system

Abstract

The system according to this embodiment aims to provide an optimal learning plan based on students' interests and learning styles. [Solution] The system according to the embodiment comprises a generation unit, a provision unit, and an analysis unit. The generation unit creates an optimal learning curriculum based on the student's interests and goals. The provision unit provides learning content based on the learning curriculum generated by the generation unit. The analysis unit analyzes the student's learning style and provides the optimal learning materials.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide an individually optimized learning plan according to the interests and learning styles of each student.

[0005] The system according to the embodiment aims to provide an optimal learning plan based on the interests and learning styles of students.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a provision unit, and an analysis unit. The generation unit creates an optimal learning curriculum based on the interests and goals of students. The provision unit provides learning content based on the learning curriculum generated by the generation unit. The analysis unit analyzes the learning styles of students and provides optimal teaching materials. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal learning plan based on students' interests and learning styles. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 that provides an individually optimized learning plan tailored to each student's interests, learning style, and level of understanding. This learning support system uses a generative AI to create an optimal learning curriculum based on the student's interests and goals. Next, the generative AI analyzes the student's learning style and provides the most suitable learning materials according to their learning style, such as visual, auditory, and kinesthetic. Furthermore, the generative AI analyzes learning data in real time and adjusts the next learning content according to the student's level of understanding. The generative AI also supports goal setting and motivation maintenance to encourage student autonomy. Finally, the generative AI shares the student's learning status with the teacher and proposes necessary support. For example, the learning support system generates an optimal learning curriculum based on the student's interests and goals. For example, the learning support system analyzes the student's learning style and provides the most suitable learning materials according to their learning style, such as visual, auditory, and kinesthetic. For example, the learning support system analyzes learning data in real time and adjusts the next learning content according to the student's level of understanding. For example, the learning support system supports goal setting and motivation maintenance to encourage student autonomy. For example, the learning support system shares the student's learning status with the teacher and proposes necessary support. This allows the learning support system to guide students' learning in line with their interests and goals, thereby increasing their self-efficacy. It also reduces the burden on teachers and enables more effective support.

[0029] The learning support system according to this embodiment comprises a generation unit, a provision unit, and an analysis unit. The generation unit creates an optimal learning curriculum based on the student's interests and goals. The generation unit generates an optimal learning curriculum based on the student's interests and goals, for example, using a generation AI. The generation unit can identify the student's interests and goals from, for example, questionnaires, interviews, past learning history, etc., and create a learning curriculum based on them. The generation unit receives, for example, a prompt from the generation AI to generate a learning curriculum based on the student's interests and goals, and generates an optimal learning curriculum. The provision unit provides learning content based on the learning curriculum generated by the generation unit. The provision unit provides learning content based on the generated learning curriculum, for example. The provision unit can also provide learning content using, for example, AI in order to provide learning content based on the generated learning curriculum. The provision unit can also provide learning content without using AI in order to provide learning content based on the generated learning curriculum, for example. The analysis unit analyzes the student's learning style and provides optimal learning materials. The analysis unit analyzes the student's learning style using, for example, a generation AI and provides optimal learning materials. The analysis unit can, for example, classify students' learning styles into visual, auditory, experiential, etc., and provide learning materials accordingly. The analysis unit can, for example, receive prompts from the generating AI to analyze students' learning styles and provide optimal learning materials. As a result, the learning support system according to this embodiment can provide an optimal learning curriculum based on students' interests and goals, and provide learning materials that match their learning style, thereby enabling individually optimized learning.

[0030] The generation unit creates an optimal learning curriculum based on the student's interests and goals. For example, it uses a generation AI to generate the optimal learning curriculum based on the student's interests and goals. Specifically, the generation unit identifies the student's interests and goals from questionnaires, interviews, and past learning history, and creates a learning curriculum based on that. Questionnaires ask in detail about subjects the student is interested in, future goals, and learning progress. Interviews allow teachers and counselors to directly interact with students and gain a deeper understanding. Past learning history includes what the student has learned so far, their grades, and their learning pace. After collecting this information, the generation AI analyzes this data and generates an optimal learning curriculum for the student. The generation AI uses natural language processing technology to analyze the content of questionnaires and interviews and extract the student's interests and goals. It also uses machine learning algorithms to analyze past learning history and identify the student's learning patterns and areas of strength and weakness. This allows the generation unit to provide an optimal learning curriculum for each individual student. The generated learning curriculum is designed to stimulate the student's interest and support effective learning toward achieving their goals. For example, students interested in science can be offered a curriculum that emphasizes experiments and observations, while students interested in literature can be offered a curriculum that emphasizes reading and writing. This allows students to pursue learning that aligns with their interests and goals, thereby increasing their motivation to learn.

[0031] The delivery unit provides learning content based on the learning curriculum generated by the generation unit. Specifically, the delivery unit can also use AI to provide learning content based on the generated learning curriculum. For example, the AI ​​can monitor students' learning progress in real time and adjust the learning content as needed. The AI ​​can provide learning content in a format that is easy for students to understand and can answer questions and doubts immediately. For example, the AI ​​can detect areas that students find difficult to understand and provide additional explanations or examples. The AI ​​can also adjust the learning content to match the student's learning pace, allowing them to learn at their own pace. Furthermore, the delivery unit can also provide learning content without using AI. For example, a teacher can conduct a lesson based on the generated learning curriculum and provide direct instruction to students. The teacher can provide appropriate guidance while observing the students' reactions and level of understanding. This allows the delivery unit to provide optimal learning content for each student and support effective learning. The delivery unit can also provide learning content through an online learning platform. For example, it can provide online classes, video lectures, and interactive learning materials, allowing students to learn at their own pace. This allows the delivery unit to provide a flexible learning environment that suits the learning style and needs of students.

[0032] The analysis unit analyzes students' learning styles and provides optimal learning materials. Specifically, the analysis unit uses generative AI to analyze students' learning styles and provide optimal learning materials. The generative AI analyzes students' learning history and learning behavior data and classifies students' learning styles into visual, auditory, experiential, etc. Visual students are provided with visual materials such as diagrams, graphs, and videos, while auditory students are provided with auditory materials such as audio lectures and podcasts. Experiential students are provided with experiential materials such as experiments, simulations, and project-based learning. This allows the analysis unit to provide optimal learning materials tailored to each student's learning style. Furthermore, the analysis unit monitors students' learning progress and comprehension in real time and adjusts the materials as needed. For example, if a student is not making progress with a particular material, it will provide a different format of material. The analysis unit also collects student feedback and evaluates the effectiveness of the materials. This allows the analysis unit to always provide optimal learning materials and maximize student learning effectiveness. The analysis unit receives prompts from the generative AI to analyze students' learning styles and provides optimal learning materials. For example, the generating AI analyzes students' learning styles based on their learning history and learning behavior data, and then suggests the most suitable learning materials. This allows the analysis unit to provide each student with the most appropriate learning materials and support personalized learning.

[0033] The learning support system includes an evaluation unit that analyzes learning data in real time and adjusts the next learning content according to the student's level of understanding. The evaluation unit, for example, uses a generative AI to analyze learning data in real time and adjusts the next learning content according to the student's level of understanding. The evaluation unit, for example, collects learning data in real time, receives prompts for the generative AI to analyze that data, and adjusts the next learning content according to the level of understanding. The evaluation unit evaluates the level of understanding based on, for example, test results or quiz correct answer rates, and adjusts the next learning content accordingly. The evaluation unit, for example, uses a generative AI to analyze learning data in real time and adjusts the next learning content according to the level of understanding. As a result, the learning support system enables effective learning by adjusting the learning content according to the student's level of understanding.

[0034] The learning support system includes a support unit that assists in goal setting and motivation maintenance to foster student autonomy. The support unit, for example, uses generative AI to support goal setting and motivation maintenance to encourage student autonomy. For example, the support unit receives prompts from the generative AI to support student goal setting and performs goal setting. For example, the support unit receives prompts from the generative AI to support student motivation maintenance and performs motivation maintenance. For example, the support unit sets short-term and long-term goals and supports student learning based on them. For example, the support unit maintains student motivation through reward systems and the provision of feedback. In this way, the learning support system fosters student autonomy and promotes proactive learning.

[0035] The learning support system includes a sharing unit that shares students' learning progress with teachers and proposes necessary support. The sharing unit, for example, uses a generative AI to share students' learning progress with teachers and proposes necessary support. The sharing unit, for example, receives a prompt from the generative AI to share students' learning progress and shares the progress. The sharing unit, for example, receives a prompt from the generative AI to propose necessary support and proposes support. The sharing unit shares information with teachers, for example, students' progress, level of understanding, and assignment status. The sharing unit proposes support such as providing additional learning materials or individual tutoring. This allows the learning support system to enable teachers to understand students' learning progress and provide appropriate support.

