system

Abstract

The system according to this embodiment aims to provide students with an optimal learning experience tailored to their learning style and abilities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an adjustment unit, a format change unit, and a provision unit. The collection unit collects student learning data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit. The format change unit changes the format of the learning materials based on the difficulty level of the problems adjusted by the adjustment unit. The provision unit provides progress reports and feedback based on the format of the learning materials changed by the format change unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, customized education according to the learning styles and abilities of students has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal learning experience according to the learning styles and abilities of students.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an adjustment unit, a format modification unit, and a provision unit. The collection unit collects student learning data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit. The format modification unit changes the format of the learning materials based on the difficulty level of the problems adjusted by the adjustment unit. The provision unit provides progress reports and feedback based on the format of the learning materials modified by the format modification unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide students with an optimal learning experience tailored to their learning style and abilities. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI tutor system according to an embodiment of the present invention is a system that provides customized education based on the learning style and abilities of elementary school students. This AI tutor system collects student learning data, and the AI ​​analyzes this data to adjust the difficulty level of problems and change the format of teaching materials according to the student's progress, thereby providing an optimal learning experience. As a result, the student's learning efficiency improves, their understanding deepens, and their grades improve. It also improves the quality of education by providing parents and teachers with real-time progress reports and feedback. For example, the AI ​​tutor system collects information on the student's correct answer rate, answer time, and learning style for problems they have solved. This data is input into the AI. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the student's learning style and abilities and generates an individualized learning plan. For example, it identifies the student's strengths and weaknesses and customizes the learning content accordingly. Furthermore, it adjusts the difficulty level of problems according to the student's progress. For example, if the student shows a certain level of understanding, the difficulty level of the next problem is increased. On the other hand, if understanding is insufficient, the difficulty level is decreased. In this way, problems are provided that match the student's learning pace. In addition, the format of the teaching materials is changed to accommodate the student's learning style. For example, students who prefer visual learning will be provided with materials that make extensive use of diagrams and graphs. Conversely, students who prefer auditory learning will be provided with materials that utilize audio and video. In this way, materials optimized for each student's learning style will be provided. Furthermore, real-time progress reports and feedback will be provided to parents and teachers. For example, the student's learning status and grade progression will be displayed in graphs, allowing parents and teachers to understand the student's progress. Advice and support will also be provided as needed. This system will improve students' learning efficiency and deepen their understanding, leading to improved grades. In addition, providing real-time progress reports and feedback to parents and teachers will improve the quality of education. For example, by providing specific advice on how students can overcome areas where they struggle, learning effectiveness can be maximized. As a result, the AI ​​tutor system can provide customized education based on each student's learning style and abilities.

[0029] The AI ​​tutor system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an adjustment unit, a format change unit, and a provision unit. The collection unit collects student learning data. The collection unit collects information such as the correct answer rate, answer time, and learning style of the problems solved by the student. The collection unit calculates the correct answer rate of the problems solved by the student and records the answer time. The collection unit can also collect information about the student's learning style. For example, the collection unit identifies whether the student prefers visual or auditory learning. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using statistical analysis or machine learning algorithms. The analysis unit identifies the student's learning style and abilities. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit generates an individualized learning plan based on the identified learning style and abilities. The generation unit identifies areas in which the student excels and areas in which they struggle, and customizes the learning content accordingly. The adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit. For example, the adjustment unit adjusts the difficulty level of the problems according to the student's progress. For example, if the student shows a certain level of understanding, the adjustment unit increases the difficulty level of the next problem. On the other hand, if the understanding is insufficient, it decreases the difficulty level. The format change unit changes the format of the learning materials based on the difficulty level of the problems adjusted by the adjustment unit. For example, the format change unit provides a format of learning materials that corresponds to the student's learning style. For example, for students who prefer visual learning, the format change unit provides materials that make extensive use of diagrams and graphs. On the other hand, for students who prefer auditory learning, it provides materials that use audio and video. The delivery unit provides progress reports and feedback based on the format of the learning materials changed by the format change unit. For example, the delivery unit provides real-time progress reports and feedback to parents and teachers. For example, the delivery unit displays the student's learning status and grade trends in graphs so that parents and teachers can understand the student's learning status. The delivery unit can also provide advice and support as needed.As a result, the AI ​​tutor system according to the embodiment can provide an optimal learning experience by collecting and analyzing student learning data, generating learning plans, adjusting the difficulty level of problems, changing the format of learning materials, and providing progress reports and feedback.

[0030] The data collection unit collects student learning data. For example, it collects information such as the accuracy rate and time taken to answer problems, and information about the student's learning style. Specifically, the data collection unit calculates the accuracy rate for each problem the student solves and records in detail the time taken to answer. This allows for an accurate understanding of the student's level of comprehension and problem-solving speed. The data collection unit also collects information about the student's learning style. For example, it monitors the student's behavior and reactions during learning to determine whether they prefer visual or auditory learning. Students who prefer visual learning tend to show high interest in materials that make extensive use of diagrams and graphs, while students who prefer auditory learning tend to show high interest in materials that use audio and video. The data collection unit centrally manages this data and stores it in a database that is updated in real time. Furthermore, the data collection unit also collects students' learning history and past performance data, allowing for an understanding of long-term learning trends. This enables the data collection unit to comprehensively understand the student's learning situation and provide foundational data to address individual learning needs.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data using statistical analysis and machine learning algorithms. Specifically, it uses statistical analysis to analyze the distribution of students' correct answer rates and answer times to evaluate their learning progress and level of understanding. It also uses machine learning algorithms to identify students' learning styles and abilities. For example, it uses clustering algorithms to group students based on their learning styles and abilities and proposes an optimal learning plan for each group. Furthermore, the analysis unit can predict students' learning patterns and trends based on past learning data. For example, it uses regression models to predict the trend of students' performance and estimate their future learning progress. It also uses anomaly detection algorithms to detect unusual learning patterns and abnormal data, enabling early detection of problems. In this way, the analysis unit can analyze the collected data from multiple angles and accurately grasp the students' learning situation.

[0032] The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. For example, the generation unit generates individualized learning plans based on identified learning styles and abilities. Specifically, it identifies areas where students excel and areas where they struggle, and customizes the learning content accordingly. For example, it provides more advanced problems and applied tasks in areas where students excel, and basic problems and supplementary materials in areas where they struggle. The generation unit can also adjust the progress schedule of the learning plan according to the student's learning pace and goals. For example, it sets short-term goals and presents specific learning steps to achieve them. Furthermore, the generation unit can flexibly modify the learning plan based on student feedback. This allows the generation unit to provide each student with an optimal learning plan and support effective learning.

[0033] The adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit. For example, the adjustment unit adjusts the difficulty level of the problems according to the student's progress. Specifically, if the student shows a certain level of understanding, the difficulty level of the next problem is increased. On the other hand, if the understanding is insufficient, the difficulty level is decreased. For example, if the student can accurately answer basic problems, applied problems or more complex problems are provided. Also, for areas where the student struggles, basic problems are repeatedly provided to support deepening understanding. Furthermore, the adjustment unit can also adjust the difficulty level of the problems considering the student's learning pace and motivation. For example, if the student is highly motivated to learn, challenging problems are provided to maintain their motivation. On the other hand, if the student shows low motivation to learn, relatively easy problems are provided to allow them to gain successful experiences and stimulate their motivation to learn. In this way, the adjustment unit can provide flexible problems according to the student's level of understanding and learning pace, supporting effective learning.

[0034] The formatting unit changes the format of the learning materials based on the difficulty level of the problems adjusted by the adjustment unit. For example, the formatting unit provides learning material formats that correspond to students' learning styles. Specifically, it provides materials that make extensive use of diagrams and graphs for students who prefer visual learning, and materials that use audio and video for students who prefer auditory learning. For example, it provides materials that use colorful diagrams and infographics for students who prefer visual learning, making the information easier to understand visually. It also provides audio explanations and video lectures for students who prefer auditory learning, effectively conveying information through hearing. Furthermore, the formatting unit can flexibly change the format of the learning materials based on student feedback. For example, if a student shows a high level of understanding with a particular format of material, it will prioritize providing materials in that format. On the other hand, if a student does not make progress with a particular format of material, it will provide materials in a different format to maximize learning effectiveness. In this way, the formatting unit can provide learning materials that are optimal for each student's learning style and support effective learning.

