system

Abstract

The system according to this embodiment aims to analyze students' learning progress and level of understanding in real time and provide optimal learning plans and guidance based on that analysis. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes students' learning progress and level of understanding in real time. The generation unit generates interactive teaching materials based on the data analyzed by the analysis unit. The provision unit provides an optimal learning plan and instruction based on the teaching materials generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the learning progress and understanding degree of students in real time and provide an optimal teaching material and learning plan based on them.

[0005] The system according to the embodiment aims to analyze the learning progress and understanding degree of students in real time and provide an optimal learning plan and guidance based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes students' learning progress and level of understanding in real time. The generation unit generates interactive learning materials based on the data analyzed by the analysis unit. The provision unit provides an optimal learning plan and instruction based on the learning materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze students' learning progress and level of understanding in real time and provide optimal learning plans and guidance based on that analysis. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system in which an AI agent tracks students' learning progress and comprehension based on real-time analysis, generates interactive learning materials including audio and images using multimodal data, and provides learning plans and guidance tailored to each student's understanding. The learning support system analyzes students' learning progress and comprehension in real time and generates interactive learning materials using multimodal data. The generated materials are customized according to the student's level of comprehension. Furthermore, the learning support system analyzes students' learning logs and behavior logs to provide optimal learning plans and guidance. This reduces the burden on teachers while improving the quality of each student's learning. For example, the learning support system analyzes students' comprehension and generates materials to supplement areas where understanding is lacking. It also formulates long-term learning plans and continuously tracks students' learning progress. This allows students to learn at their own pace and maintain their motivation to learn. Furthermore, the learning support system collects students' interests and goals using conversational AI and provides individualized learning plans. This enables learning based on students' interests and improves the effectiveness of learning. For example, a learning support system analyzes students' interests and generates learning materials based on those interests. This allows students to engage in learning with interest. As described above, by utilizing a learning support system, it is possible to track students' learning progress and comprehension in real time and provide optimal learning plans and instruction. This reduces the burden on teachers while improving the quality of each student's learning. In this way, a learning support system can track students' learning progress and comprehension in real time and provide optimal learning plans and instruction.

[0029] The learning support system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes students' learning progress and comprehension in real time. The analysis unit can, for example, collect and analyze learning logs and behavior logs in real time. The analysis unit can also use AI to analyze students' learning progress and comprehension. The generation unit generates interactive learning materials based on the data analyzed by the analysis unit. The generation unit can, for example, generate learning materials using multimodal data including audio and images. The generation unit can also use AI to generate interactive learning materials. The provision unit provides an optimal learning plan and instruction based on the learning materials generated by the generation unit. The provision unit can, for example, analyze students' learning logs and behavior logs to provide an optimal learning plan and instruction. The provision unit can also use AI to provide an optimal learning plan and instruction. As a result, the learning support system according to this embodiment can analyze students' learning progress and comprehension in real time and provide an optimal learning plan and instruction.

[0030] The analysis unit analyzes students' learning progress and comprehension in real time. Specifically, the analysis unit collects learning logs and behavioral logs generated when students study, and can analyze this data in real time. Learning logs include detailed information such as which materials students studied, for how long, and how they answered each question. Behavioral logs include information about students' actions and reactions during learning, such as where they got stuck and what questions they asked. The analysis unit uses AI to analyze this data and evaluate students' learning progress and comprehension. The AI ​​uses machine learning algorithms to detect changes in students' learning patterns and comprehension, and generates foundational data to provide optimal learning support for each individual student. For example, the AI ​​can analyze how long it took a student to understand a particular concept and what kinds of errors they are repeating, and identify areas where their comprehension is weak. Furthermore, by comparing current learning data with past learning data, the analysis unit can monitor students' learning progress over the long term and evaluate the effectiveness of their learning. This allows the analysis unit to gain a detailed understanding of students' learning situations and provide important information for providing learning support tailored to each individual student.

[0031] The generation unit generates interactive learning materials based on data analyzed by the analysis unit. Specifically, the generation unit can automatically generate optimal learning materials according to students' learning progress and level of understanding. The generation unit utilizes multimodal data, including audio and images, to create visually and aurally effective learning materials. For example, for students with low levels of understanding, the generation unit generates materials that include visually easy-to-understand diagrams and animations to help deepen their understanding. The generation unit can also generate interactive learning materials using AI. The AI ​​analyzes students' learning history and behavior logs to suggest the most suitable learning materials for each individual student. For example, the AI ​​can identify problems that students have previously answered incorrectly or concepts that they do not fully understand, and generate customized practice problems and explanatory videos based on that. Furthermore, the generation unit can update the generated learning materials in real time and respond flexibly to students' learning situations. This allows the generation unit to provide effective learning materials tailored to students' learning needs and improve learning efficiency.

[0032] The provisioning department provides optimal learning plans and instruction based on the materials generated by the generation department. Specifically, the provisioning department analyzes students' learning logs and behavioral logs to create optimal learning plans for each individual student. The provisioning department uses AI to propose optimal learning plans based on students' learning history and level of understanding. For example, the AI ​​can monitor students' learning progress in real time and adjust the learning plan according to their progress. Furthermore, the provisioning department provides specific instruction to students using the generated materials. For example, the provisioning department provides additional explanations and practice problems for concepts that students are struggling to understand, supporting them in deepening their comprehension. The provisioning department can also report on learning progress according to the student's learning status and provide feedback to parents and teachers. In this way, the provisioning department can effectively support students' learning and maximize learning outcomes. Moreover, the provisioning department can use AI to continuously improve learning plans and instructional content, providing optimal support tailored to students' learning needs. In this way, the provisioning department can comprehensively support students' learning and improve the efficiency and effectiveness of learning.

[0033] The data collection unit can collect students' interests and goals using conversational AI. For example, the data collection unit can collect students' interests using surveys or behavioral logs. The data collection unit can also collect students' interests and goals using AI. This allows for the collection of students' interests and goals and the provision of individualized learning plans. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use conversational AI to interact with students and analyze the collected data in order to collect their interests and goals.

[0034] The tracking unit can formulate long-term learning plans and continuously track students' learning progress. The tracking unit can track students' learning progress over periods such as six months or a year. The tracking unit can also use AI to continuously track students' learning progress. This allows for continuous tracking of students' learning progress and the provision of long-term learning plans. Some or all of the above-described processes in the tracking unit may be performed using AI or not. For example, the tracking unit can collect students' learning logs and behavioral logs and use AI to formulate long-term learning plans.