[0036] The learning support system uses a generation unit to analyze a student's past learning history and generate an optimal learning curriculum. For example, the generation unit uses a generation AI to analyze the student's past learning history and generate an optimal learning curriculum. The generation unit receives a prompt, for example, for the generation AI to analyze the student's past learning history and analyzes the history. The generation unit receives a prompt, for example, for the generation AI to generate an optimal learning curriculum based on the analyzed history and generates the curriculum. For example, the generation unit generates a curriculum that focuses on areas the student has struggled with in the past. For example, the generation unit generates a curriculum that reinforces areas the student excels at. For example, the generation unit generates a curriculum that matches the student's progress based on their past learning history. This enables effective learning by generating an optimal curriculum based on past learning history.

[0037] The learning support system adjusts the curriculum when it is generated, taking into account the student's current learning progress. For example, the generation unit adjusts the curriculum when it is generated, taking into account the student's current learning progress, using a generation AI. For example, the generation unit receives a prompt for the generation AI to consider the student's current learning progress and takes the progress into account. For example, the generation unit receives a prompt for the generation AI to adjust the curriculum taking the progress into account and adjusts the curriculum. For example, if the student understands the current learning content, the generation unit generates a curriculum that moves to the next step. For example, if the student is struggling with the current learning content, the generation unit generates a curriculum that includes supplementary explanations. For example, the generation unit generates a curriculum that includes review depending on the student's progress. In this way, the learning support system enables effective learning by adjusting the curriculum while taking into account the student's current learning progress.

[0038] The learning support system prioritizes incorporating highly relevant content by considering the student's geographical location when generating the learning curriculum. For example, the generation unit uses a generation AI to prioritize incorporating highly relevant content by considering the student's geographical location when generating the learning curriculum. For example, the generation unit receives a prompt from the generation AI to consider the student's geographical location and takes that information into account. For example, the generation unit receives a prompt from the generation AI to incorporate highly relevant content by considering the geographical location and incorporates that content. For example, the generation unit incorporates content related to the history and culture of the area where the student lives into the curriculum. For example, the generation unit prioritizes incorporating content that aligns with the curriculum of the school the student attends. For example, the generation unit incorporates information about a region that the student is interested in into the curriculum. As a result, the learning support system enables effective learning by incorporating highly relevant content while considering geographical location.

[0039] The learning support system analyzes students' social media activity when generating the learning curriculum and incorporates relevant learning content. For example, the generation unit uses a generation AI to analyze students' social media activity and incorporate relevant learning content when generating the learning curriculum. For example, the generation unit receives a prompt from the generation AI to analyze students' social media activity and analyzes the activity. For example, the generation unit receives a prompt from the generation AI to incorporate relevant learning content based on the social media activity analyzed and incorporates the content. For example, the generation unit incorporates topics that students have shown interest in on social media into the curriculum. For example, the generation unit incorporates content related to statements made by influencers that students follow into the curriculum. For example, the generation unit incorporates topics from online communities that students participate in into the curriculum. In this way, the learning support system enables effective learning by analyzing social media activity and incorporating relevant learning content.

[0040] The learning support system adjusts the level of detail provided based on the importance of the learning content when the delivery unit provides the learning content. For example, the delivery unit may use a generative AI to adjust the level of detail based on the importance of the learning content when providing the learning content. For example, the delivery unit may receive a prompt from the generative AI to evaluate the importance of the learning content and evaluate the importance. For example, the delivery unit may receive a prompt to adjust the level of detail based on the importance evaluated by the generative AI and adjust the level of detail. For example, the delivery unit may adopt a delivery method that includes detailed explanations for important content. For example, the delivery unit may adopt a delivery method that includes concise explanations for less important content. For example, the delivery unit may provide visual aids and supplementary materials according to the importance level. In this way, the learning support system enables effective learning by adjusting the level of detail provided based on the importance of the learning content.

[0041] The learning support system applies different delivery algorithms depending on the category of the learning content when the delivery unit delivers the learning content. For example, the delivery unit applies different delivery algorithms depending on the category of the learning content when delivering learning content using a generative AI. For example, the delivery unit receives a prompt from the generative AI to classify the category of the learning content and classifies the category. For example, the delivery unit receives a prompt from the generative AI to apply a delivery algorithm according to the category classified and applies the algorithm. For example, for science content, the delivery unit adopts a delivery method that includes experimental videos. For example, for mathematics content, the delivery unit adopts a delivery method that includes step-by-step explanations. For example, for social studies content, the delivery unit adopts a storytelling-style delivery method that includes historical background. As a result, the learning support system enables effective learning by applying a delivery algorithm according to the category of the learning content.

[0042] The learning support system prioritizes the provision of learning content based on the student's submission timing when the provision unit provides learning content. The provision unit uses, for example, a generative AI to determine the priority of provision based on the student's submission timing when providing learning content. The provision unit receives, for example, a prompt from the generative AI to evaluate the student's submission timing and evaluates the submission timing. The provision unit receives, for example, a prompt to determine the priority of provision based on the submission timing evaluated by the generative AI and determines the priority. The provision unit provides, for example, content with an approaching submission deadline first. The provision unit provides, for example, content with a distant submission deadline later. The provision unit adjusts the order in which learning content is provided according to the submission deadline. As a result, the learning support system enables effective learning by determining the priority of provision based on submission timing.

[0043] The learning support system adjusts the order of content delivery based on the relevance of the learning content when the delivery unit delivers the content. The delivery unit adjusts the order of content delivery based on the relevance of the learning content when delivering the content, for example, using a generative AI. The delivery unit receives prompts from the generative AI to evaluate the relevance of the learning content and evaluates the relevance. The delivery unit receives prompts from the generative AI to adjust the order of delivery based on the relevance evaluated and adjusts the order. The delivery unit provides highly relevant content consecutively. The delivery unit provides less relevant content with intervals in between. The delivery unit adjusts the order of delivery according to the relevance of the learning content. As a result, the learning support system enables effective learning by adjusting the order of delivery based on the relevance of the learning content.

[0044] The learning support system improves the accuracy of its analysis by referring to the student's past learning data when analyzing learning styles. The analysis unit improves the accuracy of its analysis by referring to the student's past learning data when analyzing learning styles, for example, using a generative AI. The analysis unit receives a prompt, for example, for the generative AI to refer to the student's past learning data and refers to the data. The analysis unit receives a prompt, for example, for the generative AI to improve the accuracy of its analysis based on the data it has referred to and improves the accuracy. The analysis unit performs a detailed analysis of the learning style based on the student's past learning data. The analysis unit analyzes changes in the learning style from the student's past learning data. The analysis unit analyzes trends in the learning style by referring to the student's past learning data. As a result, the learning support system can provide a more appropriate learning style by improving the accuracy of its analysis by referring to past learning data.

[0045] The learning support system's analysis unit considers the student's attribute information when analyzing their learning style. For example, the analysis unit uses a generative AI to consider the student's attribute information when analyzing their learning style. For example, the analysis unit receives a prompt from the generative AI to consider the student's attribute information and takes that information into account. For example, the analysis unit receives a prompt from the generative AI to perform an analysis based on the attribute information it has considered and performs the analysis. For example, the analysis unit analyzes the learning style considering the student's age and gender. For example, the analysis unit analyzes the learning style considering the student's grade level and learning history. For example, the analysis unit analyzes the learning style considering the student's interests and concerns. As a result, the learning support system can provide a more appropriate learning style by performing an analysis that takes the student's attribute information into consideration.

[0046] The learning support system's analysis unit considers the geographical distribution of students when analyzing their learning styles. For example, the analysis unit uses a generative AI to consider the geographical distribution of students when analyzing their learning styles. For example, the analysis unit receives a prompt from the generative AI to consider the geographical distribution of students and takes it into account. For example, the analysis unit receives a prompt from the generative AI to perform an analysis based on the geographical distribution considered and performs the analysis. For example, the analysis unit analyzes learning styles considering the educational environment of the area where the student lives. For example, the analysis unit analyzes learning styles considering the educational policies of the school the student attends. For example, the analysis unit analyzes learning styles considering the culture and customs of the area where the student lives. As a result, the learning support system can provide more appropriate learning styles by considering the geographical distribution of students when performing the analysis.

[0047] The learning support system improves the accuracy of its analysis by referring to relevant literature on learning content when analyzing learning styles. The analysis unit improves the accuracy of its analysis by referring to relevant literature on learning content when analyzing learning styles, for example, using a generative AI. The analysis unit receives a prompt from the generative AI to refer to relevant literature on learning content and refers to the literature. The analysis unit receives a prompt from the generative AI to improve the accuracy of its analysis based on the literature it has referred to and improves the accuracy. The analysis unit analyzes learning styles by referring to literature related to learning content, for example. The analysis unit analyzes learning styles by referring to research results related to learning content, for example. The analysis unit analyzes learning styles by referring to data related to learning content, for example. As a result, the learning support system can provide more appropriate learning styles by improving the accuracy of its analysis by referring to relevant literature on learning content.

