system

Abstract

The system according to this embodiment aims to provide each student with an optimal learning experience. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a feedback unit. The collection unit collects learning data. The analysis unit analyzes the learning data collected by the collection unit. The provision unit provides a learning plan based on the analysis results obtained by the analysis unit. The feedback unit tracks learning progress and provides feedback based on the learning plan provided by the provision unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide an optimal learning experience for each individual student, and there are problems in improving the learning effect and motivation.

[0005] The system according to the embodiment aims to provide an optimal learning experience for each individual student.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a feedback unit. The data collection unit collects training data. The analysis unit analyzes the training data collected by the data collection unit. The data provision unit provides a training plan based on the analysis results obtained by the analysis unit. The feedback unit tracks training progress and provides feedback based on the training plan provided by the data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide each student with an optimal learning experience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 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) The educational platform according to an embodiment of the present invention is a system that utilizes AI technology to provide an optimal learning experience for each student. This system analyzes students' learning progress and understanding in real time and provides personalized learning plans and feedback. This enables effective learning and improves learning motivation. Furthermore, it deepens understanding by providing an immersive learning experience utilizing interactive content and VR technology. As a result, the educational platform can provide an optimal learning experience for each student and maximize learning efficiency.

[0029] The educational platform according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a feedback unit. The collection unit collects learning data. Learning data includes, but is not limited to, text data, audio data, and image data. For example, the collection unit collects text data when students are learning. The collection unit can also collect audio data spoken by students. Furthermore, the collection unit can also collect image data such as diagrams and graphs drawn by students. For example, the collection unit scans images of notes written by students and saves them as digital data. The analysis unit analyzes the learning data collected by the collection unit. The analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit uses statistical analysis to analyze students' learning patterns. The analysis unit can also use machine learning algorithms to analyze students' level of understanding. Furthermore, the analysis unit can also use natural language processing techniques to analyze text data. For example, the analysis unit analyzes students' answers and calculates the accuracy rate. The provision unit provides a learning plan based on the analysis results obtained by the analysis unit. A learning plan may include, but is not limited to, learning objectives, learning content, and a learning schedule. For example, the delivery unit creates a learning plan based on the student's learning objectives. The delivery unit may also adjust the learning content according to the student's level of understanding. Furthermore, the delivery unit can manage the student's learning schedule. For example, the delivery unit adjusts the learning schedule according to the student's learning progress. The feedback unit tracks learning progress and provides feedback based on the learning plan provided by the delivery unit. Feedback may include, but is not limited to, text feedback, audio feedback, and real-time feedback. For example, the feedback unit provides text feedback according to the student's learning progress. The feedback unit may also provide audio feedback based on the student's spoken content. Furthermore, the feedback unit can track learning progress in real time and provide immediate feedback.For example, the feedback unit provides real-time feedback while students are solving problems. This enables the educational platform according to the embodiment to collect and analyze learning data, provide learning plans, and provide feedback.

[0030] The data collection unit collects learning data. This learning data includes, but is not limited to, text data, audio data, and image data. For example, the unit collects text data from students' learning. Specifically, it collects notes students take from textbooks and reference materials, and text data entered on online learning platforms. The unit can also collect audio data from students' speech. For example, it records presentations students give in class or comments made during online discussions and saves them as audio data. Furthermore, the unit can collect image data such as diagrams and graphs drawn by students. For example, it scans images of students' notes and saves them as digital data. This allows the unit to collect diverse data on students' learning activities and gain a detailed understanding of their learning progress and comprehension. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the learning data collected by the collection unit. This analysis may involve, but is not limited to, statistical analysis or machine learning algorithms. Specifically, the analysis unit uses statistical analysis to analyze students' learning patterns. For example, it statistically analyzes when students study most efficiently and which subjects they struggle with. The analysis unit can also analyze students' comprehension using machine learning algorithms. For instance, it evaluates comprehension based on the accuracy rate and time taken to answer questions, and creates individualized learning plans. Furthermore, the analysis unit can analyze text data using natural language processing techniques. For example, it can analyze students' answers, calculate accuracy rates, and evaluate the quality and logical consistency of their answers. This allows the analysis unit to quickly and accurately analyze collected data and gain a detailed understanding of students' learning progress. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term learning trends and patterns. For example, it can predict, based on past learning data, which learning methods are most effective for a particular student, helping to improve future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual learning patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term learning management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The service provider provides learning plans based on the analysis results obtained by the analysis provider. These learning plans may include, but are not limited to, learning objectives, learning content, and learning schedules. Specifically, the service provider creates learning plans based on students' learning objectives. For example, if a student aims to pass a specific exam, the service provider provides a learning plan to acquire the knowledge and skills necessary for that exam. The service provider can also adjust the learning content according to the student's level of understanding. For example, if a student struggles with a particular subject, the service provider provides a learning plan that focuses on that subject. Furthermore, the service provider can manage students' learning schedules. For example, the service provider adjusts the learning schedule according to the student's progress to support efficient learning. This allows the service provider to provide an optimal learning plan for each student, maximizing learning effectiveness. Additionally, the service provider can monitor the progress of learning plans in real time and revise or adjust the plan as needed. For example, if a student is progressing faster than planned, the service provider sets new learning objectives and updates the learning plan. The service provider can also collect student feedback to improve learning plans. This allows the service provider to consistently offer highly accurate learning plans based on the latest information, maximizing students' learning effectiveness.

[0033] The Feedback Department tracks learning progress and provides feedback based on the learning plan provided by the Delivery Department. This feedback includes, but is not limited to, text feedback, audio feedback, and real-time feedback. Specifically, the Feedback Department provides text feedback based on the student's learning progress. For example, it provides comments in text format regarding the accuracy and quality of answers to questions the student has answered. The Feedback Department can also provide audio feedback based on the student's spoken content. For example, it provides audio feedback on areas for improvement in pronunciation and expression to a student's presentation. Furthermore, the Feedback Department can track learning progress in real time and provide immediate feedback. For example, it can provide real-time feedback while the student is solving problems, instantly offering hints and advice. This allows the Feedback Department to support the student's learning activities and maximize learning effectiveness. Additionally, the Feedback Department can collect student feedback and collaborate with the Delivery Department to improve the learning plan. For example, it can analyze how students responded to the feedback and revise the learning plan based on the results. Furthermore, the feedback department can provide positive feedback and encouraging messages to boost student motivation. This allows the feedback department to maintain students' enthusiasm for learning and maximize learning effectiveness.

[0034] The educational platform includes a content section that provides interactive learning content. For example, the content section provides learning content in the form of quizzes. For instance, it provides interactive quiz-style feedback to students as they solve problems. The content section can also provide learning content in the form of simulations. For example, it can help students learn experimental procedures through simulations while conducting experiments. Furthermore, the content section can provide learning content in the form of games. For example, it provides learning content designed to help students acquire learning material through games. This enhances the enjoyment and effectiveness of learning by providing interactive learning content.

[0035] The educational platform includes a VR section that provides immersive experiences utilizing VR technology. The VR section, for example, uses head-mounted displays to provide students with immersive learning experiences. For instance, it could allow students to virtually walk through the streets of ancient Rome in a history class. The VR section can also provide immersive learning experiences using 360-degree video. For example, it could allow students to virtually tour a laboratory in a science class. Furthermore, the VR section can provide interactive VR content. For instance, it could provide interactive feedback to students as they conduct experiments in a virtual environment. This allows for a deeper understanding of the learning process through the use of VR technology.

[0036] The data collection unit can collect students' learning progress and comprehension in real time. For example, the data collection unit can collect data in real time when students are learning. For example, the data collection unit can collect answer data in real time when students are solving problems. The data collection unit can also collect audio data of students speaking in real time. For example, the data collection unit can collect audio data in real time when students are taking an English listening test. Furthermore, the data collection unit can collect image data such as diagrams and graphs drawn by students in real time. For example, the data collection unit can scan diagrams drawn by students in real time and save them as digital data. This makes it possible to create individually optimized learning plans by collecting students' learning progress and comprehension in real time. 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 progress data into a generating AI and have the generating AI perform real-time data collection.

[0037] The analysis unit can analyze collected learning data and identify students' strengths and weaknesses. For example, the analysis unit can analyze collected learning data using statistical analysis. For example, the analysis unit can statistically analyze students' test results to identify their strengths and weaknesses. The analysis unit can also analyze learning data using machine learning algorithms. For example, the analysis unit can analyze students' learning patterns using machine learning algorithms to identify their strengths and weaknesses. Furthermore, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit can analyze students' answer texts using natural language processing technology to identify their strengths and weaknesses. This makes it possible to provide individually optimized learning plans by identifying students' strengths and weaknesses. 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 collected learning data into a generating AI and have the generating AI perform the identification of strengths and weaknesses.