[0035] The supplementary unit can generate learning materials to fill in areas of misunderstanding. For example, the supplementary unit can determine areas of misunderstanding based on test results or quiz accuracy rates and generate learning materials to fill in those areas. The supplementary unit can also use AI to generate learning materials to fill in areas of misunderstanding. This allows for the generation of learning materials to fill in areas of misunderstanding and improve students' comprehension. Some or all of the above-described processes in the supplementary unit may be performed using AI or not. For example, the supplementary unit can input test results into AI, identify areas of misunderstanding, and generate learning materials to fill in those areas.

[0036] The generation unit can generate interactive teaching materials, including audio and images, by utilizing multimodal data. For example, the generation unit can generate interactive teaching materials using audio data or image data. The generation unit can also generate interactive teaching materials utilizing multimodal data using AI. This allows for the generation of more effective interactive teaching materials by utilizing multimodal data. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input audio data or image data into AI and generate interactive teaching materials.

[0037] The service provider can analyze students' learning logs and behavioral logs to provide optimal learning plans and instruction. For example, the service provider can analyze learning logs based on learning time and learning content to provide optimal learning plans. The service provider can also use AI to analyze students' learning logs and behavioral logs to provide optimal learning plans and instruction. This allows the service provider to provide optimal learning plans and instruction by analyzing students' learning logs and behavioral logs. 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 learning logs and behavioral logs into AI to provide optimal learning plans and instruction.

[0038] The analysis unit can analyze a student's past learning history and select the optimal analysis algorithm. For example, the analysis unit can focus its analysis on areas where the student has struggled in the past. The analysis unit can also select an appropriate analysis algorithm based on the student's past performance. The analysis unit can also analyze the student's learning patterns and propose the optimal analysis method. This allows the optimal analysis algorithm to be selected by analyzing the past learning history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the student's past learning history into AI and select the optimal analysis algorithm.

[0039] The analysis unit can filter data based on the student's current learning environment and circumstances when analyzing learning progress and comprehension. For example, if a student is studying at home, the analysis unit can assume a quiet environment. If a student is studying at school, the analysis unit can also take surrounding noise into account. If a student is studying while on the go, the analysis unit can evaluate comprehension in a short amount of time. This allows for more accurate analysis by filtering data based on the current learning environment and circumstances. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the student's current learning environment data into AI and perform filtering.

[0040] The analysis unit can prioritize the analysis of highly relevant data by considering the students' geographical location when analyzing learning progress and comprehension. For example, if a student lives in a specific region, the analysis unit can prioritize the analysis of data related to that region. If a student is overseas, the analysis unit can also analyze data based on the local educational curriculum. If a student lives in an urban area, the analysis unit can also prioritize the analysis of data related to that urban area. In this way, by considering geographical location, the analysis unit can prioritize the analysis of highly relevant data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the students' geographical location information into AI and prioritize the analysis of highly relevant data.

[0041] The analysis unit can analyze students' social media activity and analyze related data when analyzing learning progress and comprehension. For example, the analysis unit can analyze learning content that students share on social media. The analysis unit can also analyze information about educational accounts that students follow on social media. The analysis unit can also analyze the activities of learning groups that students participate in on social media. In this way, by analyzing social media activity, related data can be analyzed. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input students' social media activity data into AI and analyze the related data.

[0042] The generation unit can adjust the level of detail in the learning materials based on the importance of the learning content during material generation. For example, the generation unit can generate materials that include detailed explanations for important learning content. For less important learning content, the generation unit can also generate materials that include concise explanations. The generation unit can also adjust the length and level of detail of the materials according to the importance of the learning content. This allows for the generation of more effective learning materials by adjusting the level of detail based on the importance of the learning content. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input learning content importance data into AI and adjust the level of detail of the materials.

[0043] The generation unit can apply different generation algorithms depending on the category of learning content when generating learning materials. For example, for science learning content, the generation unit can generate materials that include experimental videos. For mathematics learning content, the generation unit can also generate materials that include step-by-step explanations. For history learning content, the generation unit can also generate materials that include timelines and maps. By applying different generation algorithms depending on the category of learning content, more effective learning materials can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input learning content category data into AI and apply different generation algorithms.

[0044] The generation unit can determine the priority of learning materials based on the submission deadlines for the learning content when generating the materials. For example, the generation unit can prioritize generating materials for learning content with an approaching submission deadline. It can also postpone generating materials for learning content with a distant submission deadline. The generation unit can also adjust the order in which materials are generated according to the submission deadlines. This allows for the generation of more effective materials by determining the priority of learning materials based on the submission deadlines. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input learning content submission deadline data into AI to determine the priority of learning materials.

[0045] The generation unit can adjust the order of learning materials based on the relevance of the learning content during material generation. For example, the generation unit can generate materials so that highly relevant learning content can be learned consecutively. The generation unit can also generate materials so that less relevant learning content can be learned separately. The generation unit can also adjust the order of materials according to the relevance of the learning content. By adjusting the order of materials based on the relevance of the learning content, more effective materials can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input data on the relevance of learning content into AI and adjust the order of the materials.

[0046] The service provider can select the optimal delivery method by referring to the student's past learning history when providing learning plans and instruction. For example, the service provider can focus instruction on areas where the student has previously struggled. The service provider can also select an appropriate instruction method based on the student's past performance. The service provider can also analyze the student's learning patterns and propose the optimal instruction method. This allows the service provider to select the optimal delivery method 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 or not. For example, the service provider can input the student's past learning history into AI and select the optimal delivery method.

[0047] The delivery unit can customize the delivery method based on the student's current learning environment when providing learning plans and instruction. For example, if the student is studying at home, the delivery unit can provide online instruction. If the student is studying at school, the delivery unit can provide in-person instruction. If the student is studying while on the go, the delivery unit can provide instruction using mobile devices. This allows for more effective instruction by customizing the delivery method based on the current learning environment. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input data on the student's current learning environment into AI to customize the delivery method.

[0048] The service provider can select the optimal delivery method when providing learning plans and instruction, taking into account the student's geographical location. For example, if a student lives in a specific region, the service provider can prioritize providing data relevant to that region. If a student is overseas, the service provider can provide instruction based on the local educational curriculum. If a student lives in an urban area, the service provider can prioritize providing data for urban areas. In this way, the service provider can select the optimal delivery method by considering geographical location. 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 the student's geographical location information into AI and select the optimal delivery method.