[0035] The service provider will provide progress reports and feedback based on the format of the learning materials modified by the format modification service provider. For example, the service provider will provide real-time progress reports and feedback to parents and teachers. Specifically, it will display graphs showing the progress of students' learning and grades so that parents and teachers can understand the students' learning situation. For example, it will visually display graphs showing the progress of students' learning and the progress of their grades so that parents and teachers can understand the learning situation at a glance. The service provider can also provide advice and support as needed. For example, if a student is struggling in a particular area, it will provide additional learning resources and support in that area. Furthermore, the service provider will also provide feedback to the students themselves to increase their motivation to learn. For example, it will provide positive feedback on the goals and progress that students have achieved to maintain their motivation to learn. The service provider can also collect student feedback and use it to improve the overall system. This allows the service provider to provide appropriate feedback to parents, teachers, and students and support effective learning.

[0036] The data collection unit can collect information on the accuracy rate, time taken to solve problems, and learning style of students. For example, the data collection unit can calculate the accuracy rate of problems solved by students and record the time taken to solve them. The data collection unit can also collect information on students' learning styles. For example, the data collection unit can identify whether students prefer visual or auditory learning. This allows for more accurate analysis by collecting detailed student learning data. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the accuracy rate and time taken to solve problems solved by students into an AI, which can then analyze that data.

[0037] The analysis unit can analyze the collected data to identify students' learning styles and abilities. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. The analysis unit can identify students' learning styles and abilities. For example, the analysis unit can determine whether students prefer visual or auditory learning. The analysis unit can also identify students' abilities. For example, the analysis unit can evaluate students' abilities based on their test results and assignment completion. By identifying students' learning styles and abilities, individualized learning plans can be generated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into an AI, which can then analyze that data.

[0038] The generation unit can generate individualized learning plans based on identified learning styles and abilities. For example, the generation unit can identify areas in which students excel and areas in which they struggle, and customize the learning content accordingly. For example, the generation unit can provide more advanced problems in areas in which students excel and provide basic problems in areas in which they struggle. This improves students' learning efficiency by generating individualized learning plans. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input identified learning styles and abilities into AI, and the AI ​​can generate a learning plan based on that data.

[0039] The adjustment unit can adjust the difficulty level of the problems according to the student's progress. For example, the adjustment unit can adjust the difficulty level of the problems according to the student's progress. For example, if the student shows a certain level of understanding, the adjustment unit can increase the difficulty level of the next problem. On the other hand, if the understanding is insufficient, it can decrease the difficulty level. For example, if the student shows a certain level of understanding, the adjustment unit can increase the difficulty level of the next problem. On the other hand, if the understanding is insufficient, it can decrease the difficulty level. In this way, by adjusting the difficulty level of the problems according to the student's progress, an optimal learning experience can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the student's progress into the AI, and the AI ​​can adjust the difficulty level of the problems based on that data.

[0040] The format modification unit can provide learning materials in formats that correspond to students' learning styles. For example, the format modification unit can provide learning materials that make extensive use of diagrams and graphs for students who prefer visual learning, and materials that use audio and video for students who prefer auditory learning. By providing learning materials in formats that correspond to students' learning styles, learning effectiveness can be maximized. Some or all of the above processing in the format modification unit may be performed using AI, for example, or without AI. For example, the format modification unit can input students' learning styles into AI, and the AI ​​can change the format of the learning materials based on that data.

[0041] 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 can collect learning data during time periods when students have shown high correct answer rates in the past. For example, the data collection unit can collect data based on subjects that students have focused on studying in the past. For example, the data collection unit can collect detailed data on subjects that students have struggled with in the past. By analyzing a student's past learning history, the optimal data collection method can be selected. 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 a student's past learning history into an AI, which can then select the optimal data collection method based on that data.

[0042] 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, the data collection unit will prioritize collecting data related to areas of interest to the student. For example, the data collection unit will filter data based on the learning materials and tools the student is using. This allows for the collection of more relevant data by filtering data 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 without AI. For example, the data collection unit can input the student's current learning environment and areas of interest into the AI, which can then filter the data based on that information.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location when collecting learning data. For example, if the student is at school, the data collection unit will collect data related to the school curriculum. If the student is at home, the data collection unit will collect data suitable for home study. If the student is at the library, the data collection unit will collect data related to library resources. This allows for the collection of more relevant data by considering the student's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location information into the AI, which can then prioritize the collection of highly relevant data based on that information.

[0044] 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 based on learning content shared by students on social media. For example, the data collection unit can collect data related to topics that students have shown interest in on social media. For example, the data collection unit can collect data based on posts from educators and influencers that students follow on social media. This allows for the collection of more 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 into AI, and the AI ​​can collect relevant data based on that data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning data during the analysis. For example, the analysis unit performs a detailed analysis on data for important subjects. For example, the analysis unit performs a simplified analysis on data for supplementary subjects. For example, the analysis unit prioritizes analyzing data of high importance according to the student's progress. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the learning data. 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 learning data into the AI, and the AI ​​can adjust the level of detail of the analysis based on that data.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit applies a numerical analysis algorithm to mathematical data. For example, the analysis unit applies a natural language processing algorithm to language data. For example, the analysis unit applies an experimental results analysis algorithm to science data. By applying different analysis algorithms depending on the category of the training data, more accurate 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 categories of the training data into the AI, and the AI ​​can apply different analysis algorithms based on that data.

[0047] 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 data collected at the end of the semester. For example, the analysis unit may prioritize the analysis of data collected before a test. By determining the priority of analysis based on when the training data was collected, 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 training data collection timing into the AI, and the AI ​​can determine the priority of analysis based on that data.

[0048] The analysis unit can adjust the order of analysis based on the relationships between the training data during the analysis process. For example, the analysis unit may prioritize analyzing data for important subjects. For example, the analysis unit may prioritize analyzing data for subjects that students struggle with. For example, the analysis unit may postpone analyzing data for subjects that students excel at. By adjusting the order of analysis based on the relationships between the training data, more effective analysis becomes possible. 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 relationships between the training data into the AI, and the AI ​​can adjust the order of analysis based on that data.

[0049] The generation unit can adjust the level of detail in a learning plan based on the student's strengths and weaknesses when generating the plan. For example, the generation unit can generate a detailed learning plan for subjects the student excels at. For example, the generation unit can generate a concise learning plan for subjects the student struggles with. For example, the generation unit can generate a learning plan that balances the student's strengths and weaknesses. By adjusting the level of detail in the plan based on the student's strengths and weaknesses, a more effective learning plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's strengths and weaknesses into the AI, and the AI ​​can adjust the level of detail in the plan based on that data.

[0050] The generation unit can apply different generation algorithms to students according to their learning style when generating learning plans. For example, for students who prefer visual learning, the generation unit will generate a learning plan that makes extensive use of diagrams and graphs. For students who prefer auditory learning, the generation unit will generate a learning plan that makes extensive use of audio and video. For students who prefer experiential learning, the generation unit will generate a learning plan that includes many practical tasks. By applying different generation algorithms according to students' learning styles, it is possible to provide more effective learning plans. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's learning style into AI, and the AI ​​can apply different generation algorithms based on that data.

[0051] The generation unit can determine the priority of study plans based on the student's learning history when generating study plans. For example, the generation unit can generate study plans that prioritize subjects the student has struggled with in the past. For example, the generation unit can generate study plans that postpone subjects the student has excelled at in the past. For example, the generation unit can generate balanced study plans based on the student's learning history. This allows for the provision of more effective study plans by determining the priority of plans based on the student's learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's learning history into AI, and the AI ​​can determine the priority of plans based on that data.