[0038] The service provider can provide an individually optimized learning plan based on the analysis results. For example, the service provider can set learning objectives based on the analysis results. For example, the service provider can set learning objectives based on the student's strengths and weaknesses. The service provider can also adjust the learning content based on the analysis results. For example, the service provider can adjust the learning content according to the student's level of understanding. Furthermore, the service provider can manage the learning schedule based on the analysis results. For example, the service provider can adjust the learning schedule according to the student's learning progress. This enables effective learning by providing an individually optimized learning plan. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI execute the provision of an individually optimized learning plan.

[0039] The feedback unit can track learning progress and provide feedback as needed. For example, the feedback unit can track a student's learning progress in real time. For example, the feedback unit can track a student's progress in real time while they are solving problems. The feedback unit can also provide text feedback according to the student's learning progress. For example, the feedback unit can provide text feedback for the questions the student has answered. Furthermore, the feedback unit can provide audio feedback based on what the student has said. For example, the feedback unit can provide audio feedback for what the student has said. This allows for immediate improvement and confirmation of understanding by tracking learning progress and providing feedback as needed. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input student learning progress data into a generating AI and have the generating AI provide the feedback.

[0040] The data collection unit can analyze a student's past learning history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data on learning methods that have been effective for the student in the past. For example, the data collection unit may analyze learning methods in which the student has achieved high scores in the past and prioritize collecting data on those methods. The data collection unit can also collect more detailed data on areas where the student struggles. For example, the data collection unit may collect detailed learning data on areas where the student struggles and identify areas for improvement. Furthermore, the data collection unit may adjust the collection frequency based on the student's learning pattern. For example, the data collection unit may analyze the student's learning pattern and set the optimal collection frequency. This allows the optimal data collection method to be selected by analyzing the student's past learning history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit may input the student's past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter learning data based on the student's current learning environment and areas of interest. For example, if a student is studying in a quiet environment, the data collection unit will collect data suitable for that environment. For example, if a student is studying in a library, the data collection unit will collect data suitable for a quiet environment. The data collection unit can also prioritize collecting data related to areas of interest to the student. For example, the data collection unit will prioritize collecting data related to scientific fields that the student is interested in. Furthermore, the data collection unit can collect data optimized for the device the student is using. For example, if a student is using a tablet, the data collection unit will collect data optimized for the tablet. This allows for the collection of highly relevant data by filtering based on the student's current learning environment and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the student's learning environment data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the student's geographical location when collecting learning data. For example, if a student is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if a student is in a specific city, the data collection unit will prioritize the collection of historical data related to that city. The data collection unit can also collect data related to the travel destination if the student is traveling. For example, if a student is studying at their travel destination, the data collection unit will collect geographical data related to that region. Furthermore, if a student is at home, the data collection unit can collect data suitable for home study. For example, if a student is studying at home, the data collection unit will collect learning material data suitable for home study. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on 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 collection of highly relevant data.

[0043] The data collection unit can analyze students' social media activity and collect relevant data when collecting learning data. For example, the data collection unit can collect data related to topics that students are interested in on social media. For example, the data collection unit can collect data related to science topics that students are interested in on social media. The data collection unit can also collect information on educational accounts that students follow. For example, the data collection unit can collect the content of posts from educational accounts that students follow. Furthermore, the data collection unit can collect data based on the activities of online communities in which students participate. For example, the data collection unit can collect the content of discussions in online communities in which students participate. This allows for the collection of relevant data by analyzing students' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input students' social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust 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 detailed analysis on data that has a significant impact on student performance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data that has little impact on student performance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data of moderate importance. For example, the analysis unit performs an analysis with a moderate level of detail on data that has a moderate impact on student performance. By adjusting the level of detail of the analysis based on the importance of the training data, more effective analysis becomes possible. 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 of the training data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit can apply numerical analysis algorithms to mathematical data. For instance, when solving mathematical problems, the analysis unit uses numerical analysis algorithms to analyze the data. The analysis unit can also apply natural language processing algorithms to English data. For example, when analyzing data from an English reading test, the analysis unit uses natural language processing algorithms. Furthermore, the analysis unit can apply experimental data analysis algorithms to scientific data. For example, when analyzing experimental scientific data, the analysis unit uses experimental data analysis algorithms. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the training data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the training data into a generating AI and have the generating AI execute the application of the analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on when the training data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may prioritize the analysis of recently collected training data. The analysis unit can also postpone the analysis of previously collected data. For example, the analysis unit may postpone the analysis of previously collected training data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. For example, the analysis unit may prioritize the analysis of training data collected during a specific semester. This allows for more effective analysis by determining the priority of analysis based on when the training data was collected. 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 training data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the training data during analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may prioritize the analysis of data with high relevance to the learning objective. The analysis unit can also postpone the analysis of data with low relevance. For example, the analysis unit may postpone the analysis of data with low relevance to the learning objective. Furthermore, the analysis unit may moderately analyze data with moderate relevance to the learning objective. For example, the analysis unit may moderately analyze data with moderate relevance to the learning objective. By adjusting the order of analysis based on the relevance of the training data, more effective analysis becomes possible. 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 of the training data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The service provider can adjust the level of detail in a learning plan based on the importance of the learning content when providing the plan. For example, the service provider can provide a detailed plan for highly important learning content. For example, the service provider can provide a detailed plan for learning content that has a significant impact on a student's performance. The service provider can also provide a simplified plan for less important learning content. For example, the service provider can provide a simplified plan for learning content that has little impact on a student's performance. Furthermore, the service provider can provide a plan with an appropriate level of detail for learning content of moderate importance. For example, the service provider can provide a plan with an appropriate level of detail for learning content that has a moderate impact on a student's performance. By adjusting the level of detail in the plan based on the importance of the learning content, a more effective learning plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the learning content into a generating AI and have the generating AI adjust the level of detail in the plan.

[0049] The service provider can apply different plan provision algorithms depending on the category of learning content when providing learning plans. For example, the service provider can apply a numerical analysis algorithm to mathematics learning content. For example, when solving mathematics problems, the service provider can use a numerical analysis algorithm to provide a learning plan. The service provider can also apply a natural language processing algorithm to English learning content. For example, when providing a learning plan for an English reading test, the service provider can use a natural language processing algorithm. Furthermore, the service provider can apply an experimental data analysis algorithm to science learning content. For example, when analyzing scientific experimental data, the service provider can use an experimental data analysis algorithm. By applying different plan provision algorithms depending on the category of learning content, a more appropriate learning plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of learning content into a generating AI and have the generating AI execute the application of the plan provision algorithm.

[0050] The service provider can prioritize learning plans based on the submission deadlines for the learning content when providing them. For example, the service provider can prioritize providing plans for learning content with an approaching submission deadline. For example, the service provider can prioritize providing learning plans for assignments with an approaching submission deadline. The service provider can also postpone providing learning plans for learning content with a distant submission deadline. For example, the service provider can postpone providing learning plans for assignments with a distant submission deadline. Furthermore, the service provider can appropriately provide plans for learning content with a medium-term submission deadline. For example, the service provider can appropriately provide learning plans for assignments with a medium-term submission deadline. By prioritizing plans based on the submission deadlines for the learning content, a more effective learning plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the submission deadlines for the learning content into a generating AI and have the generating AI determine the plan prioritization.

[0051] The service provider can adjust the order of learning plans based on the relevance of the learning content when providing them. For example, the service provider can prioritize providing plans for learning content with high relevance. For example, the service provider can prioritize providing plans for learning content with high relevance to the learning objectives. The service provider can also postpone providing plans for learning content with low relevance. For example, the service provider can postpone providing plans for learning content with low relevance to the learning objectives. Furthermore, the service provider can appropriately provide plans for learning content with moderate relevance. For example, the service provider can appropriately provide plans for learning content with moderate relevance to the learning objectives. By adjusting the order of plans based on the relevance of the learning content, a more effective learning plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of the learning content into a generating AI and have the generating AI perform the adjustment of the order of the plans.

