruction generated according to the student's 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, a text generation AI (e.g., LLM) or a 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 length of the curriculum and instruction.

[0092] The generation unit determines the generation priority based on the submission timing of the training data. For example, the generation unit prioritizes generating data with an approaching submission deadline. For example, it postpones generating data with a distant submission deadline. For example, the generation unit generates a special curriculum for data whose submission deadline has passed. This allows for the provision of more appropriate curricula and instruction by determining the generation priority based on the submission timing of the training data. 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 training data submission timing data into a generation AI and have the generation AI determine the generation priority.

[0093] The generation unit adjusts the generation order based on the relevance of the training data during generation. For example, the generation unit prioritizes generating highly relevant data. For example, it postpones generating less relevant data. For example, it generates data with a moderate level of relevance in an appropriate order. By adjusting the generation order based on the relevance of the training data, it is possible to provide a more appropriate curriculum and instruction. 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 the relevance data of the training data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0094] The service provider estimates the student's emotions and adjusts the curriculum and teaching methods based on the estimated emotions. For example, the service provider provides a detailed curriculum when the student is relaxed. For example, it provides a concise curriculum when the student is nervous. For example, it provides a visually appealing curriculum when the student is excited. This allows for the provision of more appropriate curriculum and teaching by adjusting the curriculum and teaching methods according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input student emotion data into a generative AI and have the generative AI adjust the curriculum and teaching methods.

[0095] The provisioning unit adjusts the level of detail provided based on the importance of the training data at the time of provision. For example, the provisioning unit provides a detailed curriculum for data of high importance. For example, the provisioning unit provides a simplified curriculum for data of low importance. For example, the provisioning unit provides a curriculum with an appropriate level of detail for data of medium importance. In this way, by adjusting the level of detail provided based on the importance of the training data, more appropriate curricula and instruction can be provided. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without using AI. For example, the provisioning unit can input training data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0096] The data provider applies different provisioning algorithms depending on the category of the learning data at the time of provision. For example, the data provider applies a numerical analysis algorithm to mathematical data. For example, the data provider applies a natural language processing algorithm to language data. For example, the data provider applies an experimental results analysis algorithm to science data. By applying different provisioning algorithms depending on the category of the learning data, more appropriate curricula and instruction can be provided. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input the category data of the learning data into a generating AI and have the generating AI execute the application of different provisioning algorithms.

[0097] The service provider estimates the student's emotions and adjusts the length of the curriculum and instruction based on the estimated emotions. For example, the service provider provides a longer curriculum when the student is relaxed. For example, it provides a shorter curriculum when the student is in a hurry. For example, it provides a curriculum of appropriate length when the student is excited. By adjusting the length of the curriculum and instruction according to the student's emotions, more appropriate curriculum and instruction can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 service provider may be performed using AI or not using AI. For example, the service provider can input student emotion data into the generative AI and have the generative AI adjust the length of the curriculum and instruction.

[0098] The provisioning unit determines the priority of provision based on the submission timing of the learning data. For example, the provisioning unit prioritizes providing data with an approaching submission deadline. For example, the provisioning unit postpones providing data with a distant submission deadline. For example, the provisioning unit provides a special curriculum for data whose submission deadline has passed. This allows for the provision of more appropriate curricula and instruction by determining the priority of provision based on the submission timing of the learning data. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input learning data submission timing data into a generating AI and have the generating AI perform the determination of provision priority.

[0099] The data delivery unit adjusts the order of delivery based on the relevance of the training data. For example, the delivery unit prioritizes providing highly relevant data. For example, the delivery unit postpones providing less relevant data. For example, the delivery unit provides data of moderate relevance in an appropriate order. By adjusting the order of delivery based on the relevance of the training data, it is possible to provide a more appropriate curriculum and instruction. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance data of the training data into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0100] The suggestion unit estimates the student's emotions and adjusts the way the suggestion is presented based on the estimated emotions. For example, the suggestion unit provides detailed suggestions when the student is relaxed. For example, it provides concise suggestions when the student is nervous. For example, it provides visually appealing suggestions when the student is excited. By adjusting the way the suggestion is presented according to the student's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input student emotion data into a generative AI and have the generative AI adjust the way the suggestion is presented.