[0052] The generation unit can adjust the order of the learning plan based on relevant student data when generating the learning plan. For example, the generation unit can generate a learning plan that places the student's strongest subjects first. For example, the generation unit can generate a learning plan that postpones the student's weakest subjects. For example, the generation unit can generate a learning plan with a balanced order based on relevant student data. By adjusting the order of the plan based on relevant student data, a more effective learning plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant student data into AI, and the AI ​​can adjust the order of the plan based on that data.

[0053] The adjustment unit can select the optimal difficulty level when adjusting the difficulty of a problem by referring to the student's past answer history. For example, the adjustment unit may increase the difficulty level of problems in which the student has answered correctly in the past. For example, the adjustment unit may decrease the difficulty level of problems in which the student has struggled in the past. For example, the adjustment unit may provide problems of a balanced difficulty level based on the student's answer history. In this way, by referring to the student's past answer history, it is possible to provide problems of the optimal difficulty level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit may input the student's past answer history into the AI, and the AI ​​may select the optimal difficulty level based on that data.

[0054] The adjustment unit can customize the difficulty level of problems based on the student's current learning status when adjusting the difficulty level of the problems. For example, if the student understands the current learning material, the adjustment unit will provide a difficult problem. For example, if the student is struggling with the current learning material, the adjustment unit will provide a difficult problem. For example, the adjustment unit will provide a problem of appropriate difficulty based on the student's current learning status. This allows for more effective learning by customizing the difficulty level based on the student's current learning status. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the student's current learning status into the AI, and the AI ​​can customize the difficulty level based on that data.

[0055] The adjustment unit can select the optimal difficulty level when adjusting the difficulty of problems, taking into account the student's geographical location information. For example, if a student is at school, the adjustment unit provides problems with a difficulty level that matches the school curriculum. For example, if a student is at home, the adjustment unit provides problems with a difficulty level suitable for home study. For example, if a student is at the library, the adjustment unit provides problems with a difficulty level that matches the library's resources. In this way, by taking into account the student's geographical location information, the adjustment unit can provide problems with an optimal difficulty level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the student's geographical location information into the AI, and the AI ​​can select the optimal difficulty level based on that data.

[0056] The adjustment unit can adjust the difficulty of questions by analyzing students' social media activity. For example, the adjustment unit can adjust the difficulty based on the learning content that students have shared on social media. For example, the adjustment unit can provide questions of a difficulty level related to topics that students have shown interest in on social media. For example, the adjustment unit can adjust the difficulty level based on posts from educators and influencers that students follow on social media. In this way, by analyzing students' social media activity, it is possible to provide questions of the optimal difficulty level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input students' social media activity into AI, and the AI ​​can adjust the difficulty level based on that data.

[0057] The format change unit can select the optimal format based on the student's learning style when changing the format of teaching materials. For example, for students who prefer visual learning, the format change unit provides teaching materials that make extensive use of diagrams and graphs. For students who prefer auditory learning, the format change unit provides teaching materials that make extensive use of audio and video. For students who prefer experiential learning, the format change unit provides teaching materials that include many practical tasks. By selecting the optimal format based on the student's learning style, more effective learning can be provided. Some or all of the above processing in the format change unit may be performed using AI, for example, or without AI. For example, the format change unit can input the student's learning style into AI, and the AI ​​can select the optimal format based on that data.

[0058] The format modification unit can customize the format of learning materials based on the student's current learning status when the format of the materials is changed. For example, if the student understands the current learning content, the format modification unit will provide detailed materials. For example, if the student is struggling with the current learning content, the format modification unit will provide simple materials. For example, based on the student's current learning status, the format modification unit will provide materials with an appropriate amount of information. In this way, by customizing the format based on the student's current learning status, more effective learning can be provided. Some or all of the above processing in the format modification unit may be performed using AI, for example, or without AI. For example, the format modification unit can input the student's current learning status into AI, and the AI ​​can customize the format based on that data.

[0059] The format conversion unit can select the optimal format when changing the format of teaching materials, taking into account the student's geographical location information. For example, if a student is at school, the format conversion unit provides teaching materials that match the school curriculum. For example, if a student is at home, the format conversion unit provides teaching materials suitable for home study. For example, if a student is at the library, the format conversion unit provides teaching materials that match the library's resources. In this way, the optimal format of teaching materials can be provided by taking into account the student's geographical location information. Some or all of the above processing in the format conversion unit may be performed using AI, for example, or without using AI. For example, the format conversion unit can input the student's geographical location information into the AI, and the AI ​​can select the optimal format based on that data.

[0060] The formatting unit can analyze students' social media activity and adjust the format when changing the format of teaching materials. For example, the formatting unit can adjust the format of teaching materials based on learning content shared by students on social media. For example, the formatting unit can provide teaching materials related to topics that students have shown interest in on social media. For example, the formatting unit can adjust the format of teaching materials based on posts from educators and influencers that students follow on social media. This allows for the provision of teaching materials in the most optimal format by analyzing students' social media activity. Some or all of the above processing in the formatting unit may be performed using AI, for example, or not using AI. For example, the formatting unit can input students' social media activity into AI, and the AI ​​can adjust the format based on that data.

[0061] The service provider can select the optimal delivery method by referring to the student's past learning history when providing progress reports and feedback. For example, the service provider can provide detailed progress reports based on the student's past learning history. For example, the service provider can provide concise feedback based on the student's past learning history. For example, the service provider can provide balanced progress reports and feedback based on the student's past learning history. This allows the service provider to provide optimal progress reports and feedback by referring to the student's past learning history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the student's past learning history into AI, and the AI ​​can select the optimal delivery method based on that data.

[0062] The delivery unit can customize the delivery method based on the student's current learning status when providing progress reports and feedback. For example, if the student understands the current learning material, the delivery unit will provide detailed progress reports and feedback. For example, if the student is struggling with the current learning material, the delivery unit will provide concise progress reports and feedback. For example, the delivery unit will provide progress reports and feedback with an appropriate amount of information based on the student's current learning status. This allows for more effective progress reports and feedback to be provided by customizing the delivery method based on the student's current learning status. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the student's current learning status into AI, and the AI ​​can customize the delivery method based on that data.

[0063] The service provider can select the optimal delivery method for progress reports and feedback, taking into account the student's geographical location. For example, if the student is at school, the service provider will provide progress reports and feedback aligned with the school curriculum. If the student is at home, the service provider will provide progress reports and feedback suitable for home study. If the student is at the library, the service provider will provide progress reports and feedback aligned with the library's resources. This allows for the provision of optimal progress reports and feedback by considering the student's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the student's geographical location information into the AI, which can then select the optimal delivery method based on that data.

[0064] The service provider can analyze students' social media activity and adjust the delivery method when providing progress reports and feedback. For example, the service provider can provide progress reports and feedback based on learning content shared by students on social media. For example, the service provider can provide progress reports and feedback related to topics that students have shown interest in on social media. For example, the service provider can provide progress reports and feedback based on posts from educators and influencers that students follow on social media. This allows the service provider to provide optimal progress reports and feedback by analyzing students' social media activity. 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 students' social media activity into AI, and the AI ​​can adjust the delivery method based on that data.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The data collection unit can also consider students' health conditions when collecting their learning data. For example, if a student complains of feeling unwell, the collection of learning data for that day will be suspended. Similarly, if a student hasn't had enough sleep, the collection of learning data can be delayed. Furthermore, if a student is tired after exercise, the data can be collected after they have rested.

[0067] The analysis unit can consider not only students' learning history but also their interests and hobbies when analyzing collected data. For example, if a student is interested in a particular sport or music, it can provide example problems and questions related to that interest. It can also increase students' motivation to learn by providing learning materials that incorporate their favorite characters or stories. Furthermore, it can provide information about occupations and careers related to the fields that students are interested in.

[0068] The generation unit can consider not only the student's learning style but also their daily routine when generating a study plan. For example, if a student has a morning routine, it can create a plan that allows them to concentrate on studying during the morning hours. Similarly, for students with a night owl routine, it can create a plan that concentrates their studies during the evening hours. Furthermore, it can take into account the student's school schedule and extracurricular activities to provide a manageable study plan.