[0052] The feedback unit can adjust the level of detail of the feedback based on the importance of the learning progress when providing feedback. For example, the feedback unit can provide detailed feedback for learning progress of high importance. For example, the feedback unit can provide detailed feedback for learning progress that has a significant impact on a student's grade. The feedback unit can also provide simplified feedback for learning progress of low importance. For example, the feedback unit can provide simplified feedback for learning progress that has little impact on a student's grade. Furthermore, the feedback unit can provide feedback with a moderate level of detail for learning progress of moderate importance. For example, the feedback unit can provide feedback with a moderate level of detail for learning progress that has a moderate impact on a student's grade. By adjusting the level of detail of the feedback based on the importance of the learning progress, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the importance of the learning progress into a generating AI and have the generating AI adjust the level of detail of the feedback.

[0053] The feedback unit can apply different feedback algorithms depending on the category of learning progress when providing feedback. For example, the feedback unit can apply a numerical analysis algorithm to mathematics learning progress. For example, when solving a mathematics problem, the feedback unit can use a numerical analysis algorithm to provide feedback. The feedback unit can also apply a natural language processing algorithm to English learning progress. For example, when providing feedback on an English reading test, the feedback unit can use a natural language processing algorithm. Furthermore, the feedback unit can apply an experimental data analysis algorithm to science learning progress. For example, when analyzing scientific experimental data, the feedback unit can use an experimental data analysis algorithm. This allows for the provision of more appropriate feedback by applying different feedback algorithms depending on the category of learning progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the category of learning progress into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0054] The feedback unit can prioritize feedback based on the submission timing of learning progress when providing feedback. For example, the feedback unit can prioritize providing feedback for learning progress with an approaching submission deadline. For example, the feedback unit can prioritize providing feedback for assignments that students are submitting soon. The feedback unit can also postpone providing feedback for learning progress with a distant submission deadline. For example, the feedback unit can postpone providing feedback for assignments that students are submitting soon. Furthermore, the feedback unit can provide feedback moderately for learning progress with a medium submission deadline. For example, the feedback unit can provide feedback moderately for assignments that students are submitting moderately. By prioritizing feedback based on the submission timing of learning progress, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the submission timing of learning progress into a generating AI and have the generating AI determine the priority of feedback.

[0055] The feedback unit can adjust the order of feedback based on the relevance of learning progress when providing feedback. For example, the feedback unit can prioritize providing feedback to learning progress that has a high relevance. For example, the feedback unit can prioritize providing feedback to learning progress that has a high relevance to the learning objective. The feedback unit can also postpone providing feedback to learning progress that has a low relevance. For example, the feedback unit can postpone providing feedback to learning progress that has a low relevance to the learning objective. Furthermore, the feedback unit can provide a moderate amount of feedback to learning progress that has a moderate relevance to the learning objective. For example, the feedback unit can provide a moderate amount of feedback to learning progress that has a moderate relevance to the learning objective. By adjusting the order of feedback based on the relevance of learning progress, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the relevance of learning progress into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0056] The content unit can adjust the level of detail of interactive content based on the importance of the learning material. For example, the content unit can provide detailed content for highly important learning material. For example, it can provide detailed content for learning material that has a significant impact on students' grades. The content unit can also provide simplified content for less important learning material. For example, it can provide simplified content for learning material that has little impact on students' grades. Furthermore, the content unit can provide content with an appropriate level of detail for learning material of moderate importance. For example, it can provide content with an appropriate level of detail for learning material that has a moderate impact on students' grades. By adjusting the level of detail of the content based on the importance of the learning material, more effective content can be provided. Some or all of the above processing in the content unit may be performed using AI, for example, or without AI. For example, the content unit can input the importance of the learning material into a generating AI and have the generating AI perform the adjustment of the level of detail of the content.

[0057] The content department can prioritize content based on the submission deadlines for learning materials when providing interactive content. For example, the content department can prioritize providing content for learning materials with approaching deadlines. For example, the content department can prioritize providing interactive content for assignments with approaching deadlines. The content department can also postpone providing content for learning materials with distant deadlines. For example, the content department can postpone providing interactive content for assignments with distant deadlines. Furthermore, the content department can provide content appropriately for learning materials with medium-term deadlines. For example, the content department can provide appropriately interactive content for assignments with medium-term deadlines. This allows for the provision of more effective content by prioritizing content based on the submission deadlines for learning materials. Some or all of the above processing in the content department may be performed using AI, for example, or not. For example, the content department can input the submission deadlines for learning materials into a generating AI and have the generating AI determine the content prioritization.

[0058] The VR unit can adjust the level of detail of a VR experience based on the importance of the learning content when providing the VR experience. For example, the VR unit can provide a detailed VR experience for highly important learning content. For example, the VR unit can provide a detailed VR experience for learning content that has a significant impact on students' grades. The VR unit can also provide a simplified VR experience for less important learning content. For example, the VR unit can provide a simplified VR experience for learning content that has little impact on students' grades. Furthermore, the VR unit can provide a VR experience with an appropriate level of detail for learning content of moderate importance. For example, the VR unit can provide a VR experience with an appropriate level of detail for learning content that has a moderate impact on students' grades. By adjusting the level of detail of the experience based on the importance of the learning content, a more effective VR experience can be provided. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit can input the importance of the learning content into a generating AI and have the generating AI perform the adjustment of the level of detail of the experience.

[0059] The VR department can prioritize VR experiences based on the submission deadlines of learning materials. For example, the VR department can prioritize providing VR experiences for learning materials with approaching submission deadlines. For example, the VR department can prioritize providing VR experiences for assignments that students are submitting soon. The VR department can also postpone providing VR experiences for learning materials with distant submission deadlines. For example, the VR department can postpone providing VR experiences for assignments that students are submitting soon. Furthermore, the VR department can provide VR experiences appropriately for learning materials with medium submission deadlines. For example, the VR department can appropriately provide VR experiences for assignments that students are submitting. By prioritizing experiences based on the submission deadlines of learning materials, a more effective VR experience can be provided. Some or all of the above processing in the VR department may be performed using AI, for example, or not using AI. For example, the VR department can input the submission deadlines of learning materials into a generating AI and have the generating AI determine the priority of experiences.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can identify students' learning styles when analyzing their learning data. For example, the analysis unit can determine whether a student is a visual, auditory, or experiential learner. This allows the provision unit to provide learning plans tailored to each student's learning style. For instance, visual learners can be provided with learning plans that heavily utilize diagrams and graphs, while auditory learners can be provided with plans that heavily utilize audio and lecture videos. Experiential learners can be provided with learning plans that heavily utilize experiments and simulations. This enables the provision of an optimal learning experience tailored to each student's learning style.

[0062] The content department can analyze students' learning history and prioritize providing learning content that has been effective in the past. For example, it can analyze learning content in which students have achieved high scores in the past and provide that content again. It can also provide more detailed learning content for areas where students struggle. For example, if a student has difficulty with a particular area of ​​mathematics, it can provide learning content specifically tailored to that area. Furthermore, it can adjust the frequency of learning content provision based on students' learning patterns. For example, if a student tends to study intensively in short periods, it can provide learning content tailored to that pattern. This allows for the provision of optimal learning content based on students' learning history.

[0063] The VR department can adjust the difficulty level of VR experiences according to students' learning progress. For example, for content students are learning for the first time, a basic VR experience is provided, and the difficulty level is increased as their understanding deepens. Furthermore, if a student has difficulty in a particular area, a VR experience tailored to that area can be provided. For example, if a student has difficulty with physics experiments, a VR experience simulating a physics experiment can be provided. In addition, the pace of the VR experience can be adjusted to match the student's learning pace. For example, if a student tends to learn slowly, the VR experience can be adjusted to match that pace. This allows for the provision of an optimal VR experience tailored to each student's learning progress.

[0064] The data collection unit can monitor students' learning environments in real time and collect optimal learning data. For example, if a student is studying in a quiet environment, it can collect data appropriate for that environment. It can also collect data appropriate for students studying while on the go. For example, if a student is studying during their commute, it can collect data suitable for short-term learning. Furthermore, it can collect data optimized for the student's learning device. For example, if a student is using a smartphone, it can collect data optimized for that device. This allows for the collection of optimal learning data tailored to each student's learning environment.