[0101] The proposal unit adjusts the level of detail of its proposals based on the importance of the training data. For example, the proposal unit provides detailed proposals for highly important data, simplified proposals for less important data, and proposals with a moderate level of detail for moderately important data. By adjusting the level of detail of the proposals based on the importance of the training data, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance data of the training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0102] The suggestion unit estimates the student's emotions and adjusts the length of the suggestion based on the estimated emotions. For example, the suggestion unit provides longer suggestions when the student is relaxed. For example, it provides shorter suggestions when the student is in a hurry. For example, it provides suggestions of moderate length when the student is excited. By adjusting the length of the suggestion according to the student's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input student emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0103] The proposal unit determines the priority of proposals based on the submission timing of the training data. For example, the proposal unit prioritizes proposals for data with approaching submission deadlines. For example, it postpones proposals for data with distant submission deadlines. For example, the proposal unit makes special proposals for data whose submission deadlines have passed. By prioritizing proposals based on the submission timing of the training data, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input training data submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0104] The practice problem provider estimates the student's emotions and adjusts the method of providing practice problems based on the estimated emotions. For example, the practice problem provider provides detailed practice problems when the student is relaxed. For example, it provides concise practice problems when the student is nervous. For example, it provides visually appealing practice problems when the student is excited. By adjusting the method of providing practice problems according to the student's emotions, more appropriate practice problems can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 practice problem provider may be performed using AI or not using AI. For example, the practice problem provider can input student emotion data into the generative AI and have the generative AI adjust the method of providing practice problems.

[0105] The practice problem provider adjusts the level of detail of the practice problems based on the importance of the training data when providing them. For example, the practice problem provider provides detailed practice problems for data with high importance. For example, the practice problem provider provides simple practice problems for data with low importance. For example, the practice problem provider provides practice problems with an appropriate level of detail for data with moderate importance. By adjusting the level of detail of the practice problems based on the importance of the training data, more appropriate practice problems can be provided. Some or all of the above processing in the practice problem provider may be performed using AI, for example, or without using AI. For example, the practice problem provider can input the importance data of the training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the practice problems.

[0106] The practice problem provider estimates the student's emotions and prioritizes practice problems based on the estimated emotions. For example, when a student is relaxed, the practice problem provider prioritizes more difficult practice problems. For example, when a student is tired, the practice problem provider prioritizes easier practice problems. For example, when a student is excited, the practice problem provider prioritizes more interesting practice problems. This allows for the provision of more appropriate practice problems by prioritizing them according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 practice problem provider may be performed using AI or not using AI. For example, the practice problem provider can input student emotion data into a generative AI and have the generative AI determine the priority of practice problems.

[0107] The practice problem provider determines the priority of practice problems based on the submission timing of the training data when providing them. For example, the practice problem provider prioritizes providing data with an approaching submission deadline. For example, it postpones providing data with a distant submission deadline. For example, it provides special practice problems for data whose submission deadline has passed. In this way, by determining the priority of practice problems based on the submission timing of the training data, more appropriate practice problems can be provided. Some or all of the above processing in the practice problem provider may be performed using AI, for example, or without AI. For example, the practice problem provider can input training data submission timing data into a generating AI and have the generating AI perform the determination of practice problem priorities.

[0108] The curriculum adjustment unit estimates students' emotions and adjusts the curriculum adjustment method based on the estimated emotions. For example, the curriculum adjustment unit provides a detailed curriculum when a student is relaxed. For example, it provides a concise curriculum when a student is nervous. For example, it provides a visually appealing curriculum when a student is excited. This allows for the provision of a more appropriate curriculum by adjusting the curriculum adjustment method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 curriculum adjustment unit may be performed using AI or not using AI. For example, the curriculum adjustment unit can input student emotion data into the generative AI and have the generative AI perform the adjustment of the curriculum adjustment method.

[0109] The curriculum adjustment unit adjusts the level of detail in the curriculum based on the importance of the learning data during curriculum adjustment. For example, the curriculum adjustment unit provides a detailed curriculum for data with high importance. For example, the curriculum adjustment unit provides a simplified curriculum for data with low importance. For example, the curriculum adjustment unit provides a curriculum with an appropriate level of detail for data with moderate importance. In this way, a more appropriate curriculum can be provided by adjusting the level of detail in the curriculum based on the importance of the learning data. Some or all of the above processing in the curriculum adjustment unit may be performed using AI, for example, or without using AI. For example, the curriculum adjustment unit can input learning data importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the curriculum.