[0049] The service provider can analyze students' social media activity and suggest delivery methods when providing learning plans and instruction. For example, the service provider can provide instruction based on learning content shared by students on social media. The service provider can also provide instruction based on information from educational accounts that students follow on social media. The service provider can also provide instruction based on the activities of learning groups that students participate in on social media. In this way, by analyzing social media activity, the service provider can suggest the most suitable delivery method. 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 students' social media activity data into AI and suggest delivery methods.

[0050] The data collection unit can select the optimal collection method by referring to the student's past behavioral history when collecting interests and goals. For example, the data collection unit can set questions based on areas the student has shown interest in in the past. The data collection unit can also analyze the student's past behavioral history and select an appropriate collection method. The data collection unit can also suggest the optimal method for collecting interests based on the student's learning patterns. This allows the optimal collection method to be selected by referring to past behavioral history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the student's past behavioral history into AI and select the optimal collection method.

[0051] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location when collecting information on their interests and goals. For example, if a student lives in a specific region, the data collection unit can prioritize the collection of data related to that region. If a student is overseas, the data collection unit can also collect data based on the local educational curriculum. If a student lives in an urban area, the data collection unit can also prioritize the collection of data related to that urban area. This allows for the priority collection of highly relevant data by considering geographical location. 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 student's geographical location information into AI and prioritize the collection of highly relevant data.

[0052] The tracking unit can select the optimal tracking method by referring to the student's past learning history when tracking learning progress. For example, the tracking unit can focus on tracking areas where the student has struggled in the past. The tracking unit can also select an appropriate tracking method based on the student's past performance. The tracking unit can also analyze the student's learning patterns and suggest the optimal tracking method. This allows the optimal tracking method to be selected by referring to the past learning history. Some or all of the above processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input the student's past learning history into AI and select the optimal tracking method.

[0053] The tracking unit can select the optimal tracking method when tracking learning progress, taking into account the student's geographical location. For example, if a student lives in a specific region, the tracking unit can prioritize tracking data related to that region. If a student is overseas, the tracking unit can also track based on the local educational curriculum. If a student lives in an urban area, the tracking unit can also prioritize tracking data from urban areas. This allows the system to select the optimal tracking method by considering geographical location. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the student's geographical location information into AI to select the optimal tracking method.

[0054] The supplementary unit can select the optimal generation method by referring to the student's past learning history when generating materials to fill in areas of misunderstanding. For example, the supplementary unit can generate materials that focus on areas where the student has struggled in the past. The supplementary unit can also generate appropriate supplementary materials based on the student's past performance. The supplementary unit can also analyze the student's learning patterns and suggest the optimal supplementary materials. In this way, the optimal supplementary materials can be generated by referring to the past learning history. Some or all of the above processes in the supplementary unit may be performed using AI or not. For example, the supplementary unit can input the student's past learning history into AI and select the optimal generation method.

[0055] The supplementary unit can select the optimal generation method when generating supplementary materials to fill in areas of misunderstanding, taking into account the student's geographical location. For example, if a student lives in a specific region, the supplementary unit can prioritize supplementing with data related to that region. If a student is overseas, the supplementary unit can also supplement based on the local educational curriculum. If a student lives in an urban area, the supplementary unit can also prioritize supplementing with data for urban areas. In this way, by considering geographical location, the optimal supplementary materials can be generated. Some or all of the above processing in the supplementary unit may be performed using AI or not. For example, the supplementary unit can input the student's geographical location information into AI and select the optimal generation method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can not only analyze students' learning progress and comprehension in real time, but also evaluate their motivation to learn. For example, the analysis unit can analyze students' learning logs and behavioral logs to detect changes in their motivation to learn. Furthermore, the analysis unit can analyze students' learning time and learning content selection trends to evaluate the level of their motivation. This allows the learning support system to provide appropriate guidance and feedback tailored to each student's motivation. For example, students with low motivation can be encouraged with messages of support and set short-term goals to improve their motivation to learn.

[0058] The data collection unit can not only gather students' interests and goals using conversational AI, but also their learning style preferences. For example, it can collect whether students prefer visual or auditory learning materials through surveys and behavioral logs. Furthermore, it can collect whether students prefer group learning or individual learning. This allows the learning support system to provide an optimal learning plan tailored to each student's learning style. For instance, students who prefer visual learning materials can be provided with materials that heavily utilize diagrams and graphs, while students who prefer auditory learning materials can be provided with materials that include many audio explanations.

[0059] The tracking unit not only develops long-term learning plans and continuously tracks students' learning progress, but can also adjust the pace of learning. For example, if a student's learning progress is behind, the tracking unit can revise the learning plan and slow down the pace. Conversely, if a student's learning progress is ahead, the learning plan can be accelerated. This allows students to learn at their own pace and continue learning without undue pressure. For instance, the tracking unit can analyze students' learning logs and adjust the learning plan according to their progress.

[0060] The supplementary component can not only generate learning materials to fill in gaps in understanding, but also include features to encourage review. For example, the supplementary component can generate materials for students to periodically review what they have learned in the past. Furthermore, the supplementary component can send reminders for students to review areas where they did not fully understand. This allows students to regularly review material they tend to forget, thereby improving their understanding. For instance, the supplementary component can identify areas that need review based on test results and generate learning materials that include that content.

[0061] The generation unit can not only generate interactive learning materials including audio and images using multimodal data, but can also generate materials that incorporate game elements. For example, the generation unit can make learning more engaging and enjoyable by presenting learning content in quiz or puzzle formats. Furthermore, the generation unit can increase student motivation by providing rewards and badges according to learning progress. This allows students to learn while having fun, improving the effectiveness of their learning. For example, the generation unit can generate interactive learning materials that incorporate game elements using audio and image data.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The analysis unit analyzes students' learning progress and comprehension in real time. For example, the analysis unit can collect and analyze learning logs and behavioral logs in real time. Furthermore, the analysis unit can also use AI to analyze students' learning progress and comprehension. Step 2: The generation unit generates interactive learning materials based on the data analyzed by the analysis unit. The generation unit can generate learning materials using multimodal data, such as audio and images. Furthermore, the generation unit can also generate interactive learning materials using AI. Step 3: The provisioning unit provides the optimal learning plan and instruction based on the materials generated by the generation unit. For example, the provisioning unit can analyze students' learning logs and behavioral logs to provide the optimal learning plan and instruction. Furthermore, the provisioning unit can also use AI to provide the optimal learning plan and instruction.