[0048] The learning support system allows the evaluation unit to predict the current evaluation by referring to past evaluation data during the evaluation process. The evaluation unit, for example, uses a generative AI to predict the current evaluation by referring to past evaluation data during the evaluation process. The evaluation unit, for example, receives a prompt for the generative AI to refer to past evaluation data and then refers to the data. The evaluation unit, for example, receives a prompt for the generative AI to predict the current evaluation based on the data it has referred to and then predicts the evaluation. The evaluation unit, for example, predicts the current evaluation based on the student's past evaluation data. The evaluation unit, for example, predicts evaluation trends from the student's past evaluation data. The evaluation unit, for example, refers to the student's past evaluation data to predict changes in evaluation. As a result, the learning support system can perform more appropriate evaluations by predicting the current evaluation by referring to past evaluation data.

[0049] The learning support system's evaluation unit applies different evaluation methods to each category of learning content during evaluation. For example, the evaluation unit uses a generative AI to apply different evaluation methods to each category of learning content during evaluation. For example, the evaluation unit receives a prompt from the generative AI to classify the categories of learning content and classifies them. For example, the evaluation unit receives a prompt from the generative AI to apply an evaluation method according to the classified categories and applies the method. For example, for science content, the evaluation unit applies an evaluation method that emphasizes experimental results. For example, for mathematics content, the evaluation unit applies an evaluation method that emphasizes the accuracy of the answers. For example, for social studies content, the evaluation unit applies an evaluation method that emphasizes the level of understanding. In this way, the learning support system can perform more appropriate evaluations by applying different evaluation methods to each category of learning content.

[0050] The learning support system's evaluation unit analyzes changes in evaluation based on the submission timing of learning content during evaluation. The evaluation unit, for example, uses a generative AI to analyze changes in evaluation based on the submission timing of learning content during evaluation. The evaluation unit receives prompts from the generative AI to evaluate the submission timing of learning content and evaluates the submission timing. The evaluation unit receives prompts from the generative AI to analyze changes in evaluation based on the evaluated submission timing and analyzes the changes. For example, the evaluation unit analyzes changes in evaluation in detail for content with an approaching submission deadline. For example, the evaluation unit analyzes changes in evaluation roughly for content with a distant submission deadline. The evaluation unit analyzes changes in evaluation in stages according to the submission deadline. As a result, the learning support system can perform more appropriate evaluations by analyzing changes in evaluation based on the submission timing of learning content.

[0051] The learning support system analyzes evaluations by referencing relevant market data for the learning content during the evaluation process. The evaluation unit, for example, uses a generative AI to analyze evaluations by referencing relevant market data for the learning content during the evaluation process. The evaluation unit receives prompts from the generative AI to reference relevant market data and references the market data. The evaluation unit receives prompts from the generative AI to analyze evaluations based on the market data referenced and analyzes the evaluations. The evaluation unit analyzes evaluations by referencing market data related to the learning content, for example. The evaluation unit analyzes evaluations by referencing industry trends related to the learning content, for example. The evaluation unit analyzes evaluations by referencing occupational demand related to the learning content, for example. As a result, the learning support system can perform more appropriate evaluations by analyzing evaluations by referencing relevant market data for the learning content.

[0052] The learning support system, when providing support, analyzes the student's past learning behavior and selects the optimal support method. The support unit, for example, uses generative AI to analyze the student's past learning behavior and selects the optimal support method. The support unit, for example, receives a prompt from the generative AI to analyze the student's past learning behavior and analyzes the behavior. The support unit, for example, receives a prompt from the generative AI to select the optimal support method based on the behavior analyzed and selects a method. The support unit, for example, selects the optimal support method based on the student's past learning behavior. The support unit, for example, selects a method for maintaining motivation from the student's past learning behavior. The support unit, for example, analyzes the student's past learning behavior and selects a method for setting goals. As a result, the learning support system can provide more appropriate support by analyzing past learning behavior and selecting the optimal support method.

[0053] The learning support system customizes the means of support based on the student's current living situation when the support unit provides support. For example, the support unit uses a generative AI to customize the means of support based on the student's current living situation when providing support. For example, the support unit receives a prompt from the generative AI to evaluate the student's current living situation and evaluates the living situation. For example, the support unit receives a prompt to customize the means of support based on the living situation evaluated by the generative AI and customizes the means. For example, the support unit provides the optimal means of support, taking into account the student's current living situation. For example, the support unit customizes the means of maintaining motivation according to the student's current living situation. For example, the support unit customizes the means of setting goals based on the student's current living situation. As a result, the learning support system can provide more appropriate support by customizing the means of support based on the current living situation.

[0054] The learning support system's support unit selects the optimal support method by considering the student's geographical location information when providing support. For example, the support unit uses a generative AI to select the optimal support method by considering the student's geographical location information. The support unit receives a prompt from the generative AI to evaluate the student's geographical location information and evaluates it. The support unit receives a prompt to select the optimal support method based on the geographical location information evaluated by the generative AI and selects a method. For example, the support unit selects a support method considering the educational environment of the area where the student lives. For example, the support unit selects a support method considering the educational policies of the school the student attends. For example, the support unit selects a support method considering the culture and customs of the area where the student lives. As a result, the learning support system can provide more appropriate support by selecting the optimal support method by considering geographical location information.

[0055] The learning support system analyzes students' social media activity and proposes support methods when the support unit provides assistance. For example, the support unit uses generative AI to analyze students' social media activity and propose support methods. The support unit receives prompts for the generative AI to analyze students' social media activity and performs the analysis. The support unit receives prompts for the generative AI to propose support methods based on the analyzed activity and proposes methods. For example, the support unit proposes support methods based on topics students have shown interest in on social media. For example, the support unit proposes support methods based on statements made by influencers students follow. For example, the support unit proposes support methods based on topics in online communities students participate in. This allows the learning support system to provide more appropriate support by analyzing social media activity and proposing support methods.

[0056] The learning support system, when sharing information, selects the optimal sharing method by referring to the student's past learning history. The sharing unit, for example, uses a generative AI to select the optimal sharing method by referring to the student's past learning history when sharing information. The sharing unit, for example, receives a prompt for the generative AI to refer to the student's past learning history and refers to the history. The sharing unit, for example, receives a prompt to select the optimal sharing method based on the history referred by the generative AI and selects a method. The sharing unit, for example, selects the optimal sharing method based on the student's past learning history. The sharing unit, for example, analyzes trends in sharing methods from the student's past learning history. The sharing unit, for example, customizes the sharing method by referring to the student's past learning history. As a result, the learning support system can share information more appropriately by selecting the optimal sharing method by referring to past learning history.

[0057] The learning support system adjusts the shared content when it is shared, taking into account the student's current learning progress. The sharing unit adjusts the shared content when it is shared, for example, using a generative AI, taking into account the student's current learning progress. The sharing unit receives prompts from the generative AI to evaluate the student's current learning progress and evaluates the progress. The sharing unit receives prompts to adjust the shared content based on the progress evaluated by the generative AI and adjusts the content. The sharing unit adjusts the shared content based on the student's current learning progress. The sharing unit adjusts the level of detail of the shared content according to the student's current learning progress. The sharing unit customizes the shared content, for example, taking into account the student's current learning progress. As a result, the learning support system can share information more appropriately by adjusting the shared content while taking into account the current learning progress.

[0058] The learning support system, when sharing information, selects the optimal sharing method by considering the student's geographical location. The sharing unit, for example, uses a generative AI to select the optimal sharing method by considering the student's geographical location. The sharing unit, for example, receives a prompt from the generative AI to evaluate the student's geographical location and evaluates the geographical location. The sharing unit, for example, receives a prompt to select the optimal sharing method based on the geographical location evaluated by the generative AI and selects a method. The sharing unit, for example, selects a sharing method by considering the educational environment of the area where the student lives. The sharing unit, for example, selects a sharing method by considering the educational policies of the school the student attends. The sharing unit, for example, selects a sharing method by considering the culture and customs of the area where the student lives. As a result, the learning support system can share information more appropriately by selecting the optimal sharing method by considering geographical location.

[0059] The learning support system analyzes students' social media activity and adjusts the shared content when it is shared. The sharing unit, for example, uses generative AI to analyze students' social media activity and adjust the shared content when it is shared. The sharing unit, for example, receives a prompt for the generative AI to analyze students' social media activity and analyzes the activity. The sharing unit, for example, receives a prompt for the generative AI to adjust the shared content based on the activity analyzed and adjusts the content. The sharing unit, for example, adjusts the shared content based on topics that students have shown interest in on social media. The sharing unit, for example, adjusts the shared content based on statements made by influencers that students follow. The sharing unit, for example, adjusts the shared content based on topics in online communities that students participate in. As a result, the learning support system can share information more appropriately by analyzing social media activity and adjusting the shared content.