[0110] The curriculum adjustment unit estimates students' emotions and determines curriculum priorities based on the estimated emotions. For example, the curriculum adjustment unit prioritizes providing more difficult curriculum when students are relaxed. For example, the curriculum adjustment unit prioritizes providing easier curriculum when students are tired. For example, the curriculum adjustment unit prioritizes providing engaging curriculum when students are excited. This allows for the provision of more appropriate curriculum by determining curriculum priorities according to 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 curriculum adjustment unit may be performed using AI or not using AI. For example, the curriculum adjustment unit can input student emotion data into a generative AI and have the generative AI determine curriculum priorities.

[0111] The curriculum adjustment unit determines curriculum priorities based on the submission timing of learning data during curriculum adjustment. For example, the curriculum adjustment unit prioritizes providing data with approaching submission deadlines. For example, the curriculum adjustment unit postpones data with distant submission deadlines. For example, the curriculum adjustment unit provides a special curriculum for data whose submission deadlines have passed. This allows for the provision of a more appropriate curriculum by determining curriculum priorities based on the submission timing of learning data. Some or all of the above processing in the curriculum adjustment unit may be performed using AI, for example, or without AI. For example, the curriculum adjustment unit can input learning data submission timing data into a generating AI and have the generating AI perform the determination of curriculum priorities.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can apply different analysis methods to student learning data based on its content. For example, numerical analysis methods can be applied to mathematical data, and natural language processing methods to language data. Experimental result analysis methods can also be applied to science data. This enables optimal analysis tailored to the content of the learning data, resulting in more accurate analysis results.

[0114] The data collection unit can adjust its data collection methods based on the student's learning environment when collecting student learning data. For example, if a student is studying at home, it can collect data suitable for home study; if a student is studying at school, it can collect data related to the school curriculum. Furthermore, if a student is studying in the library, it can collect data that should be studied in a quiet environment. This enables optimal data collection tailored to each student's learning environment.

[0115] The generation unit can adjust the curriculum content based on students' learning styles when generating a curriculum based on their learning data. For example, it can provide a curriculum that makes extensive use of diagrams and graphs for students with a visual learning style, and a curriculum that makes extensive use of audio and video for students with an auditory learning style. It can also provide a curriculum that includes many practical tasks for students with an experiential learning style. This allows for the provision of an optimal curriculum tailored to each student's learning style.

[0116] The delivery department can adjust the delivery method of generated curricula and instruction based on the student's learning history. For example, it can prioritize providing question formats in which the student has previously achieved high correct answer rates, and focus on providing instruction in question formats in which the student struggled. It can also analyze from the student's past learning history which times of day are when their concentration is highest, and deliver the curriculum and instruction during those times. This enables the delivery method to be optimized based on the student's learning history.

[0117] The data collection unit can estimate students' emotions and adjust the timing of data collection based on those estimates. For example, it can increase the frequency of data collection when students are focused and decrease it when they are tired. It can also allow students to relax before collecting data when they are stressed. This allows for data collection at the optimal time, tailored to each student's emotional state.

[0118] The analysis unit can estimate the student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, it can provide detailed analysis results when the student is relaxed and concise results when the student is tense. It can also provide visually appealing analysis results when the student is excited. This allows for the provision of optimal analysis results tailored to the student's emotions.

[0119] The generation unit can estimate students' emotions and adjust the presentation of the curriculum and instruction based on those estimated emotions. For example, it can provide a detailed curriculum when students are relaxed and a concise curriculum when they are stressed. It can also provide a visually appealing curriculum when students are excited. This allows for the provision of optimal curriculum and instruction tailored to each student's emotions.

[0120] The system can estimate students' emotions and adjust the curriculum and teaching methods based on those estimates. For example, it can provide a detailed curriculum when students are relaxed and a concise curriculum when they are stressed. It can also provide a visually engaging curriculum when students are excited. This allows for the provision of optimal curriculum and teaching tailored to each student's emotions.

[0121] The proposal function can estimate students' emotions and adjust the way it presents proposals based on those estimates. For example, it can provide detailed proposals when students are relaxed and concise proposals when they are tense. It can also provide visually appealing proposals when students are excited. This allows the system to provide optimal proposals tailored to each student's emotions.