[0065] The analysis unit can perform analyses of student learning data in a way that aligns with the student's learning objectives. For example, if a student is studying for a specific exam, the analysis will prioritize data related to that exam. Similarly, if a student aims to acquire a specific skill, the analysis will prioritize data related to that skill. For instance, if a student aims to acquire programming skills, the analysis will prioritize data related to programming. Furthermore, the system can adjust the presentation of the analysis results according to the student's learning objectives. For example, if a student is a visual learner, the analysis results will be provided using graphs and charts extensively. This allows for optimal analysis tailored to the student's learning objectives.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects learning data. This learning data includes text data, audio data, and image data. For example, the collection unit collects text data, spoken audio data, and image data such as drawings and graphs created by students during their studies. The collection unit can also scan images of students' handwritten notes and save them as digital data. Step 2: The analysis unit analyzes the training data collected by the data collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to analyze students' learning patterns and machine learning algorithms to analyze students' level of understanding. Furthermore, the analysis unit uses natural language processing technology to analyze text data and analyze students' answers to calculate the accuracy rate. Step 3: The provisioning department provides a learning plan based on the analysis results obtained by the analysis department. The learning plan includes learning objectives, learning content, and a learning schedule. For example, the provisioning department creates a learning plan based on the student's learning objectives and adjusts the learning content according to the student's level of understanding. Furthermore, the provisioning department manages and adjusts the learning schedule according to the student's learning progress. Step 4: The feedback department tracks learning progress and provides feedback based on the learning plan provided by the delivery department. Feedback may include text feedback, audio feedback, and real-time feedback. For example, the feedback department provides text feedback according to the student's learning progress and audio feedback based on the student's speech. Furthermore, the feedback department tracks learning progress in real time and provides immediate feedback.

[0068] (Example of form 2) The educational platform according to an embodiment of the present invention is a system that utilizes AI technology to provide an optimal learning experience for each student. This system analyzes students' learning progress and understanding in real time and provides personalized learning plans and feedback. This enables effective learning and improves learning motivation. Furthermore, it deepens understanding by providing an immersive learning experience utilizing interactive content and VR technology. As a result, the educational platform can provide an optimal learning experience for each student and maximize learning efficiency.

[0069] The educational platform according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a feedback unit. The collection unit collects learning data. Learning data includes, but is not limited to, text data, audio data, and image data. For example, the collection unit collects text data when students are learning. The collection unit can also collect audio data spoken by students. Furthermore, the collection unit can also collect image data such as diagrams and graphs drawn by students. For example, the collection unit scans images of notes written by students and saves them as digital data. The analysis unit analyzes the learning data collected by the collection unit. The analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit uses statistical analysis to analyze students' learning patterns. The analysis unit can also use machine learning algorithms to analyze students' level of understanding. Furthermore, the analysis unit can also use natural language processing techniques to analyze text data. For example, the analysis unit analyzes students' answers and calculates the accuracy rate. The provision unit provides a learning plan based on the analysis results obtained by the analysis unit. A learning plan may include, but is not limited to, learning objectives, learning content, and a learning schedule. For example, the delivery unit creates a learning plan based on the student's learning objectives. The delivery unit may also adjust the learning content according to the student's level of understanding. Furthermore, the delivery unit can manage the student's learning schedule. For example, the delivery unit adjusts the learning schedule according to the student's learning progress. The feedback unit tracks learning progress and provides feedback based on the learning plan provided by the delivery unit. Feedback may include, but is not limited to, text feedback, audio feedback, and real-time feedback. For example, the feedback unit provides text feedback according to the student's learning progress. The feedback unit may also provide audio feedback based on the student's spoken content. Furthermore, the feedback unit can track learning progress in real time and provide immediate feedback.For example, the feedback unit provides real-time feedback while students are solving problems. This enables the educational platform according to the embodiment to collect and analyze learning data, provide learning plans, and provide feedback.

[0070] The data collection unit collects learning data. This learning data includes, but is not limited to, text data, audio data, and image data. For example, the unit collects text data from students' learning. Specifically, it collects notes students take from textbooks and reference materials, and text data entered on online learning platforms. The unit can also collect audio data from students' speech. For example, it records presentations students give in class or comments made during online discussions and saves them as audio data. Furthermore, the unit can collect image data such as diagrams and graphs drawn by students. For example, it scans images of students' notes and saves them as digital data. This allows the unit to collect diverse data on students' learning activities and gain a detailed understanding of their learning progress and comprehension. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0071] The analysis unit analyzes the learning data collected by the collection unit. This analysis may involve, but is not limited to, statistical analysis or machine learning algorithms. Specifically, the analysis unit uses statistical analysis to analyze students' learning patterns. For example, it statistically analyzes when students study most efficiently and which subjects they struggle with. The analysis unit can also analyze students' comprehension using machine learning algorithms. For instance, it evaluates comprehension based on the accuracy rate and time taken to answer questions, and creates individualized learning plans. Furthermore, the analysis unit can analyze text data using natural language processing techniques. For example, it can analyze students' answers, calculate accuracy rates, and evaluate the quality and logical consistency of their answers. This allows the analysis unit to quickly and accurately analyze collected data and gain a detailed understanding of students' learning progress. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term learning trends and patterns. For example, it can predict, based on past learning data, which learning methods are most effective for a particular student, helping to improve future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual learning patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term learning management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The service provider provides learning plans based on the analysis results obtained by the analysis provider. These learning plans may include, but are not limited to, learning objectives, learning content, and learning schedules. Specifically, the service provider creates learning plans based on students' learning objectives. For example, if a student aims to pass a specific exam, the service provider provides a learning plan to acquire the knowledge and skills necessary for that exam. The service provider can also adjust the learning content according to the student's level of understanding. For example, if a student struggles with a particular subject, the service provider provides a learning plan that focuses on that subject. Furthermore, the service provider can manage students' learning schedules. For example, the service provider adjusts the learning schedule according to the student's progress to support efficient learning. This allows the service provider to provide an optimal learning plan for each student, maximizing learning effectiveness. Additionally, the service provider can monitor the progress of learning plans in real time and revise or adjust the plan as needed. For example, if a student is progressing faster than planned, the service provider sets new learning objectives and updates the learning plan. The service provider can also collect student feedback to improve learning plans. This allows the service provider to consistently offer highly accurate learning plans based on the latest information, maximizing students' learning effectiveness.

[0073] The Feedback Department tracks learning progress and provides feedback based on the learning plan provided by the Delivery Department. This feedback includes, but is not limited to, text feedback, audio feedback, and real-time feedback. Specifically, the Feedback Department provides text feedback based on the student's learning progress. For example, it provides comments in text format regarding the accuracy and quality of answers to questions the student has answered. The Feedback Department can also provide audio feedback based on the student's spoken content. For example, it provides audio feedback on areas for improvement in pronunciation and expression to a student's presentation. Furthermore, the Feedback Department can track learning progress in real time and provide immediate feedback. For example, it can provide real-time feedback while the student is solving problems, instantly offering hints and advice. This allows the Feedback Department to support the student's learning activities and maximize learning effectiveness. Additionally, the Feedback Department can collect student feedback and collaborate with the Delivery Department to improve the learning plan. For example, it can analyze how students responded to the feedback and revise the learning plan based on the results. Furthermore, the feedback department can provide positive feedback and encouraging messages to boost student motivation. This allows the feedback department to maintain students' enthusiasm for learning and maximize learning effectiveness.

[0074] The educational platform includes a content section that provides interactive learning content. For example, the content section provides learning content in the form of quizzes. For instance, it provides interactive quiz-style feedback to students as they solve problems. The content section can also provide learning content in the form of simulations. For example, it can help students learn experimental procedures through simulations while conducting experiments. Furthermore, the content section can provide learning content in the form of games. For example, it provides learning content designed to help students acquire learning material through games. This enhances the enjoyment and effectiveness of learning by providing interactive learning content.

[0075] The educational platform includes a VR section that provides immersive experiences utilizing VR technology. The VR section, for example, uses head-mounted displays to provide students with immersive learning experiences. For instance, it could allow students to virtually walk through the streets of ancient Rome in a history class. The VR section can also provide immersive learning experiences using 360-degree video. For example, it could allow students to virtually tour a laboratory in a science class. Furthermore, the VR section can provide interactive VR content. For instance, it could provide interactive feedback to students as they conduct experiments in a virtual environment. This allows for a deeper understanding of the learning process through the use of VR technology.

[0076] The data collection unit can collect students' learning progress and comprehension in real time. For example, the data collection unit can collect data in real time when students are learning. For example, the data collection unit can collect answer data in real time when students are solving problems. The data collection unit can also collect audio data of students speaking in real time. For example, the data collection unit can collect audio data in real time when students are taking an English listening test. Furthermore, the data collection unit can collect image data such as diagrams and graphs drawn by students in real time. For example, the data collection unit can scan diagrams drawn by students in real time and save them as digital data. This makes it possible to create individually optimized learning plans by collecting students' learning progress and comprehension in real time. 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 progress data into a generating AI and have the generating AI perform real-time data collection.