[0069] The adjustment unit can consider not only students' emotions but also their learning environment when adjusting the difficulty level of problems. For example, if a student is studying in a quiet environment, it can provide more difficult problems. On the other hand, if a student is studying in a noisy environment, it can provide easier problems. It can also provide advice on how to create an environment where students can concentrate on their studies. Furthermore, it can provide guidelines for students to find an environment suitable for their studies.

[0070] The format change function can consider not only students' feelings but also their learning history when changing the format of learning materials. For example, if a student has performed well with visual materials in the past, visual materials can be prioritized. Conversely, if a student has performed well with auditory materials in the past, auditory materials can be prioritized. Furthermore, by avoiding learning materials in formats that students have struggled with in the past, learning stress can be reduced. In addition, the optimal learning material format can be suggested based on the student's learning history.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The data collection unit collects student learning data. For example, it collects information on the accuracy rate of the problems students solve, the time it takes to solve them, and their learning style. The data collection unit can also identify whether students prefer visual or auditory learning. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data using statistical analysis and machine learning algorithms to identify students' learning styles and abilities. Step 3: The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. For example, it generates an individualized learning plan based on the identified learning style and abilities, and customizes the learning content by identifying the student's strengths and weaknesses. Step 4: The adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit. For example, it adjusts the difficulty level of the problems according to the student's progress, raising or lowering the difficulty level according to their level of understanding. Step 5: The formatting unit changes the format of the teaching materials based on the difficulty level of the questions adjusted by the adjustment unit. For example, students who prefer visual learning will be provided with materials that make extensive use of diagrams and graphs, while students who prefer auditory learning will be provided with materials that use audio and video. Step 6: The delivery team provides progress reports and feedback based on the format of the materials modified by the format modification team. For example, they provide parents and teachers with real-time progress reports and feedback, and display graphs showing the progress of students' learning and grades. They also provide advice and support as needed.

[0073] (Example of form 2) An AI tutor system according to an embodiment of the present invention is a system that provides customized education based on the learning style and abilities of elementary school students. This AI tutor system collects student learning data, and the AI ​​analyzes this data to adjust the difficulty level of problems and change the format of teaching materials according to the student's progress, thereby providing an optimal learning experience. As a result, the student's learning efficiency improves, their understanding deepens, and their grades improve. It also improves the quality of education by providing parents and teachers with real-time progress reports and feedback. For example, the AI ​​tutor system collects information on the student's correct answer rate, answer time, and learning style for problems they have solved. This data is input into the AI. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the student's learning style and abilities and generates an individualized learning plan. For example, it identifies the student's strengths and weaknesses and customizes the learning content accordingly. Furthermore, it adjusts the difficulty level of problems according to the student's progress. For example, if the student shows a certain level of understanding, the difficulty level of the next problem is increased. On the other hand, if understanding is insufficient, the difficulty level is decreased. In this way, problems are provided that match the student's learning pace. In addition, the format of the teaching materials is changed to accommodate the student's learning style. For example, students who prefer visual learning will be provided with materials that make extensive use of diagrams and graphs. Conversely, students who prefer auditory learning will be provided with materials that utilize audio and video. In this way, materials optimized for each student's learning style will be provided. Furthermore, real-time progress reports and feedback will be provided to parents and teachers. For example, the student's learning status and grade progression will be displayed in graphs, allowing parents and teachers to understand the student's progress. Advice and support will also be provided as needed. This system will improve students' learning efficiency and deepen their understanding, leading to improved grades. In addition, providing real-time progress reports and feedback to parents and teachers will improve the quality of education. For example, by providing specific advice on how students can overcome areas where they struggle, learning effectiveness can be maximized. As a result, the AI ​​tutor system can provide customized education based on each student's learning style and abilities.

[0074] The AI ​​tutor system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an adjustment unit, a format change unit, and a provision unit. The collection unit collects student learning data. The collection unit collects information such as the correct answer rate, answer time, and learning style of the problems solved by the student. The collection unit calculates the correct answer rate of the problems solved by the student and records the answer time. The collection unit can also collect information about the student's learning style. For example, the collection unit identifies whether the student prefers visual or auditory learning. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using statistical analysis or machine learning algorithms. The analysis unit identifies the student's learning style and abilities. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit generates an individualized learning plan based on the identified learning style and abilities. The generation unit identifies areas in which the student excels and areas in which they struggle, and customizes the learning content accordingly. The adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit. For example, the adjustment unit adjusts the difficulty level of the problems according to the student's progress. For example, if the student shows a certain level of understanding, the adjustment unit increases the difficulty level of the next problem. On the other hand, if the understanding is insufficient, it decreases the difficulty level. The format change unit changes the format of the learning materials based on the difficulty level of the problems adjusted by the adjustment unit. For example, the format change unit provides a format of learning materials that corresponds to the student's learning style. For example, for students who prefer visual learning, the format change unit provides materials that make extensive use of diagrams and graphs. On the other hand, for students who prefer auditory learning, it provides materials that use audio and video. The delivery unit provides progress reports and feedback based on the format of the learning materials changed by the format change unit. For example, the delivery unit provides real-time progress reports and feedback to parents and teachers. For example, the delivery unit displays the student's learning status and grade trends in graphs so that parents and teachers can understand the student's learning status. The delivery unit can also provide advice and support as needed.As a result, the AI ​​tutor system according to the embodiment can provide an optimal learning experience by collecting and analyzing student learning data, generating learning plans, adjusting the difficulty level of problems, changing the format of learning materials, and providing progress reports and feedback.

[0075] The data collection unit collects student learning data. For example, it collects information such as the accuracy rate and time taken to answer problems, and information about the student's learning style. Specifically, the data collection unit calculates the accuracy rate for each problem the student solves and records in detail the time taken to answer. This allows for an accurate understanding of the student's level of comprehension and problem-solving speed. The data collection unit also collects information about the student's learning style. For example, it monitors the student's behavior and reactions during learning to determine whether they prefer visual or auditory learning. Students who prefer visual learning tend to show high interest in materials that make extensive use of diagrams and graphs, while students who prefer auditory learning tend to show high interest in materials that use audio and video. The data collection unit centrally manages this data and stores it in a database that is updated in real time. Furthermore, the data collection unit also collects students' learning history and past performance data, allowing for an understanding of long-term learning trends. This enables the data collection unit to comprehensively understand the student's learning situation and provide foundational data to address individual learning needs.

[0076] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data using statistical analysis and machine learning algorithms. Specifically, it uses statistical analysis to analyze the distribution of students' correct answer rates and answer times to evaluate their learning progress and level of understanding. It also uses machine learning algorithms to identify students' learning styles and abilities. For example, it uses clustering algorithms to group students based on their learning styles and abilities and proposes an optimal learning plan for each group. Furthermore, the analysis unit can predict students' learning patterns and trends based on past learning data. For example, it uses regression models to predict the trend of students' performance and estimate their future learning progress. It also uses anomaly detection algorithms to detect unusual learning patterns and abnormal data, enabling early detection of problems. In this way, the analysis unit can analyze the collected data from multiple angles and accurately grasp the students' learning situation.

[0077] The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. For example, the generation unit generates individualized learning plans based on identified learning styles and abilities. Specifically, it identifies areas where students excel and areas where they struggle, and customizes the learning content accordingly. For example, it provides more advanced problems and applied tasks in areas where students excel, and basic problems and supplementary materials in areas where they struggle. The generation unit can also adjust the progress schedule of the learning plan according to the student's learning pace and goals. For example, it sets short-term goals and presents specific learning steps to achieve them. Furthermore, the generation unit can flexibly modify the learning plan based on student feedback. This allows the generation unit to provide each student with an optimal learning plan and support effective learning.