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

[0061] The learning support system uses a generation unit to analyze a student's past learning history and generate an optimal learning curriculum. For example, the generation unit uses a generation AI to analyze the student's past learning history and generate an optimal learning curriculum. The generation unit receives a prompt, for example, for the generation AI to analyze the student's past learning history and analyzes the history. The generation unit receives a prompt, for example, for the generation AI to generate an optimal learning curriculum based on the analyzed history and generates the curriculum. For example, the generation unit generates a curriculum that focuses on areas the student has struggled with in the past. For example, the generation unit generates a curriculum that reinforces areas the student excels at. For example, the generation unit generates a curriculum that matches the student's progress based on their past learning history. This enables effective learning by generating an optimal curriculum based on past learning history.

[0062] The learning support system adjusts the curriculum when it is generated, taking into account the student's current learning progress. For example, the generation unit adjusts the curriculum when it is generated, taking into account the student's current learning progress, using a generation AI. For example, the generation unit receives a prompt for the generation AI to consider the student's current learning progress and takes the progress into account. For example, the generation unit receives a prompt for the generation AI to adjust the curriculum taking the progress into account and adjusts the curriculum. For example, if the student understands the current learning content, the generation unit generates a curriculum that moves to the next step. For example, if the student is struggling with the current learning content, the generation unit generates a curriculum that includes supplementary explanations. For example, the generation unit generates a curriculum that includes review depending on the student's progress. In this way, the learning support system enables effective learning by adjusting the curriculum while taking into account the student's current learning progress.

[0063] The learning support system prioritizes incorporating highly relevant content by considering the student's geographical location when generating the learning curriculum. For example, the generation unit uses a generation AI to prioritize incorporating highly relevant content by considering the student's geographical location when generating the learning curriculum. For example, the generation unit receives a prompt from the generation AI to consider the student's geographical location and takes that information into account. For example, the generation unit receives a prompt from the generation AI to incorporate highly relevant content by considering the geographical location and incorporates that content. For example, the generation unit incorporates content related to the history and culture of the area where the student lives into the curriculum. For example, the generation unit prioritizes incorporating content that aligns with the curriculum of the school the student attends. For example, the generation unit incorporates information about a region that the student is interested in into the curriculum. As a result, the learning support system enables effective learning by incorporating highly relevant content while considering geographical location.

[0064] The learning support system adjusts the level of detail provided based on the importance of the learning content when the delivery unit provides the learning content. For example, the delivery unit may use a generative AI to adjust the level of detail based on the importance of the learning content when providing the learning content. For example, the delivery unit may receive a prompt from the generative AI to evaluate the importance of the learning content and evaluate the importance. For example, the delivery unit may receive a prompt to adjust the level of detail based on the importance evaluated by the generative AI and adjust the level of detail. For example, the delivery unit may adopt a delivery method that includes detailed explanations for important content. For example, the delivery unit may adopt a delivery method that includes concise explanations for less important content. For example, the delivery unit may provide visual aids and supplementary materials according to the importance level. In this way, the learning support system enables effective learning by adjusting the level of detail provided based on the importance of the learning content.

[0065] The learning support system applies different delivery algorithms depending on the category of the learning content when the delivery unit delivers the learning content. For example, the delivery unit applies different delivery algorithms depending on the category of the learning content when delivering learning content using a generative AI. For example, the delivery unit receives a prompt from the generative AI to classify the category of the learning content and classifies the category. For example, the delivery unit receives a prompt from the generative AI to apply a delivery algorithm according to the category classified and applies the algorithm. For example, for science content, the delivery unit adopts a delivery method that includes experimental videos. For example, for mathematics content, the delivery unit adopts a delivery method that includes step-by-step explanations. For example, for social studies content, the delivery unit adopts a storytelling-style delivery method that includes historical background. As a result, the learning support system enables effective learning by applying a delivery algorithm according to the category of the learning content.

[0066] The learning support system prioritizes the provision of learning content based on the student's submission timing when the provision unit provides learning content. The provision unit uses, for example, a generative AI to determine the priority of provision based on the student's submission timing when providing learning content. The provision unit receives, for example, a prompt from the generative AI to evaluate the student's submission timing and evaluates the submission timing. The provision unit receives, for example, a prompt to determine the priority of provision based on the submission timing evaluated by the generative AI and determines the priority. The provision unit provides, for example, content with an approaching submission deadline first. The provision unit provides, for example, content with a distant submission deadline later. The provision unit adjusts the order in which learning content is provided according to the submission deadline. As a result, the learning support system enables effective learning by determining the priority of provision based on submission timing.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The generation unit creates an optimal learning curriculum based on the student's interests and goals. The generation unit identifies the student's interests and goals from surveys, interviews, and past learning history, and creates a learning curriculum based on that. Using the generation AI, it is also possible to receive prompts to generate a learning curriculum based on the student's interests and goals and generate an optimal learning curriculum. Step 2: The providing unit provides learning content based on the learning curriculum generated by the generating unit. The providing unit may or may not use AI to provide learning content based on the generated learning curriculum. Step 3: The analysis unit analyzes students' learning styles and provides optimal learning materials. The analysis unit classifies students' learning styles into visual, auditory, experiential, etc., and provides materials accordingly. It can also use generative AI to receive prompts to analyze students' learning styles and provide optimal learning materials.

[0069] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that provides an individually optimized learning plan tailored to each student's interests, learning style, and level of understanding. This learning support system uses a generative AI to create an optimal learning curriculum based on the student's interests and goals. Next, the generative AI analyzes the student's learning style and provides the most suitable learning materials according to their learning style, such as visual, auditory, and kinesthetic. Furthermore, the generative AI analyzes learning data in real time and adjusts the next learning content according to the student's level of understanding. The generative AI also supports goal setting and motivation maintenance to encourage student autonomy. Finally, the generative AI shares the student's learning status with the teacher and proposes necessary support. For example, the learning support system generates an optimal learning curriculum based on the student's interests and goals. For example, the learning support system analyzes the student's learning style and provides the most suitable learning materials according to their learning style, such as visual, auditory, and kinesthetic. For example, the learning support system analyzes learning data in real time and adjusts the next learning content according to the student's level of understanding. For example, the learning support system supports goal setting and motivation maintenance to encourage student autonomy. For example, the learning support system shares the student's learning status with the teacher and proposes necessary support. This allows the learning support system to guide students' learning in line with their interests and goals, thereby increasing their self-efficacy. It also reduces the burden on teachers and enables more effective support.

[0070] The learning support system according to this embodiment comprises a generation unit, a provision unit, and an analysis unit. The generation unit creates an optimal learning curriculum based on the student's interests and goals. The generation unit generates an optimal learning curriculum based on the student's interests and goals, for example, using a generation AI. The generation unit can identify the student's interests and goals from, for example, questionnaires, interviews, past learning history, etc., and create a learning curriculum based on them. The generation unit receives, for example, a prompt from the generation AI to generate a learning curriculum based on the student's interests and goals, and generates an optimal learning curriculum. The provision unit provides learning content based on the learning curriculum generated by the generation unit. The provision unit provides learning content based on the generated learning curriculum, for example. The provision unit can also provide learning content using, for example, AI in order to provide learning content based on the generated learning curriculum. The provision unit can also provide learning content without using AI in order to provide learning content based on the generated learning curriculum, for example. The analysis unit analyzes the student's learning style and provides optimal learning materials. The analysis unit analyzes the student's learning style using, for example, a generation AI and provides optimal learning materials. The analysis unit can, for example, classify students' learning styles into visual, auditory, experiential, etc., and provide learning materials accordingly. The analysis unit can, for example, receive prompts from the generating AI to analyze students' learning styles and provide optimal learning materials. As a result, the learning support system according to this embodiment can provide an optimal learning curriculum based on students' interests and goals, and provide learning materials that match their learning style, thereby enabling individually optimized learning.

[0071] The generation unit creates an optimal learning curriculum based on the student's interests and goals. For example, it uses a generation AI to generate the optimal learning curriculum based on the student's interests and goals. Specifically, the generation unit identifies the student's interests and goals from questionnaires, interviews, and past learning history, and creates a learning curriculum based on that. Questionnaires ask in detail about subjects the student is interested in, future goals, and learning progress. Interviews allow teachers and counselors to directly interact with students and gain a deeper understanding. Past learning history includes what the student has learned so far, their grades, and their learning pace. After collecting this information, the generation AI analyzes this data and generates an optimal learning curriculum for the student. The generation AI uses natural language processing technology to analyze the content of questionnaires and interviews and extract the student's interests and goals. It also uses machine learning algorithms to analyze past learning history and identify the student's learning patterns and areas of strength and weakness. This allows the generation unit to provide an optimal learning curriculum for each individual student. The generated learning curriculum is designed to stimulate the student's interest and support effective learning toward achieving their goals. For example, students interested in science can be offered a curriculum that emphasizes experiments and observations, while students interested in literature can be offered a curriculum that emphasizes reading and writing. This allows students to pursue learning that aligns with their interests and goals, thereby increasing their motivation to learn.