[0077] The analysis unit can analyze collected learning data and identify students' strengths and weaknesses. For example, the analysis unit can analyze collected learning data using statistical analysis. For example, the analysis unit can statistically analyze students' test results to identify their strengths and weaknesses. The analysis unit can also analyze learning data using machine learning algorithms. For example, the analysis unit can analyze students' learning patterns using machine learning algorithms to identify their strengths and weaknesses. Furthermore, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit can analyze students' answer texts using natural language processing technology to identify their strengths and weaknesses. This makes it possible to provide individually optimized learning plans by identifying students' strengths and weaknesses. 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 collected learning data into a generating AI and have the generating AI perform the identification of strengths and weaknesses.

[0078] The service provider can provide an individually optimized learning plan based on the analysis results. For example, the service provider can set learning objectives based on the analysis results. For example, the service provider can set learning objectives based on the student's strengths and weaknesses. The service provider can also adjust the learning content based on the analysis results. For example, the service provider can adjust the learning content according to the student's level of understanding. Furthermore, the service provider can manage the learning schedule based on the analysis results. For example, the service provider can adjust the learning schedule according to the student's learning progress. This enables effective learning by providing an individually optimized learning plan. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI execute the provision of an individually optimized learning plan.

[0079] The feedback unit can track learning progress and provide feedback as needed. For example, the feedback unit can track a student's learning progress in real time. For example, the feedback unit can track a student's progress in real time while they are solving problems. The feedback unit can also provide text feedback according to the student's learning progress. For example, the feedback unit can provide text feedback for the questions the student has answered. Furthermore, the feedback unit can provide audio feedback based on what the student has said. For example, the feedback unit can provide audio feedback for what the student has said. This allows for immediate improvement and confirmation of understanding by tracking learning progress and providing feedback as needed. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input student learning progress data into a generating AI and have the generating AI provide the feedback.

[0080] The data collection unit can estimate students' emotions and adjust the timing of data collection based on the estimated emotions. For example, if a student is stressed, the unit can delay data collection until the student is relaxed. For instance, the unit can pause data collection when a student is stressed and resume it when they are relaxed. The unit can also collect data when a student is focused. For example, the unit can collect data when a student is focused, capturing data at the most effective time. Furthermore, if a student is tired, the unit can resume data collection after a break. For example, the unit can pause data collection when a student is tired and resume it after a break. This allows for data collection at more appropriate times by adjusting the timing of data collection based on students' emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input student emotional data into a generating AI and have the generating AI adjust the timing of data collection.

[0081] The data collection unit can analyze a student's past learning history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data on learning methods that have been effective for the student in the past. For example, the data collection unit may analyze learning methods in which the student has achieved high scores in the past and prioritize collecting data on those methods. The data collection unit can also collect more detailed data on areas where the student struggles. For example, the data collection unit may collect detailed learning data on areas where the student struggles and identify areas for improvement. Furthermore, the data collection unit may adjust the collection frequency based on the student's learning pattern. For example, the data collection unit may analyze the student's learning pattern and set the optimal collection frequency. This allows the optimal data collection method to be selected by analyzing the student's past learning history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit may input the student's past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0082] The data collection unit can filter learning data based on the student's current learning environment and areas of interest. For example, if a student is studying in a quiet environment, the data collection unit will collect data suitable for that environment. For example, if a student is studying in a library, the data collection unit will collect data suitable for a quiet environment. The data collection unit can also prioritize collecting data related to areas of interest to the student. For example, the data collection unit will prioritize collecting data related to scientific fields that the student is interested in. Furthermore, the data collection unit can collect data optimized for the device the student is using. For example, if a student is using a tablet, the data collection unit will collect data optimized for the tablet. This allows for the collection of highly relevant data by filtering based on the student's current learning environment and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the student's learning environment data into a generating AI and have the generating AI perform the filtering.

[0083] The data collection unit can estimate the student's emotions and determine the priority of the learning data to collect based on the estimated emotions. For example, if the student is excited, the data collection unit will prioritize collecting difficult data. For example, if the student is excited, the data collection unit will prioritize collecting difficult problems. The data collection unit can also prioritize collecting basic data if the student is relaxed. For example, if the student is relaxed, the data collection unit will prioritize collecting basic learning data. Furthermore, if the student is tired, the data collection unit can also prioritize collecting easy data. For example, if the student is tired, the data collection unit will prioritize collecting easy problems. This allows for more effective data collection by determining the priority of the learning data to collect based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input student emotional data into a generating AI and have the generating AI determine priorities.

[0084] The data collection unit can prioritize the collection of highly relevant data based on the student's geographical location when collecting learning data. For example, if a student is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if a student is in a specific city, the data collection unit will prioritize the collection of historical data related to that city. The data collection unit can also collect data related to the travel destination if the student is traveling. For example, if a student is studying at their travel destination, the data collection unit will collect geographical data related to that region. Furthermore, if a student is at home, the data collection unit can collect data suitable for home study. For example, if a student is studying at home, the data collection unit will collect learning material data suitable for home study. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on 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 collection of highly relevant data.

[0085] The data collection unit can analyze students' social media activity and collect relevant data when collecting learning data. For example, the data collection unit can collect data related to topics that students are interested in on social media. For example, the data collection unit can collect data related to science topics that students are interested in on social media. The data collection unit can also collect information on educational accounts that students follow. For example, the data collection unit can collect the content of posts from educational accounts that students follow. Furthermore, the data collection unit can collect data based on the activities of online communities in which students participate. For example, the data collection unit can collect the content of discussions in online communities in which students participate. This allows for the collection of relevant data by analyzing students' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input students' social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0086] The analysis unit can estimate a student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a student is nervous, the analysis unit can provide simple and highly visual analysis results. For example, when a student is nervous, the analysis unit can provide analysis results using simple graphs or charts. The analysis unit can also provide detailed analysis results if a student is relaxed. For example, when a student is relaxed, the analysis unit can provide analysis results using detailed text and diagrams. Furthermore, if a student is excited, the analysis unit can provide visually stimulating analysis results. For example, when a student is excited, the analysis unit can provide analysis results using colorful graphs or animations. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input students' emotional data into a generating AI and have the generating AI adjust the way the analysis is presented.

[0087] The analysis unit can adjust 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 detailed analysis on data that has a significant impact on student performance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data that has little impact on student performance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data of moderate importance. For example, the analysis unit performs an analysis with a moderate level of detail on data that has a moderate impact on student performance. By adjusting the level of detail of the analysis based on the importance of the training data, more effective analysis becomes possible. 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 of the training data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit can apply numerical analysis algorithms to mathematical data. For instance, when solving mathematical problems, the analysis unit uses numerical analysis algorithms to analyze the data. The analysis unit can also apply natural language processing algorithms to English data. For example, when analyzing data from an English reading test, the analysis unit uses natural language processing algorithms. Furthermore, the analysis unit can apply experimental data analysis algorithms to scientific data. For example, when analyzing experimental scientific data, the analysis unit uses experimental data analysis algorithms. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the training data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the training data into a generating AI and have the generating AI execute the application of the analysis algorithms.

[0089] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the student is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the student is in a hurry, the analysis unit can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis if the student is relaxed. For example, if the student is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the student is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the student is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the student's emotions, more appropriate analysis results can be provided. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student emotional data into a generating AI and have the generating AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on when the training data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may prioritize the analysis of recently collected training data. The analysis unit can also postpone the analysis of previously collected data. For example, the analysis unit may postpone the analysis of previously collected training data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. For example, the analysis unit may prioritize the analysis of training data collected during a specific semester. This allows for more effective analysis by determining the priority of analysis based on when the training data was collected. 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 training data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the training data during analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may prioritize the analysis of data with high relevance to the learning objective. The analysis unit can also postpone the analysis of data with low relevance. For example, the analysis unit may postpone the analysis of data with low relevance to the learning objective. Furthermore, the analysis unit may moderately analyze data with moderate relevance to the learning objective. For example, the analysis unit may moderately analyze data with moderate relevance to the learning objective. By adjusting the order of analysis based on the relevance of the training data, more effective analysis becomes possible. 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 of the training data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The learning plan provider can estimate a student's emotions and adjust the presentation of the learning plan based on the estimated emotions. For example, if a student is nervous, the provider can provide a simple and highly visual learning plan. For example, when a student is nervous, the provider can provide a learning plan using simple text and diagrams. The provider can also provide a detailed learning plan if a student is relaxed. For example, when a student is relaxed, the provider can provide a learning plan using detailed text and diagrams. Furthermore, if a student is excited, the provider can provide a visually stimulating learning plan. For example, when a student is excited, the provider can provide a learning plan using colorful graphs and animations. This allows for the provision of more appropriate learning plans by adjusting the presentation of the learning plan based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or without AI. For example, the service provider can input student emotional data into a generating AI and have the AI ​​adjust how the learning plan is presented.