[0078] The adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit. For example, the adjustment unit adjusts the difficulty level of the problems according to the student's progress. Specifically, if the student shows a certain level of understanding, the difficulty level of the next problem is increased. On the other hand, if the understanding is insufficient, the difficulty level is decreased. For example, if the student can accurately answer basic problems, applied problems or more complex problems are provided. Also, for areas where the student struggles, basic problems are repeatedly provided to support deepening understanding. Furthermore, the adjustment unit can also adjust the difficulty level of the problems considering the student's learning pace and motivation. For example, if the student is highly motivated to learn, challenging problems are provided to maintain their motivation. On the other hand, if the student shows low motivation to learn, relatively easy problems are provided to allow them to gain successful experiences and stimulate their motivation to learn. In this way, the adjustment unit can provide flexible problems according to the student's level of understanding and learning pace, supporting effective learning.

[0079] The formatting unit changes the format of the learning materials based on the difficulty level of the problems adjusted by the adjustment unit. For example, the formatting unit provides learning material formats that correspond to students' learning styles. Specifically, it provides materials that make extensive use of diagrams and graphs for students who prefer visual learning, and materials that use audio and video for students who prefer auditory learning. For example, it provides materials that use colorful diagrams and infographics for students who prefer visual learning, making the information easier to understand visually. It also provides audio explanations and video lectures for students who prefer auditory learning, effectively conveying information through hearing. Furthermore, the formatting unit can flexibly change the format of the learning materials based on student feedback. For example, if a student shows a high level of understanding with a particular format of material, it will prioritize providing materials in that format. On the other hand, if a student does not make progress with a particular format of material, it will provide materials in a different format to maximize learning effectiveness. In this way, the formatting unit can provide learning materials that are optimal for each student's learning style and support effective learning.

[0080] The service provider will provide progress reports and feedback based on the format of the learning materials modified by the format modification service provider. For example, the service provider will provide real-time progress reports and feedback to parents and teachers. Specifically, it will display graphs showing the progress of students' learning and grades so that parents and teachers can understand the students' learning situation. For example, it will visually display graphs showing the progress of students' learning and the progress of their grades so that parents and teachers can understand the learning situation at a glance. The service provider can also provide advice and support as needed. For example, if a student is struggling in a particular area, it will provide additional learning resources and support in that area. Furthermore, the service provider will also provide feedback to the students themselves to increase their motivation to learn. For example, it will provide positive feedback on the goals and progress that students have achieved to maintain their motivation to learn. The service provider can also collect student feedback and use it to improve the overall system. This allows the service provider to provide appropriate feedback to parents, teachers, and students and support effective learning.

[0081] The data collection unit can collect information on the accuracy rate, time taken to solve problems, and learning style of students. For example, the data collection unit can calculate the accuracy rate of problems solved by students and record the time taken to solve them. The data collection unit can also collect information on students' learning styles. For example, the data collection unit can identify whether students prefer visual or auditory learning. This allows for more accurate analysis by collecting detailed student learning data. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the accuracy rate and time taken to solve problems solved by students into an AI, which can then analyze that data.

[0082] The analysis unit can analyze the collected data to identify students' learning styles and abilities. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. The analysis unit can identify students' learning styles and abilities. For example, the analysis unit can determine whether students prefer visual or auditory learning. The analysis unit can also identify students' abilities. For example, the analysis unit can evaluate students' abilities based on their test results and assignment completion. By identifying students' learning styles and abilities, individualized learning plans can be generated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into an AI, which can then analyze that data.

[0083] The generation unit can generate individualized learning plans based on identified learning styles and abilities. For example, the generation unit can identify areas in which students excel and areas in which they struggle, and customize the learning content accordingly. For example, the generation unit can provide more advanced problems in areas in which students excel and provide basic problems in areas in which they struggle. This improves students' learning efficiency by generating individualized learning plans. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input identified learning styles and abilities into AI, and the AI ​​can generate a learning plan based on that data.

[0084] The adjustment unit can adjust the difficulty level of the problems according to the student's progress. For example, the adjustment unit can adjust the difficulty level of the problems according to the student's progress. For example, if the student shows a certain level of understanding, the adjustment unit can increase the difficulty level of the next problem. On the other hand, if the understanding is insufficient, it can decrease the difficulty level. For example, if the student shows a certain level of understanding, the adjustment unit can increase the difficulty level of the next problem. On the other hand, if the understanding is insufficient, it can decrease the difficulty level. In this way, by adjusting the difficulty level of the problems according to the student's progress, an optimal learning experience can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the student's progress into the AI, and the AI ​​can adjust the difficulty level of the problems based on that data.

[0085] The format modification unit can provide learning materials in formats that correspond to students' learning styles. For example, the format modification unit can provide learning materials that make extensive use of diagrams and graphs for students who prefer visual learning, and materials that use audio and video for students who prefer auditory learning. By providing learning materials in formats that correspond to students' learning styles, learning effectiveness can be maximized. Some or all of the above processing in the format modification unit may be performed using AI, for example, or without AI. For example, the format modification unit can input students' learning styles into AI, and the AI ​​can change the format of the learning materials based on that data.

[0086] The service provider can provide parents and teachers with real-time progress reports and feedback. For example, the service provider can display a student's learning progress and grade trends in a graph, allowing parents and teachers to understand the student's learning situation. The service provider can also provide advice and support as needed. This improves the quality of education by providing parents and teachers with real-time progress reports and feedback. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines 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 service provider may be performed using AI or not. For example, the service provider can input a student's learning progress and grade trends into an AI, which can then generate progress reports and feedback based on that data.

[0087] The data collection unit can estimate the student's emotions and adjust the timing of learning data collection based on the estimated emotions. For example, if the student is stressed, the data collection unit will collect learning data during times when the student can relax. For example, if the student is focused, the data collection unit will collect learning data at that time. For example, if the student is tired, the data collection unit will collect learning data after a break. By adjusting the timing of learning data collection based on the student's emotions, data can be collected at a more appropriate 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 the student's emotions into the AI, and the AI ​​can adjust the timing of learning data collection based on that data.

[0088] 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 can collect learning data during time periods when students have shown high correct answer rates in the past. For example, the data collection unit can collect data based on subjects that students have focused on studying in the past. For example, the data collection unit can collect detailed data on subjects that students have struggled with in the past. By analyzing a student's past learning history, the optimal data collection method can be selected. 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 a student's past learning history into an AI, which can then select the optimal data collection method based on that data.

[0089] 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, the data collection unit will prioritize collecting data related to areas of interest to the student. For example, the data collection unit will filter data based on the learning materials and tools the student is using. This allows for the collection of more relevant data by filtering data 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 without AI. For example, the data collection unit can input the student's current learning environment and areas of interest into the AI, which can then filter the data based on that information.

[0090] The data collection unit can estimate students' emotions and prioritize the data to collect based on the estimated emotions. For example, if a student is relaxed, the data collection unit will prioritize collecting data on difficult problems. If a student is stressed, the data collection unit will prioritize collecting data on easy problems. If a student is focused, the data collection unit will prioritize collecting data on important subjects. This allows for more effective data collection by prioritizing the data to collect based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input students' emotions into an AI, which can then determine the priority of the data to collect based on that data.

[0091] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location when collecting learning data. For example, if the student is at school, the data collection unit will collect data related to the school curriculum. If the student is at home, the data collection unit will collect data suitable for home study. If the student is at the library, the data collection unit will collect data related to library resources. This allows for the collection of more relevant data by considering the student's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location information into the AI, which can then prioritize the collection of highly relevant data based on that information.

[0092] 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 based on learning content shared by students on social media. For example, the data collection unit can collect data related to topics that students have shown interest in on social media. For example, the data collection unit can collect data based on posts from educators and influencers that students follow on social media. This allows for the collection of more 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 into AI, and the AI ​​can collect relevant data based on that data.

[0093] The analysis unit can estimate the student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the student is relaxed, the analysis unit provides detailed analysis results. For example, if the student is stressed, the analysis unit provides concise analysis results. For example, if the student is focused, the analysis unit provides visually easy-to-understand analysis results. By adjusting the presentation of the analysis based on the student's emotions, more effective analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the student's emotions into the AI, and the AI ​​can adjust the presentation of the analysis based on that data.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning data during the analysis. For example, the analysis unit performs a detailed analysis on data for important subjects. For example, the analysis unit performs a simplified analysis on data for supplementary subjects. For example, the analysis unit prioritizes analyzing data of high importance according to the student's progress. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the learning data. 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 learning data into the AI, and the AI ​​can adjust the level of detail of the analysis based on that data.