[0072] The delivery unit provides learning content based on the learning curriculum generated by the generation unit. Specifically, the delivery unit can also use AI to provide learning content based on the generated learning curriculum. For example, the AI ​​can monitor students' learning progress in real time and adjust the learning content as needed. The AI ​​can provide learning content in a format that is easy for students to understand and can answer questions and doubts immediately. For example, the AI ​​can detect areas that students find difficult to understand and provide additional explanations or examples. The AI ​​can also adjust the learning content to match the student's learning pace, allowing them to learn at their own pace. Furthermore, the delivery unit can also provide learning content without using AI. For example, a teacher can conduct a lesson based on the generated learning curriculum and provide direct instruction to students. The teacher can provide appropriate guidance while observing the students' reactions and level of understanding. This allows the delivery unit to provide optimal learning content for each student and support effective learning. The delivery unit can also provide learning content through an online learning platform. For example, it can provide online classes, video lectures, and interactive learning materials, allowing students to learn at their own pace. This allows the delivery unit to provide a flexible learning environment that suits the learning style and needs of students.

[0073] The analysis unit analyzes students' learning styles and provides optimal learning materials. Specifically, the analysis unit uses generative AI to analyze students' learning styles and provide optimal learning materials. The generative AI analyzes students' learning history and learning behavior data and classifies students' learning styles into visual, auditory, experiential, etc. Visual students are provided with visual materials such as diagrams, graphs, and videos, while auditory students are provided with auditory materials such as audio lectures and podcasts. Experiential students are provided with experiential materials such as experiments, simulations, and project-based learning. This allows the analysis unit to provide optimal learning materials tailored to each student's learning style. Furthermore, the analysis unit monitors students' learning progress and comprehension in real time and adjusts the materials as needed. For example, if a student is not making progress with a particular material, it will provide a different format of material. The analysis unit also collects student feedback and evaluates the effectiveness of the materials. This allows the analysis unit to always provide optimal learning materials and maximize student learning effectiveness. The analysis unit receives prompts from the generative AI to analyze students' learning styles and provides optimal learning materials. For example, the generating AI analyzes students' learning styles based on their learning history and learning behavior data, and then suggests the most suitable learning materials. This allows the analysis unit to provide each student with the most appropriate learning materials and support personalized learning.

[0074] The learning support system includes an evaluation unit that analyzes learning data in real time and adjusts the next learning content according to the student's level of understanding. The evaluation unit, for example, uses a generative AI to analyze learning data in real time and adjusts the next learning content according to the student's level of understanding. The evaluation unit, for example, collects learning data in real time, receives prompts for the generative AI to analyze that data, and adjusts the next learning content according to the level of understanding. The evaluation unit evaluates the level of understanding based on, for example, test results or quiz correct answer rates, and adjusts the next learning content accordingly. The evaluation unit, for example, uses a generative AI to analyze learning data in real time and adjusts the next learning content according to the level of understanding. As a result, the learning support system enables effective learning by adjusting the learning content according to the student's level of understanding.

[0075] The learning support system includes a support unit that assists in goal setting and motivation maintenance to foster student autonomy. The support unit, for example, uses generative AI to support goal setting and motivation maintenance to encourage student autonomy. For example, the support unit receives prompts from the generative AI to support student goal setting and performs goal setting. For example, the support unit receives prompts from the generative AI to support student motivation maintenance and performs motivation maintenance. For example, the support unit sets short-term and long-term goals and supports student learning based on them. For example, the support unit maintains student motivation through reward systems and the provision of feedback. In this way, the learning support system fosters student autonomy and promotes proactive learning.

[0076] The learning support system includes a sharing unit that shares students' learning progress with teachers and proposes necessary support. The sharing unit, for example, uses a generative AI to share students' learning progress with teachers and proposes necessary support. The sharing unit, for example, receives a prompt from the generative AI to share students' learning progress and shares the progress. The sharing unit, for example, receives a prompt from the generative AI to propose necessary support and proposes support. The sharing unit shares information with teachers, for example, students' progress, level of understanding, and assignment status. The sharing unit proposes support such as providing additional learning materials or individual tutoring. This allows the learning support system to enable teachers to understand students' learning progress and provide appropriate support.

[0077] The learning support system uses a generation unit to estimate a student's emotions and adjust the content of the learning curriculum based on the estimated emotions. The generation unit, for example, uses a generation AI to estimate the student's emotions and adjusts the content of the learning curriculum based on the estimated emotions. The generation unit, for example, receives a prompt from the generation AI to estimate the student's emotions and estimates them. The generation unit, for example, receives a prompt from the generation AI to adjust the content of the learning curriculum based on the estimated emotions and adjusts the content. For example, if the student is stressed, the generation unit generates a curriculum that includes content to help them relax. For example, if the student is excited, the generation unit generates a curriculum that includes content to improve concentration. For example, if the student is tired, the generation unit generates a curriculum that includes breaks. This allows the learning support system to provide a more appropriate learning environment by adjusting the learning curriculum according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The learning support system uses a generation unit to analyze a student's past learning history and generate an optimal learning curriculum. For example, the generation unit uses a generation AI to analyze the student's past learning history and generate an optimal learning curriculum. The generation unit receives a prompt, for example, for the generation AI to analyze the student's past learning history and analyzes the history. The generation unit receives a prompt, for example, for the generation AI to generate an optimal learning curriculum based on the analyzed history and generates the curriculum. For example, the generation unit generates a curriculum that focuses on areas the student has struggled with in the past. For example, the generation unit generates a curriculum that reinforces areas the student excels at. For example, the generation unit generates a curriculum that matches the student's progress based on their past learning history. This enables effective learning by generating an optimal curriculum based on past learning history.

[0079] The learning support system adjusts the curriculum when it is generated, taking into account the student's current learning progress. For example, the generation unit adjusts the curriculum when it is generated, taking into account the student's current learning progress, using a generation AI. For example, the generation unit receives a prompt for the generation AI to consider the student's current learning progress and takes the progress into account. For example, the generation unit receives a prompt for the generation AI to adjust the curriculum taking the progress into account and adjusts the curriculum. For example, if the student understands the current learning content, the generation unit generates a curriculum that moves to the next step. For example, if the student is struggling with the current learning content, the generation unit generates a curriculum that includes supplementary explanations. For example, the generation unit generates a curriculum that includes review depending on the student's progress. In this way, the learning support system enables effective learning by adjusting the curriculum while taking into account the student's current learning progress.

[0080] The learning support system uses a generation unit to estimate the student's emotions and prioritize the learning curriculum based on the estimated emotions. The generation unit, for example, uses a generation AI to estimate the student's emotions and prioritizes the learning curriculum based on the estimated emotions. The generation unit receives a prompt from the generation AI to estimate the student's emotions and estimates them. The generation unit receives a prompt from the generation AI to prioritize the learning curriculum based on the estimated emotions and determines the priorities. For example, if the student is stressed, the generation unit prioritizes content that promotes relaxation. If the student is excited, the generation unit prioritizes content that enhances concentration. If the student is tired, the generation unit prioritizes content that includes breaks. This allows the learning support system to provide a more appropriate learning environment by prioritizing the learning curriculum according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The learning support system prioritizes incorporating highly relevant content by considering the student's geographical location when generating the learning curriculum. For example, the generation unit uses a generation AI to prioritize incorporating highly relevant content by considering the student's geographical location when generating the learning curriculum. For example, the generation unit receives a prompt from the generation AI to consider the student's geographical location and takes that information into account. For example, the generation unit receives a prompt from the generation AI to incorporate highly relevant content by considering the geographical location and incorporates that content. For example, the generation unit incorporates content related to the history and culture of the area where the student lives into the curriculum. For example, the generation unit prioritizes incorporating content that aligns with the curriculum of the school the student attends. For example, the generation unit incorporates information about a region that the student is interested in into the curriculum. As a result, the learning support system enables effective learning by incorporating highly relevant content while considering geographical location.

[0082] The learning support system analyzes students' social media activity when generating the learning curriculum and incorporates relevant learning content. For example, the generation unit uses a generation AI to analyze students' social media activity and incorporate relevant learning content when generating the learning curriculum. For example, the generation unit receives a prompt from the generation AI to analyze students' social media activity and analyzes the activity. For example, the generation unit receives a prompt from the generation AI to incorporate relevant learning content based on the social media activity analyzed and incorporates the content. For example, the generation unit incorporates topics that students have shown interest in on social media into the curriculum. For example, the generation unit incorporates content related to statements made by influencers that students follow into the curriculum. For example, the generation unit incorporates topics from online communities that students participate in into the curriculum. In this way, the learning support system enables effective learning by analyzing social media activity and incorporating relevant learning content.