[0093] The service provider can adjust the level of detail in a learning plan based on the importance of the learning content when providing the plan. For example, the service provider can provide a detailed plan for highly important learning content. For example, the service provider can provide a detailed plan for learning content that has a significant impact on a student's performance. The service provider can also provide a simplified plan for less important learning content. For example, the service provider can provide a simplified plan for learning content that has little impact on a student's performance. Furthermore, the service provider can provide a plan with an appropriate level of detail for learning content of moderate importance. For example, the service provider can provide a plan with an appropriate level of detail for learning content that has a moderate impact on a student's performance. By adjusting the level of detail in the plan based on the importance of the learning content, a more effective learning plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the learning content into a generating AI and have the generating AI adjust the level of detail in the plan.

[0094] The service provider can apply different plan provision algorithms depending on the category of learning content when providing learning plans. For example, the service provider can apply a numerical analysis algorithm to mathematics learning content. For example, when solving mathematics problems, the service provider can use a numerical analysis algorithm to provide a learning plan. The service provider can also apply a natural language processing algorithm to English learning content. For example, when providing a learning plan for an English reading test, the service provider can use a natural language processing algorithm. Furthermore, the service provider can apply an experimental data analysis algorithm to science learning content. For example, when analyzing scientific experimental data, the service provider can use an experimental data analysis algorithm. By applying different plan provision algorithms depending on the category of learning content, a more appropriate learning plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of learning content into a generating AI and have the generating AI execute the application of the plan provision algorithm.

[0095] The service provider can estimate a student's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if a student is in a hurry, the service provider can provide a short, concise learning plan. For example, if a student is in a hurry, the service provider can provide a short, concise learning plan. The service provider can also provide a detailed learning plan if a student is relaxed. For example, if a student is relaxed, the service provider can provide a detailed learning plan. Furthermore, if a student is excited, the service provider can provide a visually stimulating learning plan. For example, if a student is excited, the service provider can provide a visually stimulating learning plan. This allows for the provision of more appropriate learning plans by adjusting the length of the learning plan based on 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, 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, for example, or without AI. For example, the service provider can input student emotional data into a generating AI and have the AI ​​adjust the length of the learning plan.

[0096] The service provider can prioritize learning plans based on the submission deadlines for the learning content when providing them. For example, the service provider can prioritize providing plans for learning content with an approaching submission deadline. For example, the service provider can prioritize providing learning plans for assignments with an approaching submission deadline. The service provider can also postpone providing learning plans for learning content with a distant submission deadline. For example, the service provider can postpone providing learning plans for assignments with a distant submission deadline. Furthermore, the service provider can appropriately provide plans for learning content with a medium-term submission deadline. For example, the service provider can appropriately provide learning plans for assignments with a medium-term submission deadline. By prioritizing plans based on the submission deadlines for the learning content, a more effective learning plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the submission deadlines for the learning content into a generating AI and have the generating AI determine the plan prioritization.

[0097] The service provider can adjust the order of learning plans based on the relevance of the learning content when providing them. For example, the service provider can prioritize providing plans for learning content with high relevance. For example, the service provider can prioritize providing plans for learning content with high relevance to the learning objectives. The service provider can also postpone providing plans for learning content with low relevance. For example, the service provider can postpone providing plans for learning content with low relevance to the learning objectives. Furthermore, the service provider can appropriately provide plans for learning content with moderate relevance. For example, the service provider can appropriately provide plans for learning content with moderate relevance to the learning objectives. By adjusting the order of plans based on the relevance of the learning content, a more effective learning plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of the learning content into a generating AI and have the generating AI perform the adjustment of the order of the plans.

[0098] The feedback unit can estimate a student's emotions and adjust the way it presents feedback based on those emotions. For example, if a student is nervous, the feedback unit can provide simple and easily understandable feedback. For example, it can provide feedback using simple text or diagrams when a student is nervous. The feedback unit can also provide detailed feedback if a student is relaxed. For example, it can provide feedback using detailed text or diagrams when a student is relaxed. Furthermore, if a student is excited, the feedback unit can provide visually stimulating feedback. For example, it can provide feedback using colorful graphs or animations when a student is excited. This allows for more appropriate feedback to be provided by adjusting the way feedback is presented based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input student emotional data into a generating AI and have the AI ​​adjust the way the feedback is expressed.

[0099] The feedback unit can adjust the level of detail of the feedback based on the importance of the learning progress when providing feedback. For example, the feedback unit can provide detailed feedback for learning progress of high importance. For example, the feedback unit can provide detailed feedback for learning progress that has a significant impact on a student's grade. The feedback unit can also provide simplified feedback for learning progress of low importance. For example, the feedback unit can provide simplified feedback for learning progress that has little impact on a student's grade. Furthermore, the feedback unit can provide feedback with a moderate level of detail for learning progress of moderate importance. For example, the feedback unit can provide feedback with a moderate level of detail for learning progress that has a moderate impact on a student's grade. By adjusting the level of detail of the feedback based on the importance of the learning progress, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the importance of the learning progress into a generating AI and have the generating AI adjust the level of detail of the feedback.

[0100] The feedback unit can apply different feedback algorithms depending on the category of learning progress when providing feedback. For example, the feedback unit can apply a numerical analysis algorithm to mathematics learning progress. For example, when solving a mathematics problem, the feedback unit can use a numerical analysis algorithm to provide feedback. The feedback unit can also apply a natural language processing algorithm to English learning progress. For example, when providing feedback on an English reading test, the feedback unit can use a natural language processing algorithm. Furthermore, the feedback unit can apply an experimental data analysis algorithm to science learning progress. For example, when analyzing scientific experimental data, the feedback unit can use an experimental data analysis algorithm. This allows for the provision of more appropriate feedback by applying different feedback algorithms depending on the category of learning progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the category of learning progress into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0101] The feedback unit can estimate the student's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the student is in a hurry, the feedback unit can provide short, concise feedback. For example, if the student is in a hurry, the feedback unit can provide short, concise feedback. For example, if the student is relaxed, the feedback unit can provide detailed feedback. For example, if the student is excited, the feedback unit can provide visually stimulating feedback. For example, if the student is excited, the feedback unit can provide visually stimulating feedback. This allows for more appropriate feedback to be provided by adjusting the length of the feedback based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input student emotional data into a generating AI and have the AI ​​adjust the length of the feedback.

[0102] The feedback unit can prioritize feedback based on the submission timing of learning progress when providing feedback. For example, the feedback unit can prioritize providing feedback for learning progress with an approaching submission deadline. For example, the feedback unit can prioritize providing feedback for assignments that students are submitting soon. The feedback unit can also postpone providing feedback for learning progress with a distant submission deadline. For example, the feedback unit can postpone providing feedback for assignments that students are submitting soon. Furthermore, the feedback unit can provide feedback moderately for learning progress with a medium submission deadline. For example, the feedback unit can provide feedback moderately for assignments that students are submitting moderately. By prioritizing feedback based on the submission timing of learning progress, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the submission timing of learning progress into a generating AI and have the generating AI determine the priority of feedback.

[0103] The feedback unit can adjust the order of feedback based on the relevance of learning progress when providing feedback. For example, the feedback unit can prioritize providing feedback to learning progress that has a high relevance. For example, the feedback unit can prioritize providing feedback to learning progress that has a high relevance to the learning objective. The feedback unit can also postpone providing feedback to learning progress that has a low relevance. For example, the feedback unit can postpone providing feedback to learning progress that has a low relevance to the learning objective. Furthermore, the feedback unit can provide a moderate amount of feedback to learning progress that has a moderate relevance to the learning objective. For example, the feedback unit can provide a moderate amount of feedback to learning progress that has a moderate relevance to the learning objective. By adjusting the order of feedback based on the relevance of learning progress, more effective feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the relevance of learning progress into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0104] The content unit can estimate students' emotions and adjust the presentation of interactive content based on the estimated emotions. For example, if a student is nervous, the content unit can provide simple and highly visual content. For example, when a student is nervous, the content unit can provide interactive content using simple text and charts. The content unit can also provide detailed content if a student is relaxed. For example, when a student is relaxed, the content unit can provide interactive content using detailed text and charts. Furthermore, if a student is excited, the content unit can provide visually stimulating content. For example, when a student is excited, the content unit can provide interactive content using colorful graphs and animations. This allows for the provision of more appropriate content by adjusting the presentation of interactive content based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content unit may be performed using AI, for example, or without AI. For example, the content department can input student emotional data into a generative AI and have the AI ​​adjust how interactive content is presented.