[0064] (Example of form 2) The learning support system according to an embodiment of the present invention is a system in which an AI agent tracks students' learning progress and comprehension based on real-time analysis, generates interactive learning materials including audio and images using multimodal data, and provides learning plans and guidance tailored to each student's understanding. The learning support system analyzes students' learning progress and comprehension in real time and generates interactive learning materials using multimodal data. The generated materials are customized according to the student's level of comprehension. Furthermore, the learning support system analyzes students' learning logs and behavior logs to provide optimal learning plans and guidance. This reduces the burden on teachers while improving the quality of each student's learning. For example, the learning support system analyzes students' comprehension and generates materials to supplement areas where understanding is lacking. It also formulates long-term learning plans and continuously tracks students' learning progress. This allows students to learn at their own pace and maintain their motivation to learn. Furthermore, the learning support system collects students' interests and goals using conversational AI and provides individualized learning plans. This enables learning based on students' interests and improves the effectiveness of learning. For example, a learning support system analyzes students' interests and generates learning materials based on those interests. This allows students to engage in learning with interest. As described above, by utilizing a learning support system, it is possible to track students' learning progress and comprehension in real time and provide optimal learning plans and instruction. This reduces the burden on teachers while improving the quality of each student's learning. In this way, a learning support system can track students' learning progress and comprehension in real time and provide optimal learning plans and instruction.

[0065] The learning support system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes students' learning progress and comprehension in real time. The analysis unit can, for example, collect and analyze learning logs and behavior logs in real time. The analysis unit can also use AI to analyze students' learning progress and comprehension. The generation unit generates interactive learning materials based on the data analyzed by the analysis unit. The generation unit can, for example, generate learning materials using multimodal data including audio and images. The generation unit can also use AI to generate interactive learning materials. The provision unit provides an optimal learning plan and instruction based on the learning materials generated by the generation unit. The provision unit can, for example, analyze students' learning logs and behavior logs to provide an optimal learning plan and instruction. The provision unit can also use AI to provide an optimal learning plan and instruction. As a result, the learning support system according to this embodiment can analyze students' learning progress and comprehension in real time and provide an optimal learning plan and instruction.

[0066] The analysis unit analyzes students' learning progress and comprehension in real time. Specifically, the analysis unit collects learning logs and behavioral logs generated when students study, and can analyze this data in real time. Learning logs include detailed information such as which materials students studied, for how long, and how they answered each question. Behavioral logs include information about students' actions and reactions during learning, such as where they got stuck and what questions they asked. The analysis unit uses AI to analyze this data and evaluate students' learning progress and comprehension. The AI ​​uses machine learning algorithms to detect changes in students' learning patterns and comprehension, and generates foundational data to provide optimal learning support for each individual student. For example, the AI ​​can analyze how long it took a student to understand a particular concept and what kinds of errors they are repeating, and identify areas where their comprehension is weak. Furthermore, by comparing current learning data with past learning data, the analysis unit can monitor students' learning progress over the long term and evaluate the effectiveness of their learning. This allows the analysis unit to gain a detailed understanding of students' learning situations and provide important information for providing learning support tailored to each individual student.

[0067] The generation unit generates interactive learning materials based on data analyzed by the analysis unit. Specifically, the generation unit can automatically generate optimal learning materials according to students' learning progress and level of understanding. The generation unit utilizes multimodal data, including audio and images, to create visually and aurally effective learning materials. For example, for students with low levels of understanding, the generation unit generates materials that include visually easy-to-understand diagrams and animations to help deepen their understanding. The generation unit can also generate interactive learning materials using AI. The AI ​​analyzes students' learning history and behavior logs to suggest the most suitable learning materials for each individual student. For example, the AI ​​can identify problems that students have previously answered incorrectly or concepts that they do not fully understand, and generate customized practice problems and explanatory videos based on that. Furthermore, the generation unit can update the generated learning materials in real time and respond flexibly to students' learning situations. This allows the generation unit to provide effective learning materials tailored to students' learning needs and improve learning efficiency.

[0068] The provisioning department provides optimal learning plans and instruction based on the materials generated by the generation department. Specifically, the provisioning department analyzes students' learning logs and behavioral logs to create optimal learning plans for each individual student. The provisioning department uses AI to propose optimal learning plans based on students' learning history and level of understanding. For example, the AI ​​can monitor students' learning progress in real time and adjust the learning plan according to their progress. Furthermore, the provisioning department provides specific instruction to students using the generated materials. For example, the provisioning department provides additional explanations and practice problems for concepts that students are struggling to understand, supporting them in deepening their comprehension. The provisioning department can also report on learning progress according to the student's learning status and provide feedback to parents and teachers. In this way, the provisioning department can effectively support students' learning and maximize learning outcomes. Moreover, the provisioning department can use AI to continuously improve learning plans and instructional content, providing optimal support tailored to students' learning needs. In this way, the provisioning department can comprehensively support students' learning and improve the efficiency and effectiveness of learning.

[0069] The data collection unit can collect students' interests and goals using conversational AI. For example, the data collection unit can collect students' interests using surveys or behavioral logs. The data collection unit can also collect students' interests and goals using AI. This allows for the collection of students' interests and goals and the provision of individualized learning plans. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use conversational AI to interact with students and analyze the collected data in order to collect their interests and goals.

[0070] The tracking unit can formulate long-term learning plans and continuously track students' learning progress. The tracking unit can track students' learning progress over periods such as six months or a year. The tracking unit can also use AI to continuously track students' learning progress. This allows for continuous tracking of students' learning progress and the provision of long-term learning plans. Some or all of the above-described processes in the tracking unit may be performed using AI or not. For example, the tracking unit can collect students' learning logs and behavioral logs and use AI to formulate long-term learning plans.

[0071] The supplementary unit can generate learning materials to fill in areas of misunderstanding. For example, the supplementary unit can determine areas of misunderstanding based on test results or quiz accuracy rates and generate learning materials to fill in those areas. The supplementary unit can also use AI to generate learning materials to fill in areas of misunderstanding. This allows for the generation of learning materials to fill in areas of misunderstanding and improve students' comprehension. Some or all of the above-described processes in the supplementary unit may be performed using AI or not. For example, the supplementary unit can input test results into AI, identify areas of misunderstanding, and generate learning materials to fill in those areas.