[0083] The learning support system estimates the student's emotions and adjusts the method of delivering learning content based on the estimated emotions. For example, the delivery unit uses generative AI to estimate the student's emotions and adjusts the method of delivering learning content based on the estimated emotions. For example, the delivery unit receives a prompt from the generative AI to estimate the student's emotions and estimates the emotions. For example, the delivery unit receives a prompt from the generative AI to adjust the method of delivering learning content based on the estimated emotions and adjusts the method of delivery. For example, if the student is feeling stressed, the delivery unit delivers learning content while playing relaxing music in the background. For example, if the student is excited, the delivery unit delivers learning content in a quiet environment to enhance concentration. For example, if the student is tired, the delivery unit delivers learning content with short breaks in between. In this way, the learning support system can provide a more appropriate learning environment by adjusting the method of delivering learning content according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0084] The learning support system adjusts the level of detail provided based on the importance of the learning content when the delivery unit provides the learning content. For example, the delivery unit may use a generative AI to adjust the level of detail based on the importance of the learning content when providing the learning content. For example, the delivery unit may receive a prompt from the generative AI to evaluate the importance of the learning content and evaluate the importance. For example, the delivery unit may receive a prompt to adjust the level of detail based on the importance evaluated by the generative AI and adjust the level of detail. For example, the delivery unit may adopt a delivery method that includes detailed explanations for important content. For example, the delivery unit may adopt a delivery method that includes concise explanations for less important content. For example, the delivery unit may provide visual aids and supplementary materials according to the importance level. In this way, the learning support system enables effective learning by adjusting the level of detail provided based on the importance of the learning content.

[0085] The learning support system applies different delivery algorithms depending on the category of the learning content when the delivery unit delivers the learning content. For example, the delivery unit applies different delivery algorithms depending on the category of the learning content when delivering learning content using a generative AI. For example, the delivery unit receives a prompt from the generative AI to classify the category of the learning content and classifies the category. For example, the delivery unit receives a prompt from the generative AI to apply a delivery algorithm according to the category classified and applies the algorithm. For example, for science content, the delivery unit adopts a delivery method that includes experimental videos. For example, for mathematics content, the delivery unit adopts a delivery method that includes step-by-step explanations. For example, for social studies content, the delivery unit adopts a storytelling-style delivery method that includes historical background. As a result, the learning support system enables effective learning by applying a delivery algorithm according to the category of the learning content.

[0086] The learning support system estimates the student's emotions and adjusts the order in which learning content is delivered based on the estimated emotions. The delivery unit estimates the student's emotions using, for example, generative AI and adjusts the order in which learning content is delivered based on the estimated emotions. The delivery unit receives, for example, a prompt for the generative AI to estimate the student's emotions and estimates the emotions. The delivery unit receives, for example, a prompt for the generative AI to adjust the order in which learning content is delivered based on the estimated emotions and adjusts the order. For example, if the student is feeling stressed, the delivery unit will first provide content that helps them relax. For example, if the student is excited, the delivery unit will first provide content that helps them concentrate. For example, if the student is tired, the delivery unit will first provide content that includes a break. In this way, the learning support system can provide a more appropriate learning environment by adjusting the order in which learning content is delivered according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The learning support system prioritizes the provision of learning content based on the student's submission timing when the provision unit provides learning content. The provision unit uses, for example, a generative AI to determine the priority of provision based on the student's submission timing when providing learning content. The provision unit receives, for example, a prompt from the generative AI to evaluate the student's submission timing and evaluates the submission timing. The provision unit receives, for example, a prompt to determine the priority of provision based on the submission timing evaluated by the generative AI and determines the priority. The provision unit provides, for example, content with an approaching submission deadline first. The provision unit provides, for example, content with a distant submission deadline later. The provision unit adjusts the order in which learning content is provided according to the submission deadline. As a result, the learning support system enables effective learning by determining the priority of provision based on submission timing.

[0088] The learning support system adjusts the order of content delivery based on the relevance of the learning content when the delivery unit delivers the content. The delivery unit adjusts the order of content delivery based on the relevance of the learning content when delivering the content, for example, using a generative AI. The delivery unit receives prompts from the generative AI to evaluate the relevance of the learning content and evaluates the relevance. The delivery unit receives prompts from the generative AI to adjust the order of delivery based on the relevance evaluated and adjusts the order. The delivery unit provides highly relevant content consecutively. The delivery unit provides less relevant content with intervals in between. The delivery unit adjusts the order of delivery according to the relevance of the learning content. As a result, the learning support system enables effective learning by adjusting the order of delivery based on the relevance of the learning content.

[0089] The learning support system uses an analysis unit to estimate the student's emotions and adjust the learning style analysis method based on the estimated emotions. The analysis unit, for example, uses a generative AI to estimate the student's emotions and adjusts the learning style analysis method based on the estimated emotions. The analysis unit receives a prompt from the generative AI to estimate the student's emotions and estimates them. The analysis unit receives a prompt from the generative AI to adjust the learning style analysis method based on the estimated emotions and adjusts the analysis method. For example, if the student is stressed, the analysis unit analyzes the learning style in a relaxing environment. If the student is excited, the analysis unit analyzes the learning style in an environment that enhances concentration. If the student is tired, the analysis unit analyzes the learning style while incorporating breaks. In this way, the learning support system can provide a more appropriate learning style by adjusting the learning style analysis method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0090] The learning support system improves the accuracy of its analysis by referring to the student's past learning data when analyzing learning styles. The analysis unit improves the accuracy of its analysis by referring to the student's past learning data when analyzing learning styles, for example, using a generative AI. The analysis unit receives a prompt, for example, for the generative AI to refer to the student's past learning data and refers to the data. The analysis unit receives a prompt, for example, for the generative AI to improve the accuracy of its analysis based on the data it has referred to and improves the accuracy. The analysis unit performs a detailed analysis of the learning style based on the student's past learning data. The analysis unit analyzes changes in the learning style from the student's past learning data. The analysis unit analyzes trends in the learning style by referring to the student's past learning data. As a result, the learning support system can provide a more appropriate learning style by improving the accuracy of its analysis by referring to past learning data.

[0091] The learning support system's analysis unit considers the student's attribute information when analyzing their learning style. For example, the analysis unit uses a generative AI to consider the student's attribute information when analyzing their learning style. For example, the analysis unit receives a prompt from the generative AI to consider the student's attribute information and takes that information into account. For example, the analysis unit receives a prompt from the generative AI to perform an analysis based on the attribute information it has considered and performs the analysis. For example, the analysis unit analyzes the learning style considering the student's age and gender. For example, the analysis unit analyzes the learning style considering the student's grade level and learning history. For example, the analysis unit analyzes the learning style considering the student's interests and concerns. As a result, the learning support system can provide a more appropriate learning style by performing an analysis that takes the student's attribute information into consideration.

[0092] The learning support system uses an analysis unit to estimate a student's emotions and adjust the display method of the analysis results based on the estimated emotions. The analysis unit, for example, uses a generative AI to estimate the student's emotions and adjusts the display method of the analysis results based on the estimated emotions. The analysis unit, for example, receives a prompt from the generative AI to estimate the student's emotions and estimates the emotions. The analysis unit, for example, receives a prompt from the generative AI to adjust the display method of the analysis results based on the estimated emotions and adjusts the display method. For example, if the student is stressed, the analysis unit provides a simple and highly visible display method. For example, if the student is excited, the analysis unit provides a display method that includes detailed information. For example, if the student is tired, the analysis unit displays the analysis results with breaks in between. This allows the learning support system to provide more appropriate information by adjusting the display method of the analysis results according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The learning support system's analysis unit considers the geographical distribution of students when analyzing their learning styles. For example, the analysis unit uses a generative AI to consider the geographical distribution of students when analyzing their learning styles. For example, the analysis unit receives a prompt from the generative AI to consider the geographical distribution of students and takes it into account. For example, the analysis unit receives a prompt from the generative AI to perform an analysis based on the geographical distribution considered and performs the analysis. For example, the analysis unit analyzes learning styles considering the educational environment of the area where the student lives. For example, the analysis unit analyzes learning styles considering the educational policies of the school the student attends. For example, the analysis unit analyzes learning styles considering the culture and customs of the area where the student lives. As a result, the learning support system can provide more appropriate learning styles by considering the geographical distribution of students when performing the analysis.

[0094] The learning support system improves the accuracy of its analysis by referring to relevant literature on learning content when analyzing learning styles. The analysis unit improves the accuracy of its analysis by referring to relevant literature on learning content when analyzing learning styles, for example, using a generative AI. The analysis unit receives a prompt from the generative AI to refer to relevant literature on learning content and refers to the literature. The analysis unit receives a prompt from the generative AI to improve the accuracy of its analysis based on the literature it has referred to and improves the accuracy. The analysis unit analyzes learning styles by referring to literature related to learning content, for example. The analysis unit analyzes learning styles by referring to research results related to learning content, for example. The analysis unit analyzes learning styles by referring to data related to learning content, for example. As a result, the learning support system can provide more appropriate learning styles by improving the accuracy of its analysis by referring to relevant literature on learning content.