[0105] The content unit can adjust the level of detail of interactive content based on the importance of the learning material. For example, the content unit can provide detailed content for highly important learning material. For example, it can provide detailed content for learning material that has a significant impact on students' grades. The content unit can also provide simplified content for less important learning material. For example, it can provide simplified content for learning material that has little impact on students' grades. Furthermore, the content unit can provide content with an appropriate level of detail for learning material of moderate importance. For example, it can provide content with an appropriate level of detail for learning material that has a moderate impact on students' grades. By adjusting the level of detail of the content based on the importance of the learning material, more effective content can be provided. Some or all of the above processing in the content unit may be performed using AI, for example, or without AI. For example, the content unit can input the importance of the learning material into a generating AI and have the generating AI perform the adjustment of the level of detail of the content.

[0106] The content unit can estimate a student's emotions and adjust the length of the interactive content based on the estimated emotions. For example, if a student is in a hurry, the content unit can provide short, concise content. For example, if a student is in a hurry, the content unit can provide short, concise, interactive content. The content unit can also provide detailed content if a student is relaxed. For example, if a student is relaxed, the content unit can provide detailed, interactive content. Furthermore, if a student is excited, the content unit can provide visually stimulating content. For example, if a student is excited, the content unit can provide visually stimulating interactive content. This allows for the provision of more appropriate content by adjusting the length of the interactive content based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content unit may be performed using AI, for example, or without AI. For example, the content department can input student emotional data into a generative AI and have the AI ​​adjust the length of the interactive content.

[0107] The content department can prioritize content based on the submission deadlines for learning materials when providing interactive content. For example, the content department can prioritize providing content for learning materials with approaching deadlines. For example, the content department can prioritize providing interactive content for assignments with approaching deadlines. The content department can also postpone providing content for learning materials with distant deadlines. For example, the content department can postpone providing interactive content for assignments with distant deadlines. Furthermore, the content department can provide content appropriately for learning materials with medium-term deadlines. For example, the content department can provide appropriately interactive content for assignments with medium-term deadlines. This allows for the provision of more effective content by prioritizing content based on the submission deadlines for learning materials. Some or all of the above processing in the content department may be performed using AI, for example, or not. For example, the content department can input the submission deadlines for learning materials into a generating AI and have the generating AI determine the content prioritization.

[0108] The VR department can estimate students' emotions and adjust the way the VR experience is presented based on those estimated emotions. For example, if a student is nervous, the VR department can provide a simple and highly visual VR experience. For example, if a student is nervous, the VR department can provide a VR experience using simple visuals and sounds. The VR department can also provide a detailed VR experience if a student is relaxed. For example, if a student is relaxed, the VR department can provide a VR experience using detailed visuals and sounds. Furthermore, if a student is excited, the VR department can provide a visually stimulating VR experience. For example, if a student is excited, the VR department can provide a VR experience using colorful visuals and animations. In this way, by adjusting the way the VR experience is presented based on students' emotions, a more appropriate VR experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, 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 VR department may be performed using AI, for example, or without AI. For example, the VR department can input students' emotional data into a generative AI and have the AI ​​adjust how the VR experience is presented.

[0109] The VR unit can adjust the level of detail of a VR experience based on the importance of the learning content when providing the VR experience. For example, the VR unit can provide a detailed VR experience for highly important learning content. For example, the VR unit can provide a detailed VR experience for learning content that has a significant impact on students' grades. The VR unit can also provide a simplified VR experience for less important learning content. For example, the VR unit can provide a simplified VR experience for learning content that has little impact on students' grades. Furthermore, the VR unit can provide a VR experience with an appropriate level of detail for learning content of moderate importance. For example, the VR unit can provide a VR experience with an appropriate level of detail for learning content that has a moderate impact on students' grades. By adjusting the level of detail of the experience based on the importance of the learning content, a more effective VR experience can be provided. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit can input the importance of the learning content into a generating AI and have the generating AI perform the adjustment of the level of detail of the experience.

[0110] The VR unit can estimate a student's emotions and adjust the length of the VR experience based on the estimated emotions. For example, if a student is in a hurry, the VR unit can provide a short, concise VR experience. For example, if a student is in a hurry, the VR unit can provide a short, concise VR experience. The VR unit can also provide a detailed VR experience if a student is relaxed. For example, if a student is relaxed, the VR unit can provide a detailed VR experience. Furthermore, if a student is excited, the VR unit can provide a visually stimulating VR experience. For example, if a student is excited, the VR unit can provide a visually stimulating VR experience. In this way, by adjusting the length of the VR experience based on the student's emotions, a more appropriate VR experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR department can input students' emotional data into a generative AI and have the AI ​​adjust the length of the VR experience.

[0111] The VR department can prioritize VR experiences based on the submission deadlines of learning materials. For example, the VR department can prioritize providing VR experiences for learning materials with approaching submission deadlines. For example, the VR department can prioritize providing VR experiences for assignments that students are submitting soon. The VR department can also postpone providing VR experiences for learning materials with distant submission deadlines. For example, the VR department can postpone providing VR experiences for assignments that students are submitting soon. Furthermore, the VR department can provide VR experiences appropriately for learning materials with medium submission deadlines. For example, the VR department can appropriately provide VR experiences for assignments that students are submitting. By prioritizing experiences based on the submission deadlines of learning materials, a more effective VR experience can be provided. Some or all of the above processing in the VR department may be performed using AI, for example, or not using AI. For example, the VR department can input the submission deadlines of learning materials into a generating AI and have the generating AI determine the priority of experiences.

[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 identify students' learning styles when analyzing their learning data. For example, the analysis unit can determine whether a student is a visual, auditory, or experiential learner. This allows the provision unit to provide learning plans tailored to each student's learning style. For instance, visual learners can be provided with learning plans that heavily utilize diagrams and graphs, while auditory learners can be provided with plans that heavily utilize audio and lecture videos. Experiential learners can be provided with learning plans that heavily utilize experiments and simulations. This enables the provision of an optimal learning experience tailored to each student's learning style.

[0114] The content department can analyze students' learning history and prioritize providing learning content that has been effective in the past. For example, it can analyze learning content in which students have achieved high scores in the past and provide that content again. It can also provide more detailed learning content for areas where students struggle. For example, if a student has difficulty with a particular area of ​​mathematics, it can provide learning content specifically tailored to that area. Furthermore, it can adjust the frequency of learning content provision based on students' learning patterns. For example, if a student tends to study intensively in short periods, it can provide learning content tailored to that pattern. This allows for the provision of optimal learning content based on students' learning history.

[0115] The VR department can adjust the difficulty level of VR experiences according to students' learning progress. For example, for content students are learning for the first time, a basic VR experience is provided, and the difficulty level is increased as their understanding deepens. Furthermore, if a student has difficulty in a particular area, a VR experience tailored to that area can be provided. For example, if a student has difficulty with physics experiments, a VR experience simulating a physics experiment can be provided. In addition, the pace of the VR experience can be adjusted to match the student's learning pace. For example, if a student tends to learn slowly, the VR experience can be adjusted to match that pace. This allows for the provision of an optimal VR experience tailored to each student's learning progress.

[0116] The data collection unit can monitor students' learning environments in real time and collect optimal learning data. For example, if a student is studying in a quiet environment, it can collect data appropriate for that environment. It can also collect data appropriate for students studying while on the go. For example, if a student is studying during their commute, it can collect data suitable for short-term learning. Furthermore, it can collect data optimized for the student's learning device. For example, if a student is using a smartphone, it can collect data optimized for that device. This allows for the collection of optimal learning data tailored to each student's learning environment.

[0117] The analysis unit can perform analyses of student learning data in a way that aligns with the student's learning objectives. For example, if a student is studying for a specific exam, the analysis will prioritize data related to that exam. Similarly, if a student aims to acquire a specific skill, the analysis will prioritize data related to that skill. For instance, if a student aims to acquire programming skills, the analysis will prioritize data related to programming. Furthermore, the system can adjust the presentation of the analysis results according to the student's learning objectives. For example, if a student is a visual learner, the analysis results will be provided using graphs and charts extensively. This allows for optimal analysis tailored to the student's learning objectives.