[0095] The analysis unit can apply different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit applies a numerical analysis algorithm to mathematical data. For example, the analysis unit applies a natural language processing algorithm to language data. For example, the analysis unit applies an experimental results analysis algorithm to science data. By applying different analysis algorithms depending on the category of the training data, more accurate 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 categories of the training data into the AI, and the AI ​​can apply different analysis algorithms based on that data.

[0096] 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 relaxed, the analysis unit will perform a detailed analysis. For example, if the student is stressed, the analysis unit will perform a concise analysis. For example, if the student is focused, the analysis unit will perform an analysis of an appropriate length. By adjusting the length of the analysis based on the student's emotions, more effective analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the student's emotions into the AI, and the AI ​​can adjust the length of the analysis based on that data.

[0097] 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 data collected at the end of the semester. For example, the analysis unit may prioritize the analysis of data collected before a test. By determining the priority of analysis based on when the training data was collected, 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 training data collection timing into the AI, and the AI ​​can determine the priority of analysis based on that data.

[0098] The analysis unit can adjust the order of analysis based on the relationships between the training data during the analysis process. For example, the analysis unit may prioritize analyzing data for important subjects. For example, the analysis unit may prioritize analyzing data for subjects that students struggle with. For example, the analysis unit may postpone analyzing data for subjects that students excel at. By adjusting the order of analysis based on the relationships between the training data, more effective analysis becomes possible. 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 relationships between the training data into the AI, and the AI ​​can adjust the order of analysis based on that data.

[0099] The generation unit can estimate the student's emotions and adjust the method of generating the learning plan based on the estimated emotions. For example, if the student is relaxed, the generation unit generates a detailed learning plan. For example, if the student is stressed, the generation unit generates a concise learning plan. For example, if the student is focused, the generation unit generates a visually easy-to-understand learning plan. This allows for the provision of more effective learning plans by adjusting the method of generating the learning plan based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not using AI. For example, the generation unit can input the student's emotions into an AI, and the AI ​​can adjust the method of generating the learning plan based on that data.

[0100] The generation unit can adjust the level of detail in a learning plan based on the student's strengths and weaknesses when generating the plan. For example, the generation unit can generate a detailed learning plan for subjects the student excels at. For example, the generation unit can generate a concise learning plan for subjects the student struggles with. For example, the generation unit can generate a learning plan that balances the student's strengths and weaknesses. By adjusting the level of detail in the plan based on the student's strengths and weaknesses, a more effective learning plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's strengths and weaknesses into the AI, and the AI ​​can adjust the level of detail in the plan based on that data.

[0101] The generation unit can apply different generation algorithms to students according to their learning style when generating learning plans. For example, for students who prefer visual learning, the generation unit will generate a learning plan that makes extensive use of diagrams and graphs. For students who prefer auditory learning, the generation unit will generate a learning plan that makes extensive use of audio and video. For students who prefer experiential learning, the generation unit will generate a learning plan that includes many practical tasks. By applying different generation algorithms according to students' learning styles, it is possible to provide more effective learning plans. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's learning style into AI, and the AI ​​can apply different generation algorithms based on that data.

[0102] The generation unit can estimate the student's emotions and adjust the length of the study plan based on the estimated emotions. For example, if the student is relaxed, the generation unit will generate a longer study plan. For example, if the student is stressed, the generation unit will generate a shorter study plan. For example, if the student is focused, the generation unit will generate a study plan of appropriate length. By adjusting the length of the study plan based on the student's emotions, a more effective study plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input the student's emotions into the AI, and the AI ​​can adjust the length of the study plan based on that data.

[0103] The generation unit can determine the priority of study plans based on the student's learning history when generating study plans. For example, the generation unit can generate study plans that prioritize subjects the student has struggled with in the past. For example, the generation unit can generate study plans that postpone subjects the student has excelled at in the past. For example, the generation unit can generate balanced study plans based on the student's learning history. This allows for the provision of more effective study plans by determining the priority of plans based on the student's learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's learning history into AI, and the AI ​​can determine the priority of plans based on that data.

[0104] The generation unit can adjust the order of the learning plan based on relevant student data when generating the learning plan. For example, the generation unit can generate a learning plan that places the student's strongest subjects first. For example, the generation unit can generate a learning plan that postpones the student's weakest subjects. For example, the generation unit can generate a learning plan with a balanced order based on relevant student data. By adjusting the order of the plan based on relevant student data, a more effective learning plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant student data into AI, and the AI ​​can adjust the order of the plan based on that data.

[0105] The adjustment unit can estimate a student's emotions and adjust the difficulty level of the problems based on the estimated emotions. For example, if a student is relaxed, the adjustment unit will provide a difficult problem. For example, if a student is stressed, the adjustment unit will provide a difficult problem. For example, if a student is focused, the adjustment unit will provide a problem of moderate difficulty. This allows for more effective learning by adjusting the difficulty level of the problems based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input the student's emotions into the AI, and the AI ​​can adjust the difficulty level of the problems based on that data.

[0106] The adjustment unit can select the optimal difficulty level when adjusting the difficulty of a problem by referring to the student's past answer history. For example, the adjustment unit may increase the difficulty level of problems in which the student has answered correctly in the past. For example, the adjustment unit may decrease the difficulty level of problems in which the student has struggled in the past. For example, the adjustment unit may provide problems of a balanced difficulty level based on the student's answer history. In this way, by referring to the student's past answer history, it is possible to provide problems of the optimal difficulty level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit may input the student's past answer history into the AI, and the AI ​​may select the optimal difficulty level based on that data.

[0107] The adjustment unit can customize the difficulty level of problems based on the student's current learning status when adjusting the difficulty level of the problems. For example, if the student understands the current learning material, the adjustment unit will provide a difficult problem. For example, if the student is struggling with the current learning material, the adjustment unit will provide a difficult problem. For example, the adjustment unit will provide a problem of appropriate difficulty based on the student's current learning status. This allows for more effective learning by customizing the difficulty level based on the student's current learning status. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the student's current learning status into the AI, and the AI ​​can customize the difficulty level based on that data.

[0108] The adjustment unit can estimate a student's emotions and prioritize the difficulty level of problems based on the estimated emotions. For example, if a student is relaxed, the adjustment unit will prioritize providing more difficult problems. For example, if a student is stressed, the adjustment unit will prioritize providing less difficult problems. For example, if a student is focused, the adjustment unit will prioritize providing problems of moderate difficulty. This allows for more effective learning by prioritizing problem difficulty based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input the student's emotions into an AI, and the AI ​​can determine the priority of problem difficulty based on that data.

[0109] The adjustment unit can select the optimal difficulty level when adjusting the difficulty of problems, taking into account the student's geographical location information. For example, if a student is at school, the adjustment unit provides problems with a difficulty level that matches the school curriculum. For example, if a student is at home, the adjustment unit provides problems with a difficulty level suitable for home study. For example, if a student is at the library, the adjustment unit provides problems with a difficulty level that matches the library's resources. In this way, by taking into account the student's geographical location information, the adjustment unit can provide problems with an optimal difficulty level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the student's geographical location information into the AI, and the AI ​​can select the optimal difficulty level based on that data.

[0110] The adjustment unit can adjust the difficulty of questions by analyzing students' social media activity. For example, the adjustment unit can adjust the difficulty based on the learning content that students have shared on social media. For example, the adjustment unit can provide questions of a difficulty level related to topics that students have shown interest in on social media. For example, the adjustment unit can adjust the difficulty level based on posts from educators and influencers that students follow on social media. In this way, by analyzing students' social media activity, it is possible to provide questions of the optimal difficulty level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input students' social media activity into AI, and the AI ​​can adjust the difficulty level based on that data.