[0095] The learning support system has an evaluation unit that estimates a student's emotions and adjusts the display method of the evaluation based on the estimated emotions. The evaluation unit estimates a student's emotions using, for example, a generative AI and adjusts the display method of the evaluation based on the estimated emotions. The evaluation unit receives, for example, a prompt for the generative AI to estimate a student's emotions and estimates the emotions. The evaluation unit receives, for example, a prompt for the generative AI to adjust the display method of the evaluation based on the emotions estimated by the generative AI and adjusts the display method. For example, if the student is stressed, the evaluation unit provides a simple and highly visible evaluation display method. For example, if the student is excited, the evaluation unit provides an evaluation display method that includes detailed information. For example, if the student is tired, the evaluation unit displays the evaluation with breaks in between. In this way, the learning support system can provide more appropriate evaluations by adjusting the display method of the evaluation according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The learning support system allows the evaluation unit to predict the current evaluation by referring to past evaluation data during the evaluation process. The evaluation unit, for example, uses a generative AI to predict the current evaluation by referring to past evaluation data during the evaluation process. The evaluation unit, for example, receives a prompt for the generative AI to refer to past evaluation data and then refers to the data. The evaluation unit, for example, receives a prompt for the generative AI to predict the current evaluation based on the data it has referred to and then predicts the evaluation. The evaluation unit, for example, predicts the current evaluation based on the student's past evaluation data. The evaluation unit, for example, predicts evaluation trends from the student's past evaluation data. The evaluation unit, for example, refers to the student's past evaluation data to predict changes in evaluation. As a result, the learning support system can perform more appropriate evaluations by predicting the current evaluation by referring to past evaluation data.

[0097] The learning support system's evaluation unit applies different evaluation methods to each category of learning content during evaluation. For example, the evaluation unit uses a generative AI to apply different evaluation methods to each category of learning content during evaluation. For example, the evaluation unit receives a prompt from the generative AI to classify the categories of learning content and classifies them. For example, the evaluation unit receives a prompt from the generative AI to apply an evaluation method according to the classified categories and applies the method. For example, for science content, the evaluation unit applies an evaluation method that emphasizes experimental results. For example, for mathematics content, the evaluation unit applies an evaluation method that emphasizes the accuracy of the answers. For example, for social studies content, the evaluation unit applies an evaluation method that emphasizes the level of understanding. In this way, the learning support system can perform more appropriate evaluations by applying different evaluation methods to each category of learning content.

[0098] The learning support system has an evaluation unit that estimates the student's emotions and adjusts the importance of the evaluation based on the estimated emotions. The evaluation unit estimates the student's emotions using, for example, a generative AI and adjusts the importance of the evaluation based on the estimated emotions. The evaluation unit receives, for example, a prompt for the generative AI to estimate the student's emotions and estimates the emotions. The evaluation unit receives, for example, a prompt for the generative AI to adjust the importance of the evaluation based on the emotions estimated by the generative AI and adjusts the importance. For example, if the student is stressed, the evaluation unit sets the importance of the evaluation low. For example, if the student is excited, the evaluation unit sets the importance of the evaluation high. For example, if the student is tired, the evaluation unit sets the importance of the evaluation to medium. In this way, the learning support system can provide more appropriate evaluations by adjusting the importance of the evaluation according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The learning support system's evaluation unit analyzes changes in evaluation based on the submission timing of learning content during evaluation. The evaluation unit, for example, uses a generative AI to analyze changes in evaluation based on the submission timing of learning content during evaluation. The evaluation unit receives prompts from the generative AI to evaluate the submission timing of learning content and evaluates the submission timing. The evaluation unit receives prompts from the generative AI to analyze changes in evaluation based on the evaluated submission timing and analyzes the changes. For example, the evaluation unit analyzes changes in evaluation in detail for content with an approaching submission deadline. For example, the evaluation unit analyzes changes in evaluation roughly for content with a distant submission deadline. The evaluation unit analyzes changes in evaluation in stages according to the submission deadline. As a result, the learning support system can perform more appropriate evaluations by analyzing changes in evaluation based on the submission timing of learning content.

[0100] The learning support system analyzes evaluations by referencing relevant market data for the learning content during the evaluation process. The evaluation unit, for example, uses a generative AI to analyze evaluations by referencing relevant market data for the learning content during the evaluation process. The evaluation unit receives prompts from the generative AI to reference relevant market data and references the market data. The evaluation unit receives prompts from the generative AI to analyze evaluations based on the market data referenced and analyzes the evaluations. The evaluation unit analyzes evaluations by referencing market data related to the learning content, for example. The evaluation unit analyzes evaluations by referencing industry trends related to the learning content, for example. The evaluation unit analyzes evaluations by referencing occupational demand related to the learning content, for example. As a result, the learning support system can perform more appropriate evaluations by analyzing evaluations by referencing relevant market data for the learning content.

[0101] The learning support system works by having the support unit estimate the student's emotions and adjusting goal setting and motivation maintenance methods based on the estimated emotions. For example, the support unit might use generative AI to estimate the student's emotions and adjust goal setting and motivation maintenance methods based on the estimated emotions. The support unit might receive a prompt from the generative AI to estimate the student's emotions and then estimate the emotions. For example, the support unit might receive a prompt from the generative AI to adjust goal setting and motivation maintenance methods based on the estimated emotions and then adjust the methods. For example, if the student is feeling stressed, the support unit might suggest relaxing goals. For example, if the student is excited, the support unit might suggest challenging goals. For example, if the student is tired, the support unit might suggest goals that include breaks. This allows the learning support system to provide more appropriate support by adjusting goal setting and motivation maintenance methods according to 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) and multimodal generation AI.

[0102] The learning support system, when providing support, analyzes the student's past learning behavior and selects the optimal support method. The support unit, for example, uses generative AI to analyze the student's past learning behavior and selects the optimal support method. The support unit, for example, receives a prompt from the generative AI to analyze the student's past learning behavior and analyzes the behavior. The support unit, for example, receives a prompt from the generative AI to select the optimal support method based on the behavior analyzed and selects a method. The support unit, for example, selects the optimal support method based on the student's past learning behavior. The support unit, for example, selects a method for maintaining motivation from the student's past learning behavior. The support unit, for example, analyzes the student's past learning behavior and selects a method for setting goals. As a result, the learning support system can provide more appropriate support by analyzing past learning behavior and selecting the optimal support method.

[0103] The learning support system customizes the means of support based on the student's current living situation when the support unit provides support. For example, the support unit uses a generative AI to customize the means of support based on the student's current living situation when providing support. For example, the support unit receives a prompt from the generative AI to evaluate the student's current living situation and evaluates the living situation. For example, the support unit receives a prompt to customize the means of support based on the living situation evaluated by the generative AI and customizes the means. For example, the support unit provides the optimal means of support, taking into account the student's current living situation. For example, the support unit customizes the means of maintaining motivation according to the student's current living situation. For example, the support unit customizes the means of setting goals based on the student's current living situation. As a result, the learning support system can provide more appropriate support by customizing the means of support based on the current living situation.

[0104] The learning support system has a support unit that estimates the student's emotions and determines the priority of support based on the estimated emotions. The support unit estimates the student's emotions using, for example, generative AI and determines the priority of support based on the estimated emotions. The support unit receives, for example, a prompt for the generative AI to estimate the student's emotions and estimates the emotions. The support unit receives, for example, a prompt for the generative AI to determine the priority of support based on the estimated emotions and determines the priority. For example, if the student is feeling stressed, the support unit will prioritize providing relaxing support. For example, if the student is excited, the support unit will prioritize providing challenging support. For example, if the student is tired, the support unit will prioritize providing support that includes breaks. In this way, the learning support system can provide more appropriate support by determining the priority of support according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The learning support system's support unit selects the optimal support method by considering the student's geographical location information when providing support. For example, the support unit uses a generative AI to select the optimal support method by considering the student's geographical location information. The support unit receives a prompt from the generative AI to evaluate the student's geographical location information and evaluates it. The support unit receives a prompt to select the optimal support method based on the geographical location information evaluated by the generative AI and selects a method. For example, the support unit selects a support method considering the educational environment of the area where the student lives. For example, the support unit selects a support method considering the educational policies of the school the student attends. For example, the support unit selects a support method considering the culture and customs of the area where the student lives. As a result, the learning support system can provide more appropriate support by selecting the optimal support method by considering geographical location information.

[0106] The learning support system analyzes students' social media activity and proposes support methods when the support unit provides assistance. For example, the support unit uses generative AI to analyze students' social media activity and propose support methods. The support unit receives prompts for the generative AI to analyze students' social media activity and performs the analysis. The support unit receives prompts for the generative AI to propose support methods based on the analyzed activity and proposes methods. For example, the support unit proposes support methods based on topics students have shown interest in on social media. For example, the support unit proposes support methods based on statements made by influencers students follow. For example, the support unit proposes support methods based on topics in online communities students participate in. This allows the learning support system to provide more appropriate support by analyzing social media activity and proposing support methods.