[0118] The system can estimate a student's emotions and adjust the difficulty level of the learning plan based on those emotions. For example, if a student is stressed, it can provide an easy learning plan. For instance, a basic learning plan can be provided when a student is stressed. Conversely, if a student is relaxed, it can provide a more difficult learning plan. For example, an advanced learning plan can be provided when a student is relaxed. Furthermore, if a student is excited, it can provide a challenging learning plan. For example, a learning plan containing difficult problems can be provided when a student is excited. By adjusting the difficulty level of the learning plan based on the student's emotions, a more appropriate learning plan can be provided.

[0119] The feedback system can estimate a student's emotions and adjust the timing of feedback based on those emotions. For example, if a student is stressed, it can delay providing feedback. For instance, if a student is stressed, it may withhold feedback until they are relaxed. Conversely, if a student is focused, it can provide feedback at that moment. For example, it may provide immediate feedback when a student is focused. Furthermore, if a student is tired, it may provide feedback after a break. For example, if a student is tired, it may provide feedback after a break. By adjusting the timing of feedback based on the student's emotions, more appropriate feedback can be provided.

[0120] The content department can estimate students' emotions and adjust the difficulty level of interactive content based on those emotions. For example, if a student is stressed, it can provide low-difficulty content. For instance, it can provide basic interactive content when a student is stressed. Conversely, if a student is relaxed, it can provide more challenging content. For example, it can provide applied interactive content when a student is relaxed. Furthermore, if a student is excited, it can provide challenging content. For example, it can provide interactive content containing difficult questions when a student is excited. By adjusting the difficulty level of interactive content based on students' emotions, more appropriate content can be provided.

[0121] The VR department can estimate students' emotions and adjust the timing of VR experiences based on those estimates. For example, if a student is feeling stressed, the VR experience can be delayed. For instance, if a student is stressed, the VR experience will not be provided until they are relaxed. Conversely, if a student is concentrating, the VR experience can be provided at that time. For example, if a student is concentrating, the VR experience can be provided immediately. Furthermore, if a student is tired, the VR experience can be provided after a break. For example, if a student is tired, the VR experience can be provided after a break. In this way, by adjusting the timing of VR experiences based on students' emotions, a more appropriate VR experience can be provided.

[0122] The system can estimate a student's emotions and adjust the feedback method of the learning plan based on those emotions. For example, if a student is nervous, it can provide simple, highly visual feedback. For instance, it can provide feedback using simple text or charts when a student is nervous. It can also provide detailed feedback if a student is relaxed. For example, it can provide feedback using detailed text or charts when a student is relaxed. Furthermore, if a student is excited, it can provide visually stimulating feedback. For example, it can provide feedback using colorful graphs or animations when a student is excited. This allows for more appropriate feedback to be provided by adjusting the feedback method based on the student's emotions.

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

[0124] Step 1: The collection unit collects learning data. This learning data includes text data, audio data, and image data. For example, the collection unit collects text data, spoken audio data, and image data such as drawings and graphs created by students during their studies. The collection unit can also scan images of students' handwritten notes and save them as digital data. Step 2: The analysis unit analyzes the training data collected by the data collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to analyze students' learning patterns and machine learning algorithms to analyze students' level of understanding. Furthermore, the analysis unit uses natural language processing technology to analyze text data and analyze students' answers to calculate the accuracy rate. Step 3: The provisioning department provides a learning plan based on the analysis results obtained by the analysis department. The learning plan includes learning objectives, learning content, and a learning schedule. For example, the provisioning department creates a learning plan based on the student's learning objectives and adjusts the learning content according to the student's level of understanding. Furthermore, the provisioning department manages and adjusts the learning schedule according to the student's learning progress. Step 4: The feedback department tracks learning progress and provides feedback based on the learning plan provided by the delivery department. Feedback may include text feedback, audio feedback, and real-time feedback. For example, the feedback department provides text feedback according to the student's learning progress and audio feedback based on the student's speech. Furthermore, the feedback department tracks learning progress in real time and provides immediate feedback.

[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, provision unit, feedback unit, content unit, and VR unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects learning data using the camera 42 and microphone 38B of the smart device 14 and transmits the data 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 provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides a learning plan based on the analysis results. The feedback unit is implemented in the specific processing unit 46A of the smart device 14 and provides feedback according to the learning progress. The content unit is implemented in the specific processing unit 46A of the smart device 14 and provides interactive learning content. The VR unit is implemented in the specific processing unit 46A of the smart device 14 and provides an immersive learning experience using a head-mounted display. 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, provision unit, feedback unit, content unit, and VR 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 learning data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data 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 provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a learning plan based on the analysis results. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides feedback according to the learning progress. The content unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides interactive learning content. The VR unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides an immersive learning experience using a head-mounted display. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[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, provision unit, feedback unit, content unit, and VR unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects learning data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the data 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 provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a learning plan based on the analysis results. The feedback unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides feedback according to the learning progress. The content unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides interactive learning content. The VR unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides an immersive learning experience using a head-mounted display. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[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, provision unit, feedback unit, content unit, and VR unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects learning data using the camera 42 and microphone 238 of the robot 414 and transmits the data 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 provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a learning plan based on the analysis results. The feedback unit is implemented, for example, by the control unit 46A of the robot 414 and provides feedback according to the learning progress. The content unit is implemented, for example, by the control unit 46A of the robot 414 and provides interactive learning content. The VR unit is implemented, for example, by the control unit 46A of the robot 414 and provides an immersive learning experience using a head-mounted display. 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) A data collection unit that collects training data, An analysis unit analyzes the learning data collected by the aforementioned collection unit, A providing unit that provides a learning plan based on the analysis results obtained by the analysis unit, The system includes a feedback unit that tracks learning progress and provides feedback based on the learning plan provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) It includes a content section that provides interactive learning content. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a VR department that provides immersive experiences utilizing VR technology. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect student learning progress and understanding in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The collected learning data is analyzed to identify students' strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide an individually optimized learning plan based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is Track learning progress and provide feedback as needed. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) 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 11) The aforementioned collection unit is The system estimates students' emotions and prioritizes the training data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting learning data, the system prioritizes collecting data that is highly relevant based on students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) 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 16) 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 17) 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 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the training data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned supply unit is, The system estimates students' emotions and adjusts how the learning plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing a learning plan, we adjust the level of detail in the plan based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing a learning plan, different plan provision algorithms are applied depending on the category of learning content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates students' emotions and adjusts the length of the learning plan 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 a learning plan, we will prioritize the plan based on when the learning content will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing a learning plan, the order of the plan will be adjusted based on the relevance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is The system estimates students' emotions and adjusts the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the category of learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is The system estimates the student's emotions and adjusts the length of the feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, we prioritize feedback based on when learning progress is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, adjust the order of feedback based on the relevance of learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned content section is, It estimates students' emotions and adjusts how interactive content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned content section is, When providing interactive content, adjust the level of detail based on the importance of the learning material. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned content section is, It estimates students' emotions and adjusts the length of interactive content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned content section is, When providing interactive content, prioritize content based on when learning materials are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned VR section is The system estimates students' emotions and adjusts the way the VR experience is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned VR section is When providing a VR experience, adjust the level of detail of the experience based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned VR section is The system estimates the students' emotions and adjusts the length of the VR experience based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned VR section is When providing VR experiences, the priority of the experiences will be determined based on when the learning materials were submitted. The system described in Appendix 1, 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. A data collection unit that collects training data, An analysis unit analyzes the learning data collected by the aforementioned collection unit, A providing unit that provides a learning plan based on the analysis results obtained by the analysis unit, The system includes a feedback unit that tracks learning progress and provides feedback based on the learning plan provided by the aforementioned provision unit. A system characterized by the following features.

2. It includes a content section that provides interactive learning content. The system according to feature 1.

3. It has a VR department that provides immersive experiences utilizing VR technology. The system according to feature 1.

4. The aforementioned collection unit is Collect student learning progress and understanding in real time. The system according to feature 1.

5. The aforementioned analysis unit, The collected learning data is analyzed to identify students' strengths and weaknesses. The system according to feature 1.

6. The aforementioned supply unit is, We provide an individually optimized learning plan based on the analysis results. The system according to feature 1.

7. The aforementioned feedback unit is Track learning progress and provide feedback as needed. The system according to feature 1.

8. 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.

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

10. 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.

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