[0072] The generation unit can generate interactive teaching materials, including audio and images, by utilizing multimodal data. For example, the generation unit can generate interactive teaching materials using audio data or image data. The generation unit can also generate interactive teaching materials utilizing multimodal data using AI. This allows for the generation of more effective interactive teaching materials by utilizing multimodal data. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input audio data or image data into AI and generate interactive teaching materials.

[0073] The service provider can analyze students' learning logs and behavioral logs to provide optimal learning plans and instruction. For example, the service provider can analyze learning logs based on learning time and learning content to provide optimal learning plans. The service provider can also use AI to analyze students' learning logs and behavioral logs to provide optimal learning plans and instruction. This allows the service provider to provide optimal learning plans and instruction by analyzing students' learning logs and behavioral logs. 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 learning logs and behavioral logs into AI to provide optimal learning plans and instruction.

[0074] The analysis unit can estimate students' emotions and adjust the analysis method for learning progress and comprehension based on the estimated emotions. For example, if a student is stressed, the analysis unit can simplify the analysis method to reduce the burden. If a student is relaxed, the analysis unit can perform a detailed analysis to evaluate their level of understanding. If a student is focused, the analysis unit can provide detailed feedback in real time. This allows for more appropriate analysis by adjusting the analysis method based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input student facial expression data into AI, estimate emotions, and adjust the analysis method based on those emotions.

[0075] The analysis unit can analyze a student's past learning history and select the optimal analysis algorithm. For example, the analysis unit can focus its analysis on areas where the student has struggled in the past. The analysis unit can also select an appropriate analysis algorithm based on the student's past performance. The analysis unit can also analyze the student's learning patterns and propose the optimal analysis method. This allows the optimal analysis algorithm to be selected by analyzing the past learning history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the student's past learning history into AI and select the optimal analysis algorithm.

[0076] The analysis unit can filter data based on the student's current learning environment and circumstances when analyzing learning progress and comprehension. For example, if a student is studying at home, the analysis unit can assume a quiet environment. If a student is studying at school, the analysis unit can also take surrounding noise into account. If a student is studying while on the go, the analysis unit can evaluate comprehension in a short amount of time. This allows for more accurate analysis by filtering data based on the current learning environment and circumstances. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the student's current learning environment data into AI and perform filtering.

[0077] The analysis unit can estimate students' emotions and prioritize analysis results based on the estimated emotions. For example, if a student is feeling anxious, the analysis unit can prioritize analyzing areas where the student has difficulty understanding. If a student is confident, the analysis unit can prioritize analyzing areas where they are progressing quickly. If a student is interested in a particular area, the analysis unit can prioritize analyzing that area. This allows for more effective analysis by prioritizing analysis results based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input student facial expression data into an AI, estimate emotions, and prioritize analysis results based on those emotions.

[0078] The analysis unit can prioritize the analysis of highly relevant data by considering the students' geographical location when analyzing learning progress and comprehension. For example, if a student lives in a specific region, the analysis unit can prioritize the analysis of data related to that region. If a student is overseas, the analysis unit can also analyze data based on the local educational curriculum. If a student lives in an urban area, the analysis unit can also prioritize the analysis of data related to that urban area. In this way, by considering geographical location, the analysis unit can prioritize the analysis of highly relevant data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the students' geographical location information into AI and prioritize the analysis of highly relevant data.

[0079] The analysis unit can analyze students' social media activity and analyze related data when analyzing learning progress and comprehension. For example, the analysis unit can analyze learning content that students share on social media. The analysis unit can also analyze information about educational accounts that students follow on social media. The analysis unit can also analyze the activities of learning groups that students participate in on social media. In this way, by analyzing social media activity, related data can be analyzed. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input students' social media activity data into AI and analyze the related data.

[0080] The generation unit can estimate students' emotions and adjust the method of generating learning materials based on the estimated emotions. For example, if a student is relaxed, the generation unit can generate learning materials that proceed at a relaxed pace. If a student is in a hurry, the generation unit can also generate concise learning materials that can be learned in a short amount of time. If a student is excited, the generation unit can also generate learning materials with visually stimulating effects. In this way, more effective learning materials can be generated by adjusting the method of generating learning materials based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student facial expression data into an AI, estimate emotions, and adjust the method of generating learning materials based on those emotions.

[0081] The generation unit can adjust the level of detail in the learning materials based on the importance of the learning content during material generation. For example, the generation unit can generate materials that include detailed explanations for important learning content. For less important learning content, the generation unit can also generate materials that include concise explanations. The generation unit can also adjust the length and level of detail of the materials according to the importance of the learning content. This allows for the generation of more effective learning materials by adjusting the level of detail based on the importance of the learning content. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input learning content importance data into AI and adjust the level of detail of the materials.

[0082] The generation unit can apply different generation algorithms depending on the category of learning content when generating learning materials. For example, for science learning content, the generation unit can generate materials that include experimental videos. For mathematics learning content, the generation unit can also generate materials that include step-by-step explanations. For history learning content, the generation unit can also generate materials that include timelines and maps. By applying different generation algorithms depending on the category of learning content, more effective learning materials can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input learning content category data into AI and apply different generation algorithms.

[0083] The generation unit can estimate a student's emotions and adjust the length of the learning material based on the estimated emotions. For example, if a student is tired, the generation unit can generate short, concise learning materials. If a student is relaxed, the generation unit can also generate longer materials with detailed explanations. If a student is excited, the generation unit can also generate materials with visually stimulating effects. By adjusting the length of the learning material based on the student's emotions, more effective learning materials can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student facial expression data into an AI, estimate emotions, and adjust the length of the learning material based on those emotions.

[0084] The generation unit can determine the priority of learning materials based on the submission deadlines for the learning content when generating the materials. For example, the generation unit can prioritize generating materials for learning content with an approaching submission deadline. It can also postpone generating materials for learning content with a distant submission deadline. The generation unit can also adjust the order in which materials are generated according to the submission deadlines. This allows for the generation of more effective materials by determining the priority of learning materials based on the submission deadlines. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input learning content submission deadline data into AI to determine the priority of learning materials.

[0085] The generation unit can adjust the order of learning materials based on the relevance of the learning content during material generation. For example, the generation unit can generate materials so that highly relevant learning content can be learned consecutively. The generation unit can also generate materials so that less relevant learning content can be learned separately. The generation unit can also adjust the order of materials according to the relevance of the learning content. By adjusting the order of materials based on the relevance of the learning content, more effective materials can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input data on the relevance of learning content into AI and adjust the order of the materials.