[0111] The formatting unit can estimate students' emotions and adjust the format of the learning materials based on those estimated emotions. For example, if a student is relaxed, the formatting unit can provide visually rich materials. If a student is stressed, the formatting unit can provide simple and easy-to-understand materials. If a student is focused, the formatting unit can provide materials containing detailed information. This allows for more effective learning by adjusting the format of the learning materials based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input students' emotions into an AI, which can then adjust the format of the learning materials based on that data.

[0112] The format change unit can select the optimal format based on the student's learning style when changing the format of teaching materials. For example, for students who prefer visual learning, the format change unit provides teaching materials that make extensive use of diagrams and graphs. For students who prefer auditory learning, the format change unit provides teaching materials that make extensive use of audio and video. For students who prefer experiential learning, the format change unit provides teaching materials that include many practical tasks. By selecting the optimal format based on the student's learning style, more effective learning can be provided. Some or all of the above processing in the format change unit may be performed using AI, for example, or without AI. For example, the format change unit can input the student's learning style into AI, and the AI ​​can select the optimal format based on that data.

[0113] The format modification unit can customize the format of learning materials based on the student's current learning status when the format of the materials is changed. For example, if the student understands the current learning content, the format modification unit will provide detailed materials. For example, if the student is struggling with the current learning content, the format modification unit will provide simple materials. For example, based on the student's current learning status, the format modification unit will provide materials with an appropriate amount of information. In this way, by customizing the format based on the student's current learning status, more effective learning can be provided. Some or all of the above processing in the format modification unit may be performed using AI, for example, or without AI. For example, the format modification unit can input the student's current learning status into AI, and the AI ​​can customize the format based on that data.

[0114] The formatting unit can estimate students' emotions and prioritize the format of learning materials based on those estimated emotions. For example, if a student is relaxed, the formatting unit will prioritize providing visually rich materials. If a student is stressed, the formatting unit will prioritize providing simple and easy-to-understand materials. If a student is focused, the formatting unit will prioritize providing materials containing detailed information. This allows for more effective learning by prioritizing the format of learning materials based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the formatting unit may be performed using AI or not. For example, the formatting unit can input students' emotions into an AI, which can then determine the priority of learning material formats based on that data.

[0115] The format conversion unit can select the optimal format when changing the format of teaching materials, taking into account the student's geographical location information. For example, if a student is at school, the format conversion unit provides teaching materials that match the school curriculum. For example, if a student is at home, the format conversion unit provides teaching materials suitable for home study. For example, if a student is at the library, the format conversion unit provides teaching materials that match the library's resources. In this way, the optimal format of teaching materials can be provided by taking into account the student's geographical location information. Some or all of the above processing in the format conversion unit may be performed using AI, for example, or without using AI. For example, the format conversion unit can input the student's geographical location information into the AI, and the AI ​​can select the optimal format based on that data.

[0116] The formatting unit can analyze students' social media activity and adjust the format when changing the format of teaching materials. For example, the formatting unit can adjust the format of teaching materials based on learning content shared by students on social media. For example, the formatting unit can provide teaching materials related to topics that students have shown interest in on social media. For example, the formatting unit can adjust the format of teaching materials based on posts from educators and influencers that students follow on social media. This allows for the provision of teaching materials in the most optimal format by analyzing students' social media activity. Some or all of the above processing in the formatting unit may be performed using AI, for example, or not using AI. For example, the formatting unit can input students' social media activity into AI, and the AI ​​can adjust the format based on that data.

[0117] The service provider can estimate a student's emotions and adjust the method of providing progress reports and feedback based on the estimated emotions. For example, if a student is relaxed, the service provider will provide detailed progress reports and feedback. If a student is stressed, the service provider will provide concise progress reports and feedback. If a student is focused, the service provider will provide visually clear progress reports and feedback. This allows for more effective feedback by adjusting the method of providing progress reports and feedback based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input student emotions into an AI, and the AI ​​can adjust the method of providing progress reports and feedback based on that data.

[0118] The service provider can select the optimal delivery method by referring to the student's past learning history when providing progress reports and feedback. For example, the service provider can provide detailed progress reports based on the student's past learning history. For example, the service provider can provide concise feedback based on the student's past learning history. For example, the service provider can provide balanced progress reports and feedback based on the student's past learning history. This allows the service provider to provide optimal progress reports and feedback by referring to the student's past learning history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the student's past learning history into AI, and the AI ​​can select the optimal delivery method based on that data.

[0119] The delivery unit can customize the delivery method based on the student's current learning status when providing progress reports and feedback. For example, if the student understands the current learning material, the delivery unit will provide detailed progress reports and feedback. For example, if the student is struggling with the current learning material, the delivery unit will provide concise progress reports and feedback. For example, the delivery unit will provide progress reports and feedback with an appropriate amount of information based on the student's current learning status. This allows for more effective progress reports and feedback to be provided by customizing the delivery method based on the student's current learning status. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the student's current learning status into AI, and the AI ​​can customize the delivery method based on that data.

[0120] The service provider can estimate a student's emotions and prioritize progress reports and feedback based on the estimated emotions. For example, if a student is relaxed, the service provider will prioritize providing detailed progress reports and feedback. If a student is stressed, the service provider will prioritize providing concise progress reports and feedback. If a student is focused, the service provider will prioritize providing visually clear progress reports and feedback. This allows for more effective feedback by prioritizing progress reports and feedback 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 service provider may be performed using AI or not. For example, the service provider can input student emotions into an AI, which can then use that data to prioritize progress reports and feedback.

[0121] The service provider can select the optimal delivery method for progress reports and feedback, taking into account the student's geographical location. For example, if the student is at school, the service provider will provide progress reports and feedback aligned with the school curriculum. If the student is at home, the service provider will provide progress reports and feedback suitable for home study. If the student is at the library, the service provider will provide progress reports and feedback aligned with the library's resources. This allows for the provision of optimal progress reports and feedback by considering the student's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the student's geographical location information into the AI, which can then select the optimal delivery method based on that data.

[0122] The service provider can analyze students' social media activity and adjust the delivery method when providing progress reports and feedback. For example, the service provider can provide progress reports and feedback based on learning content shared by students on social media. For example, the service provider can provide progress reports and feedback related to topics that students have shown interest in on social media. For example, the service provider can provide progress reports and feedback based on posts from educators and influencers that students follow on social media. This allows the service provider to provide optimal progress reports and feedback by analyzing students' social media activity. 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 students' social media activity into AI, and the AI ​​can adjust the delivery method based on that data.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The system can not only notify parents and teachers of students' learning progress based on their learning data, but also provide feedback to the students themselves. For example, it can display a message that helps students feel a sense of accomplishment when they complete a specific assignment. It can also send encouraging messages when students are working on subjects they find difficult. Furthermore, it can boost students' motivation by awarding them badges or points when they achieve certain learning goals.

[0125] The data collection unit can also consider students' health conditions when collecting their learning data. For example, if a student complains of feeling unwell, the collection of learning data for that day will be suspended. Similarly, if a student hasn't had enough sleep, the collection of learning data can be delayed. Furthermore, if a student is tired after exercise, the data can be collected after they have rested.

[0126] The analysis unit can consider not only students' learning history but also their interests and hobbies when analyzing collected data. For example, if a student is interested in a particular sport or music, it can provide example problems and questions related to that interest. It can also increase students' motivation to learn by providing learning materials that incorporate their favorite characters or stories. Furthermore, it can provide information about occupations and careers related to the fields that students are interested in.

[0127] The generation unit can consider not only the student's learning style but also their daily routine when generating a study plan. For example, if a student has a morning routine, it can create a plan that allows them to concentrate on studying during the morning hours. Similarly, for students with a night owl routine, it can create a plan that concentrates their studies during the evening hours. Furthermore, it can take into account the student's school schedule and extracurricular activities to provide a manageable study plan.