[0122] The data collection unit can analyze students' social media activity and collect relevant data when gathering student learning data. For example, it can collect data related to topics students have shown interest in on social media and collect data based on learning content they have shared on social media. It can also collect data based on information from educational accounts that students follow on social media. This enables optimal data collection based on students' social media activity.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects student learning data. For example, it collects data such as questions answered by students, assignments submitted, and learning progress. The data collection unit collects data such as the correct answer rate for questions answered online and the evaluation results of submitted assignments. It can also acquire data from the Learning Management System (LMS) and monitor students' learning behavior in real time using sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses natural language processing (NLP) to analyze student inputs and machine learning algorithms to analyze and predict learning progress. Furthermore, it can use statistical analysis to understand data trends and clustering techniques to group students by their level of understanding. Step 3: The generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the analysis unit. For example, it provides optimal teaching methods and practice problems according to the student's level of understanding and progress, and provides additional practice problems for areas where understanding is weak. For students who are falling behind, it provides a curriculum to adjust the learning pace, and can also use AI to automatically generate curricula and teaching methods that are tailored to the student's learning situation. Step 4: The delivery unit provides the curriculum and instruction generated by the generation unit. For example, it provides the curriculum and instruction to students through an online platform and provides feedback and suggestions to teachers. Furthermore, it can use AI to monitor students' learning progress in real time, provide instruction at the appropriate time, and automatically generate feedback tailored to the students' learning progress.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects student learning data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates individualized instruction and learning curricula based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the generated curriculum and instruction to the students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

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

[0132] The 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.

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

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

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

[0136] Figure 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.

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

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

[0139] In the 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.

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

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0143] The data processing system 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects student learning data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates individualized instruction and learning curricula 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 curriculum and instruction to the students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

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

[0148] The 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.

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects student learning data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates individual instruction and learning curricula based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the generated curriculum and instruction to the students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects student learning data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates individual instruction and learning curricula based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated curriculum and instruction to the students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The data collection department collects student learning data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the aforementioned analysis unit, The system comprises a providing unit that provides the curriculum and instruction generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, The department includes a suggestion section that provides feedback and suggestions to teachers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It includes a section that provides additional practice problems for areas where understanding is weak. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It includes a curriculum adjustment unit that provides a curriculum to adjust the learning pace. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting learning data, filtering is performed based on students' current learning environment and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate students' emotions and prioritize the data to 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 When collecting learning data, the system prioritizes collecting highly relevant data by considering students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting learning data, analyze students' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate the students' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the students' emotions and adjusts the length of the analysis 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, the priority of the analysis is determined based on when the training data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is We estimate students' emotions and adjust the curriculum and teaching methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, adjust the level of detail based on the importance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, different generative algorithms are applied depending on the category of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates students' emotions and adjusts the length of the curriculum and instruction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the generation priority is determined based on when the training data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the generation order is adjusted based on the relevance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, We estimate students' emotions and adjust the curriculum and teaching methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the data, adjust the level of detail based on the importance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the data, different distribution algorithms are applied depending on the category of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates students' emotions and adjusts the curriculum and the length of instruction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the data, we will prioritize its distribution based on when the training data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the data, the order of delivery will be adjusted based on the relevance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, We estimate the students' emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, adjust the level of detail of the proposal based on the importance of the training data. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned proposal section is, Estimate the students' emotions and adjust the length of the proposal based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the training data was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned practice problem provision unit, The system estimates students' emotions and adjusts how practice problems are presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned practice problem provision unit, When providing practice problems, adjust the level of detail of the practice problems based on the importance of the training data. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned practice problem provision unit, The system estimates students' emotions and prioritizes practice problems based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned practice problem provision unit, When providing practice problems, the priority of the practice problems will be determined based on when the learning data was submitted. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned curriculum adjustment unit, We estimate students' emotions and adjust the curriculum based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned curriculum adjustment unit, When adjusting the curriculum, adjust the level of detail in the curriculum based on the importance of the learning data. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned curriculum adjustment unit, The system estimates students' emotions and prioritizes the curriculum based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned curriculum adjustment unit, When adjusting the curriculum, prioritize the curriculum based on when learning data is submitted. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0197] 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 data collection department collects student learning data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates individualized instruction and learning curricula based on the analysis results obtained by the aforementioned analysis unit, The system comprises a providing unit that provides the curriculum and instruction generated by the generation unit. A system characterized by the following features.

2. The aforementioned supply unit is, The department includes a suggestion section that provides feedback and suggestions to teachers. The system according to feature 1.

3. The generating unit is It includes a section that provides additional practice problems for areas where understanding is weak. The system according to feature 1.

4. The generating unit is It includes a curriculum adjustment unit that provides a curriculum to adjust the learning pace. The system according to feature 1.

5. The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of data collection based on the estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting learning data, filtering is performed based on students' current learning environment and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is We estimate students' emotions and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

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