[0086] The delivery unit can estimate a student's emotions and adjust the learning plan and instruction delivery method based on the estimated emotions. For example, if a student is stressed, the delivery unit can provide simple tasks to reduce the burden. If a student is relaxed, the delivery unit can provide detailed instruction to promote deeper understanding. If a student is focused, the delivery unit can provide detailed feedback in real time. This allows for more effective learning plans and instruction by adjusting the delivery method 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input student facial expression data into AI, estimate emotions, and adjust the delivery method based on those emotions.

[0087] The service provider can select the optimal delivery method by referring to the student's past learning history when providing learning plans and instruction. For example, the service provider can focus instruction on areas where the student has previously struggled. The service provider can also select an appropriate instruction method based on the student's past performance. The service provider can also analyze the student's learning patterns and propose the optimal instruction method. This allows the service provider to select the optimal delivery method 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 or not. For example, the service provider can input the student's past learning history into AI and select the optimal delivery method.

[0088] The delivery unit can customize the delivery method based on the student's current learning environment when providing learning plans and instruction. For example, if the student is studying at home, the delivery unit can provide online instruction. If the student is studying at school, the delivery unit can provide in-person instruction. If the student is studying while on the go, the delivery unit can provide instruction using mobile devices. This allows for more effective instruction by customizing the delivery method based on the current learning environment. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input data on the student's current learning environment into AI to customize the delivery method.

[0089] The service provider can estimate a student's emotions and determine learning plans and instructional priorities based on those estimated emotions. For example, if a student is feeling anxious, the service provider can prioritize instruction on areas where the student is struggling. If a student is confident, the service provider can prioritize instruction on areas where the student is progressing quickly. If a student is interested in a particular area, the service provider can prioritize instruction on that area. This allows for more effective instruction by prioritizing based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input student facial expression data into an AI, estimate emotions, and determine priorities based on those emotions.

[0090] The service provider can select the optimal delivery method when providing learning plans and instruction, taking into account the student's geographical location. For example, if a student lives in a specific region, the service provider can prioritize providing data relevant to that region. If a student is overseas, the service provider can provide instruction based on the local educational curriculum. If a student lives in an urban area, the service provider can prioritize providing data for urban areas. In this way, the service provider can select the optimal delivery method by considering geographical location. 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 the student's geographical location information into AI and select the optimal delivery method.

[0091] The service provider can analyze students' social media activity and suggest delivery methods when providing learning plans and instruction. For example, the service provider can provide instruction based on learning content shared by students on social media. The service provider can also provide instruction based on information from educational accounts that students follow on social media. The service provider can also provide instruction based on the activities of learning groups that students participate in on social media. In this way, by analyzing social media activity, the service provider can suggest the most suitable delivery method. 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 students' social media activity data into AI and suggest delivery methods.

[0092] The data collection unit can estimate students' emotions and adjust its methods for collecting interests and goals based on those estimated emotions. For example, if a student is relaxed, the data collection unit can collect interests through detailed questions. If a student is in a hurry, the data collection unit can collect interests through concise questions. If a student is excited, the data collection unit can collect interests in a visually stimulating way. This allows for more effective collection of interests and goals by adjusting the collection method based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input student facial expression data into an AI to estimate emotions and adjust the collection method based on those emotions.

[0093] The data collection unit can select the optimal collection method by referring to the student's past behavioral history when collecting interests and goals. For example, the data collection unit can set questions based on areas the student has shown interest in in the past. The data collection unit can also analyze the student's past behavioral history and select an appropriate collection method. The data collection unit can also suggest the optimal method for collecting interests based on the student's learning patterns. This allows the optimal collection method to be selected by referring to past behavioral history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the student's past behavioral history into AI and select the optimal collection method.

[0094] The data collection unit can estimate students' emotions and prioritize the data to collect based on those estimated emotions. For example, if a student is feeling anxious, the data collection unit can prioritize collecting data that provides a sense of security. If a student is interested in something, the data collection unit can prioritize collecting data related to that interest. If a student is focused, the data collection unit can prioritize collecting data related to learning. This allows for more effective data collection by prioritizing data based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input student facial expression data into an AI, estimate emotions, and prioritize data based on those emotions.

[0095] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location when collecting information on their interests and goals. For example, if a student lives in a specific region, the data collection unit can prioritize the collection of data related to that region. If a student is overseas, the data collection unit can also collect data based on the local educational curriculum. If a student lives in an urban area, the data collection unit can also prioritize the collection of data related to that urban area. This allows for the priority collection of highly relevant data by considering geographical location. 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 student's geographical location information into AI and prioritize the collection of highly relevant data.

[0096] The tracking unit can estimate a student's emotions and adjust the tracking method of learning progress based on the estimated emotions. For example, if a student is stressed, the tracking unit can reduce the frequency of tracking to alleviate the burden. If a student is relaxed, the tracking unit can perform detailed tracking to assess their level of understanding. If a student is focused, the tracking unit can provide detailed feedback in real time. This allows for more effective tracking of learning progress by adjusting the tracking method 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 tracking unit may be performed using AI or not. For example, the tracking unit can input student facial expression data into an AI, estimate emotions, and adjust the tracking method based on those emotions.

[0097] The tracking unit can select the optimal tracking method by referring to the student's past learning history when tracking learning progress. For example, the tracking unit can focus on tracking areas where the student has struggled in the past. The tracking unit can also select an appropriate tracking method based on the student's past performance. The tracking unit can also analyze the student's learning patterns and suggest the optimal tracking method. This allows the optimal tracking method to be selected by referring to the past learning history. Some or all of the above processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input the student's past learning history into AI and select the optimal tracking method.

[0098] The tracking unit can estimate a student's emotions and determine tracking priorities based on those estimated emotions. For example, if a student is feeling anxious, the tracking unit can prioritize tracking areas where the student is struggling to understand. If a student is confident, the tracking unit can prioritize tracking areas where they are progressing quickly. If a student is interested in a particular area, the tracking unit can prioritize tracking that area. This allows for more effective tracking by determining tracking priorities based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input student facial expression data into an AI, estimate emotions, and determine tracking priorities based on those emotions.