[0128] The adjustment unit can consider not only students' emotions but also their learning environment when adjusting the difficulty level of problems. For example, if a student is studying in a quiet environment, it can provide more difficult problems. On the other hand, if a student is studying in a noisy environment, it can provide easier problems. It can also provide advice on how to create an environment where students can concentrate on their studies. Furthermore, it can provide guidelines for students to find an environment suitable for their studies.

[0129] The format change function can consider not only students' feelings but also their learning history when changing the format of learning materials. For example, if a student has performed well with visual materials in the past, visual materials can be prioritized. Conversely, if a student has performed well with auditory materials in the past, auditory materials can be prioritized. Furthermore, by avoiding learning materials in formats that students have struggled with in the past, learning stress can be reduced. In addition, the optimal learning material format can be suggested based on the student's learning history.

[0130] When providing progress reports and feedback, the support department can consider not only the students' emotions but also their learning goals. For example, if a student achieves a short-term goal, they can send a congratulatory message. If a student is working towards a long-term goal, the support department can provide feedback that evaluates their progress. Furthermore, by providing specific advice and support regarding the goals students have set, their motivation to achieve those goals can be increased.

[0131] The data collection unit can estimate students' emotions and adjust the method of collecting learning data based on the estimated emotions. For example, if a student is relaxed, detailed data can be collected. On the other hand, if a student is stressed, concise data can be collected. Furthermore, if a student is focused, collecting learning data at that time can yield more accurate results. In addition, the frequency of data collection can be adjusted according to the student's emotions.

[0132] The analysis unit can estimate students' emotions when analyzing collected data and adjust the analysis method based on the estimated emotions. For example, if a student is relaxed, a detailed analysis can be performed. On the other hand, if a student is stressed, a concise analysis can be performed. Furthermore, if a student is focused, a detailed analysis can be performed at that time to obtain more accurate results. In addition, the frequency of analysis can be adjusted according to the students' emotions.

[0133] The generation unit can estimate the student's emotions when generating a learning plan and adjust the content of the learning plan based on the estimated emotions. For example, if the student is relaxed, it can generate a learning plan that includes challenging tasks. On the other hand, if the student is stressed, it can generate a learning plan that includes easier tasks. Furthermore, if the student is focused, generating a learning plan at that time can provide more effective learning. In addition, the frequency of the learning plan can be adjusted according to the student's emotions.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The data collection unit collects student learning data. For example, it collects information on the accuracy rate of the problems students solve, the time it takes to solve them, and their learning style. The data collection unit can also identify whether students prefer visual or auditory learning. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data using statistical analysis and machine learning algorithms to identify students' learning styles and abilities. Step 3: The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. For example, it generates an individualized learning plan based on the identified learning style and abilities, and customizes the learning content by identifying the student's strengths and weaknesses. Step 4: The adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit. For example, it adjusts the difficulty level of the problems according to the student's progress, raising or lowering the difficulty level according to their level of understanding. Step 5: The formatting unit changes the format of the teaching materials based on the difficulty level of the questions adjusted by the adjustment unit. For example, students who prefer visual learning will be provided with materials that make extensive use of diagrams and graphs, while students who prefer auditory learning will be provided with materials that use audio and video. Step 6: The delivery team provides progress reports and feedback based on the format of the materials modified by the format modification team. For example, they provide parents and teachers with real-time progress reports and feedback, and display graphs showing the progress of students' learning and grades. They also provide advice and support as needed.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, adjustment unit, format change unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects student learning data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a learning plan based on the analysis results. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of the problems based on the learning plan. The format change unit is implemented by the control unit 46A of the smart device 14 and changes the format of the teaching materials based on the adjusted difficulty level of the problems. The provision unit is implemented by the control unit 46A of the smart device 14 and provides progress reports and feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, adjustment unit, format change unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects student learning data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a learning plan based on the analysis results. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of the problems based on the learning plan. The format change unit is implemented by the control unit 46A of the smart glasses 214 and changes the format of the teaching materials based on the adjusted difficulty level of the problems. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides progress reports and feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, adjustment unit, format change unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects student learning data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a learning plan based on the analysis results. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the difficulty level of the problems based on the learning plan. The format change unit is implemented by the control unit 46A of the headset terminal 314 and changes the format of the teaching materials based on the adjusted difficulty level of the problems. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides progress reports and feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, adjustment unit, format change unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects student learning data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a learning plan based on the analysis results. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the difficulty level of the problems based on the learning plan. The format change unit is implemented by the control unit 46A of the robot 414 and changes the format of the teaching materials based on the adjusted difficulty level of the problems. The provision unit is implemented by the control unit 46A of the robot 414 and provides progress reports and feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Note 1) The data collection department collects student learning data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a learning plan based on the analysis results obtained by the analysis unit, An adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit, A format change unit that changes the format of the teaching materials based on the difficulty level of the problems adjusted by the adjustment unit, The system includes a provisioning unit that provides progress reports and feedback based on the format of the teaching materials modified by the format modification unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information on the accuracy rate of students' answers to problems, the time it takes to solve them, and their learning style. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify students' learning styles and abilities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate individualized learning plans based on identified learning styles and abilities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, The difficulty level of the problems is adjusted according to the students' progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned format change unit is We provide learning materials in formats that suit students' learning styles. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide parents and teachers with real-time progress reports and feedback. 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 We estimate students' emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting learning data, the system prioritizes collecting highly relevant data by considering students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 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 generating unit is We estimate students' emotions and adjust the method of generating learning plans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a study plan, adjust the level of detail based on the student's strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating learning plans, different generation algorithms are applied depending on the student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating 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 generating unit is When generating a study plan, the plan's priorities are determined based on the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating a learning plan, the order of the plan is adjusted based on relevant student data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, The system estimates students' emotions and adjusts the difficulty level of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, When adjusting the difficulty level of the questions, the system will refer to students' past answer history to select the optimal difficulty level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, When adjusting the difficulty level of a problem, customize the difficulty level based on the student's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, The system estimates students' emotions and prioritizes the difficulty level of problems based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, When adjusting the difficulty level of the problems, the optimal difficulty level will be selected by taking into account the students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, When adjusting the difficulty level of the questions, we analyze students' social media activity to adjust the difficulty accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned format change unit is The system estimates students' emotions and adjusts the format of the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned format change unit is When changing the format of teaching materials, select the most suitable format based on the students' learning styles. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned format change unit is When changing the format of teaching materials, customize the format based on the students' current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned format change unit is The system estimates students' emotions and prioritizes the format of teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned format change unit is When changing the format of teaching materials, the most suitable format will be selected considering the students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned format change unit is When changing the format of teaching materials, we analyze students' social media activity and adjust the format accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, We estimate students' emotions and adjust the way progress reports and feedback are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When providing progress reports and feedback, the most suitable delivery method will be selected by referring to the student's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When providing progress reports and feedback, customize the delivery method based on the student's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned supply unit is, The system estimates students' emotions and prioritizes progress reports and feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When providing progress reports and feedback, the most suitable method of delivery will be selected, taking into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned supply unit is, When providing progress reports and feedback, we analyze students' social media activity and adjust the delivery method accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects student learning data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a learning plan based on the analysis results obtained by the analysis unit, An adjustment unit adjusts the difficulty level of the problems based on the learning plan generated by the generation unit, A format change unit that changes the format of the teaching materials based on the difficulty level of the problems adjusted by the adjustment unit, The system includes a provisioning unit that provides progress reports and feedback based on the format of the teaching materials modified by the format modification unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect information on the accuracy rate of students' answers to problems, the time it takes to solve them, and their learning style. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify students' learning styles and abilities. The system according to feature 1.

4. The generating unit is Generate individualized learning plans based on identified learning styles and abilities. The system according to feature 1.

5. The adjustment unit is, The difficulty level of the problems is adjusted according to the students' progress. The system according to feature 1.

6. The aforementioned format change unit is We provide learning materials in formats that suit students' learning styles. The system according to feature 1.

7. The aforementioned supply unit is, Provide parents and teachers with real-time progress reports and feedback. 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.

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