[0099] The tracking unit can select the optimal tracking method when tracking learning progress, taking into account the student's geographical location. For example, if a student lives in a specific region, the tracking unit can prioritize tracking data related to that region. If a student is overseas, the tracking unit can also track based on the local educational curriculum. If a student lives in an urban area, the tracking unit can also prioritize tracking data from urban areas. This allows the system to select the optimal tracking method by considering geographical location. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the student's geographical location information into AI to select the optimal tracking method.

[0100] The supplementary unit can estimate a student's emotions and adjust the method of generating supplementary materials to fill in areas of misunderstanding based on the estimated emotions. For example, if a student is stressed, the supplementary unit can provide simple tasks to reduce their burden. If a student is relaxed, the supplementary unit can provide detailed instruction to promote deeper understanding. If a student is focused, the supplementary unit can also provide detailed feedback in real time. This allows for the generation of more effective supplementary materials by adjusting the generation method 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 processing in the supplementary unit may be performed using AI or not. For example, the supplementary unit can input student facial expression data into an AI, estimate emotions, and adjust the generation method based on those emotions.

[0101] The supplementary unit can select the optimal generation method by referring to the student's past learning history when generating materials to fill in areas of misunderstanding. For example, the supplementary unit can generate materials that focus on areas where the student has struggled in the past. The supplementary unit can also generate appropriate supplementary materials based on the student's past performance. The supplementary unit can also analyze the student's learning patterns and suggest the optimal supplementary materials. In this way, the optimal supplementary materials can be generated by referring to the past learning history. Some or all of the above processes in the supplementary unit may be performed using AI or not. For example, the supplementary unit can input the student's past learning history into AI and select the optimal generation method.

[0102] The supplementary unit can estimate students' emotions and prioritize supplementary materials based on those estimated emotions. For example, if a student is feeling anxious, the supplementary unit can prioritize supplementing areas where the student has difficulty understanding. If a student is confident, the supplementary unit can prioritize supplementing areas where the student is progressing quickly. If a student is interested in a particular area, the supplementary unit can prioritize supplementing related fields. This allows for the generation of more effective supplementary materials by prioritizing based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the supplementary unit may be performed using AI or not. For example, the supplementary unit can input student facial expression data into an AI, estimate emotions, and determine priorities based on those emotions.

[0103] The supplementary unit can select the optimal generation method when generating supplementary materials to fill in areas of misunderstanding, taking into account the student's geographical location. For example, if a student lives in a specific region, the supplementary unit can prioritize supplementing with data related to that region. If a student is overseas, the supplementary unit can also supplement based on the local educational curriculum. If a student lives in an urban area, the supplementary unit can also prioritize supplementing with data for urban areas. In this way, by considering geographical location, the optimal supplementary materials can be generated. Some or all of the above processing in the supplementary unit may be performed using AI or not. For example, the supplementary unit can input the student's geographical location information into AI and select the optimal generation method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The analysis unit can not only analyze students' learning progress and comprehension in real time, but also evaluate their motivation to learn. For example, the analysis unit can analyze students' learning logs and behavioral logs to detect changes in their motivation to learn. Furthermore, the analysis unit can analyze students' learning time and learning content selection trends to evaluate the level of their motivation. This allows the learning support system to provide appropriate guidance and feedback tailored to each student's motivation. For example, students with low motivation can be encouraged with messages of support and set short-term goals to improve their motivation to learn.

[0106] The data collection unit can not only gather students' interests and goals using conversational AI, but also their learning style preferences. For example, it can collect whether students prefer visual or auditory learning materials through surveys and behavioral logs. Furthermore, it can collect whether students prefer group learning or individual learning. This allows the learning support system to provide an optimal learning plan tailored to each student's learning style. For instance, students who prefer visual learning materials can be provided with materials that heavily utilize diagrams and graphs, while students who prefer auditory learning materials can be provided with materials that include many audio explanations.

[0107] The tracking unit not only develops long-term learning plans and continuously tracks students' learning progress, but can also adjust the pace of learning. For example, if a student's learning progress is behind, the tracking unit can revise the learning plan and slow down the pace. Conversely, if a student's learning progress is ahead, the learning plan can be accelerated. This allows students to learn at their own pace and continue learning without undue pressure. For instance, the tracking unit can analyze students' learning logs and adjust the learning plan according to their progress.

[0108] The supplementary component can not only generate learning materials to fill in gaps in understanding, but also include features to encourage review. For example, the supplementary component can generate materials for students to periodically review what they have learned in the past. Furthermore, the supplementary component can send reminders for students to review areas where they did not fully understand. This allows students to regularly review material they tend to forget, thereby improving their understanding. For instance, the supplementary component can identify areas that need review based on test results and generate learning materials that include that content.

[0109] The generation unit can not only generate interactive learning materials including audio and images using multimodal data, but can also generate materials that incorporate game elements. For example, the generation unit can make learning more engaging and enjoyable by presenting learning content in quiz or puzzle formats. Furthermore, the generation unit can increase student motivation by providing rewards and badges according to learning progress. This allows students to learn while having fun, improving the effectiveness of their learning. For example, the generation unit can generate interactive learning materials that incorporate game elements using audio and image data.

[0110] The analysis unit can estimate students' emotions and adjust the analysis method for learning progress and comprehension based on the estimated emotions. For example, if a student is stressed, the analysis unit can simplify the analysis method to reduce the burden. If a student is relaxed, the analysis unit can perform a detailed analysis to evaluate their level of understanding. If a student is focused, the analysis unit can provide detailed feedback in real time. This allows for more appropriate analysis by adjusting the analysis method based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input student facial expression data into AI, estimate emotions, and adjust the analysis method based on those emotions.

[0111] The generation unit can estimate students' emotions and adjust the method of generating learning materials based on the estimated emotions. For example, if a student is relaxed, the generation unit can generate learning materials that proceed at a relaxed pace. If a student is in a hurry, the generation unit can also generate concise learning materials that can be learned in a short amount of time. If a student is excited, the generation unit can also generate learning materials with visually stimulating effects. In this way, more effective learning materials can be generated by adjusting the method of generating learning materials based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student facial expression data into an AI, estimate emotions, and adjust the method of generating learning materials based on those emotions.

[0112] The delivery unit can estimate a student's emotions and adjust the learning plan and instruction delivery method based on the estimated emotions. For example, if a student is stressed, the delivery unit can provide simple tasks to reduce the burden. If a student is relaxed, the delivery unit can provide detailed instruction to promote deeper understanding. If a student is focused, the delivery unit can provide detailed feedback in real time. This allows for more effective learning plans and instruction by adjusting the delivery method 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input student facial expression data into AI, estimate emotions, and adjust the delivery method based on those emotions.

[0113] The data collection unit can estimate students' emotions and adjust its methods for collecting interests and goals based on those estimated emotions. For example, if a student is relaxed, the data collection unit can collect interests through detailed questions. If a student is in a hurry, the data collection unit can collect interests through concise questions. If a student is excited, the data collection unit can collect interests in a visually stimulating way. This allows for more effective collection of interests and goals by adjusting the collection method based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input student facial expression data into an AI to estimate emotions and adjust the collection method based on those emotions.

[0114] The tracking unit can estimate a student's emotions and adjust the tracking method of learning progress based on the estimated emotions. For example, if a student is stressed, the tracking unit can reduce the frequency of tracking to alleviate the burden. If a student is relaxed, the tracking unit can perform detailed tracking to assess their level of understanding. If a student is focused, the tracking unit can provide detailed feedback in real time. This allows for more effective tracking of learning progress by adjusting the tracking method 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 tracking unit may be performed using AI or not. For example, the tracking unit can input student facial expression data into an AI, estimate emotions, and adjust the tracking method based on those emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The analysis unit analyzes students' learning progress and comprehension in real time. For example, the analysis unit can collect and analyze learning logs and behavioral logs in real time. Furthermore, the analysis unit can also use AI to analyze students' learning progress and comprehension. Step 2: The generation unit generates interactive learning materials based on the data analyzed by the analysis unit. The generation unit can generate learning materials using multimodal data, such as audio and images. Furthermore, the generation unit can also generate interactive learning materials using AI. Step 3: The provisioning unit provides the optimal learning plan and instruction based on the materials generated by the generation unit. For example, the provisioning unit can analyze students' learning logs and behavioral logs to provide the optimal learning plan and instruction. Furthermore, the provisioning unit can also use AI to provide the optimal learning plan and instruction.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, collection unit, tracking unit, and complementation unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The tracking unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The complementation unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, collection unit, tracking unit, and complementation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The tracking unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The complementation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, collection unit, tracking unit, and complementation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The tracking unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The complementation unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In 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.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 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.

[0169] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, collection unit, tracking unit, and complementation unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The tracking unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The complementation unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) The analysis unit analyzes students' learning progress and understanding in real time, A generation unit that generates interactive teaching materials based on the data analyzed by the analysis unit, The system includes a providing unit that provides an optimal learning plan and instruction based on the teaching materials generated by the generation unit. A system characterized by the following features. (Note 2) It features a data collection unit that uses conversational AI to gather information about students' interests, concerns, and goals. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a tracking unit that develops long-term learning plans and continuously tracks students' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a supplementary section that generates learning materials to fill in any gaps in understanding. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Use multimodal data to generate interactive learning materials that include audio and images. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We analyze students' learning logs and behavioral logs to provide optimal learning plans and instruction. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate students' emotions and adjust the analysis methods for learning progress and comprehension based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Analyze students' past learning history and select the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing learning progress and comprehension, filtering is performed based on the student's current learning environment and situation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates students' emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing learning progress and comprehension levels, the system prioritizes analyzing highly relevant data by considering students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing learning progress and comprehension levels, we analyze students' social media activity and related data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates students' emotions and adjusts the method of generating teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating learning materials, adjust the level of detail based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating learning materials, different generation algorithms are applied depending on the category of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates students' emotions and adjusts the length of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating learning materials, prioritize the materials based on the submission deadlines for the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating learning materials, the order of the materials is adjusted based on the relevance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates students' emotions and adjusts learning plans and teaching methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing learning plans and instruction, the optimal delivery method is selected by referring to the student's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing learning plans and instruction, customize the delivery method based on the student's current learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates students' emotions and determines learning plans and instructional priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing learning plans and instruction, the most suitable delivery method will be selected, taking into account the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing learning plans and instruction, we analyze students' social media activity and propose methods for sharing it. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is The system estimates students' emotions and adjusts the methods for gathering information on their interests and goals based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned collection unit is When collecting information on students' interests, concerns, and goals, the most suitable collection method is selected by referring to students' past behavioral history. The system described in Appendix 2, characterized by the features described herein. (Note 27) 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 2, characterized by the features described herein. (Note 28) The aforementioned collection unit is When collecting information on students' interests and goals, the system prioritizes collecting highly relevant data, taking into account students' geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned tracking unit is We estimate students' emotions and adjust the learning progress tracking method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned tracking unit is When tracking learning progress, the system selects the optimal tracking method by referring to the student's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned tracking unit is The system estimates students' emotions and determines tracking priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned tracking unit is When tracking learning progress, the optimal tracking method is selected by considering the students' geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned supplementary unit is, We estimate students' emotions and adjust the method of generating teaching materials to supplement areas where they lack understanding based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned supplementary unit is, When generating learning materials to address areas of misunderstanding, the system selects the optimal generation method by referring to the student's past learning history. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned supplementary unit is, The system estimates students' emotions and prioritizes supplementary materials based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned supplementary unit is, When generating teaching materials to address areas of misunderstanding, the optimal generation method is selected by considering the students' geographical location information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0189] 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 analysis unit analyzes students' learning progress and understanding in real time, A generation unit that generates interactive teaching materials based on the data analyzed by the analysis unit, The system includes a providing unit that provides an optimal learning plan and instruction based on the teaching materials generated by the generation unit. A system characterized by the following features.

2. It features a data collection unit that uses conversational AI to gather information about students' interests, concerns, and goals. The system according to feature 1.

3. It includes a tracking unit that develops long-term learning plans and continuously tracks students' learning progress. The system according to feature 1.

4. It includes a supplementary section that generates learning materials to fill in any gaps in understanding. The system according to feature 1.

5. The generating unit is Use multimodal data to generate interactive learning materials that include audio and images. The system according to feature 1.

6. The aforementioned supply unit is, We analyze students' learning logs and behavioral logs to provide optimal learning plans and instruction. The system according to feature 1.

7. The aforementioned analysis unit, The system estimates students' emotions and adjusts the analysis methods for learning progress and comprehension based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, Analyze students' past learning history and select the optimal analysis algorithm. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing learning progress and comprehension, filtering is performed based on the student's current learning environment and situation. The system according to feature 1.

Images