[0107] The learning support system has a sharing unit that estimates the student's emotions and adjusts the method of sharing the learning status based on the estimated emotions. The sharing unit estimates the student's emotions using, for example, generative AI and adjusts the method of sharing the learning status based on the estimated emotions. The sharing unit receives, for example, a prompt for the generative AI to estimate the student's emotions and estimates the emotions. The sharing unit receives, for example, a prompt for the generative AI to adjust the method of sharing the learning status based on the estimated emotions and adjusts the sharing method. For example, if the student is feeling stressed, the sharing unit provides a simple and highly visible sharing method. For example, if the student is excited, the sharing unit provides a sharing method that includes detailed information. For example, if the student is tired, the sharing unit shares the learning status with breaks in between. In this way, the learning support system enables more appropriate information sharing by adjusting the method of sharing the learning status according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The learning support system, when sharing information, selects the optimal sharing method by referring to the student's past learning history. The sharing unit, for example, uses a generative AI to select the optimal sharing method by referring to the student's past learning history when sharing information. The sharing unit, for example, receives a prompt for the generative AI to refer to the student's past learning history and refers to the history. The sharing unit, for example, receives a prompt to select the optimal sharing method based on the history referred by the generative AI and selects a method. The sharing unit, for example, selects the optimal sharing method based on the student's past learning history. The sharing unit, for example, analyzes trends in sharing methods from the student's past learning history. The sharing unit, for example, customizes the sharing method by referring to the student's past learning history. As a result, the learning support system can share information more appropriately by selecting the optimal sharing method by referring to past learning history.

[0109] The learning support system adjusts the shared content when it is shared, taking into account the student's current learning progress. The sharing unit adjusts the shared content when it is shared, for example, using a generative AI, taking into account the student's current learning progress. The sharing unit receives prompts from the generative AI to evaluate the student's current learning progress and evaluates the progress. The sharing unit receives prompts to adjust the shared content based on the progress evaluated by the generative AI and adjusts the content. The sharing unit adjusts the shared content based on the student's current learning progress. The sharing unit adjusts the level of detail of the shared content according to the student's current learning progress. The sharing unit customizes the shared content, for example, taking into account the student's current learning progress. As a result, the learning support system can share information more appropriately by adjusting the shared content while taking into account the current learning progress.

[0110] The learning support system has a sharing unit that estimates the student's emotions and determines the priority of sharing based on the estimated emotions. The sharing unit estimates the student's emotions using, for example, generative AI and determines the priority of sharing based on the estimated emotions. The sharing unit receives, for example, a prompt for the generative AI to estimate the student's emotions and estimates the emotions. The sharing unit receives, for example, a prompt for the generative AI to determine the priority of sharing based on the estimated emotions and determines the priority. If the student is feeling stressed, the sharing unit prioritizes sharing content that helps them relax. If the student is excited, the sharing unit prioritizes sharing detailed information. If the student is tired, the sharing unit provides content with breaks in between. In this way, the learning support system can share information more appropriately by determining the priority of sharing according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The learning support system, when sharing information, selects the optimal sharing method by considering the student's geographical location. The sharing unit, for example, uses a generative AI to select the optimal sharing method by considering the student's geographical location. The sharing unit, for example, receives a prompt from the generative AI to evaluate the student's geographical location and evaluates the geographical location. The sharing unit, for example, receives a prompt to select the optimal sharing method based on the geographical location evaluated by the generative AI and selects a method. The sharing unit, for example, selects a sharing method by considering the educational environment of the area where the student lives. The sharing unit, for example, selects a sharing method by considering the educational policies of the school the student attends. The sharing unit, for example, selects a sharing method by considering the culture and customs of the area where the student lives. As a result, the learning support system can share information more appropriately by selecting the optimal sharing method by considering geographical location.

[0112] The learning support system analyzes students' social media activity and adjusts the shared content when it is shared. The sharing unit, for example, uses generative AI to analyze students' social media activity and adjust the shared content when it is shared. The sharing unit, for example, receives a prompt for the generative AI to analyze students' social media activity and analyzes the activity. The sharing unit, for example, receives a prompt for the generative AI to adjust the shared content based on the activity analyzed and adjusts the content. The sharing unit, for example, adjusts the shared content based on topics that students have shown interest in on social media. The sharing unit, for example, adjusts the shared content based on statements made by influencers that students follow. The sharing unit, for example, adjusts the shared content based on topics in online communities that students participate in. As a result, the learning support system can share information more appropriately by analyzing social media activity and adjusting the shared content.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The learning support system uses a generation unit to estimate the student's emotions and adjust the content of the learning curriculum based on those emotions. For example, the generation unit might use a generation AI to estimate the student's emotions and adjust the curriculum based on those emotions. Alternatively, the generation unit might receive a prompt from the generation AI to estimate the student's emotions and then estimate them. The generation unit might also receive a prompt from the generation AI to adjust the curriculum based on the estimated emotions and then adjust the content accordingly. For example, if the student is stressed, the generation unit might generate a curriculum that includes relaxing content. If the student is excited, the generation unit might generate a curriculum that includes content to improve concentration. If the student is tired, the generation unit might generate a curriculum that includes breaks. This allows the learning support system to provide a more appropriate learning environment by adjusting the curriculum according to the student's emotions.

[0115] The learning support system uses a generation unit to analyze a student's past learning history and generate an optimal learning curriculum. For example, the generation unit uses a generation AI to analyze the student's past learning history and generate an optimal learning curriculum. The generation unit receives a prompt, for example, for the generation AI to analyze the student's past learning history and analyzes the history. The generation unit receives a prompt, for example, for the generation AI to generate an optimal learning curriculum based on the analyzed history and generates the curriculum. For example, the generation unit generates a curriculum that focuses on areas the student has struggled with in the past. For example, the generation unit generates a curriculum that reinforces areas the student excels at. For example, the generation unit generates a curriculum that matches the student's progress based on their past learning history. This enables effective learning by generating an optimal curriculum based on past learning history.

[0116] The learning support system adjusts the curriculum when it is generated, taking into account the student's current learning progress. For example, the generation unit adjusts the curriculum when it is generated, taking into account the student's current learning progress, using a generation AI. For example, the generation unit receives a prompt for the generation AI to consider the student's current learning progress and takes the progress into account. For example, the generation unit receives a prompt for the generation AI to adjust the curriculum taking the progress into account and adjusts the curriculum. For example, if the student understands the current learning content, the generation unit generates a curriculum that moves to the next step. For example, if the student is struggling with the current learning content, the generation unit generates a curriculum that includes supplementary explanations. For example, the generation unit generates a curriculum that includes review depending on the student's progress. In this way, the learning support system enables effective learning by adjusting the curriculum while taking into account the student's current learning progress.

[0117] The learning support system uses a generation unit to estimate the student's emotions and prioritize the learning curriculum based on those emotions. For example, the generation unit might use a generation AI to estimate the student's emotions and prioritize the learning curriculum based on those emotions. Alternatively, the generation unit might receive a prompt from the generation AI to estimate the student's emotions and then estimate them. The generation unit might also receive a prompt from the generation AI to prioritize the learning curriculum based on the estimated emotions and then prioritize the curriculum. For example, if the student is stressed, the generation unit might prioritize content that promotes relaxation. If the student is excited, the generation unit might prioritize content that enhances concentration. If the student is tired, the generation unit might prioritize content that includes breaks. This allows the learning support system to provide a more appropriate learning environment by prioritizing the learning curriculum according to the student's emotions.

[0118] The learning support system prioritizes incorporating highly relevant content by considering the student's geographical location when generating the learning curriculum. For example, the generation unit uses a generation AI to prioritize incorporating highly relevant content by considering the student's geographical location when generating the learning curriculum. For example, the generation unit receives a prompt from the generation AI to consider the student's geographical location and takes that information into account. For example, the generation unit receives a prompt from the generation AI to incorporate highly relevant content by considering the geographical location and incorporates that content. For example, the generation unit incorporates content related to the history and culture of the area where the student lives into the curriculum. For example, the generation unit prioritizes incorporating content that aligns with the curriculum of the school the student attends. For example, the generation unit incorporates information about a region that the student is interested in into the curriculum. As a result, the learning support system enables effective learning by incorporating highly relevant content while considering geographical location.

[0119] The learning support system estimates the student's emotions and adjusts the method of delivering learning content based on the estimated emotions. For example, the delivery unit uses generative AI to estimate the student's emotions and adjusts the method of delivering learning content based on the estimated emotions. For example, the delivery unit receives a prompt for the generative AI to estimate the student's emotions and estimates the emotions. For example, the delivery unit receives a prompt for the generative AI to adjust the method of delivering learning content based on the estimated emotions and adjusts the method of delivery. For example, if the student is feeling stressed, the delivery unit delivers learning content while playing relaxing music in the background. For example, if the student is excited, the delivery unit delivers learning content in a quiet environment to enhance concentration. For example, if the student is tired, the delivery unit delivers learning content with short breaks in between. In this way, the learning support system can provide a more appropriate learning environment by adjusting the method of delivering learning content according to the student's emotions.

[0120] The learning support system adjusts the level of detail provided based on the importance of the learning content when the delivery unit provides the learning content. For example, the delivery unit may use a generative AI to adjust the level of detail based on the importance of the learning content when providing the learning content. For example, the delivery unit may receive a prompt from the generative AI to evaluate the importance of the learning content and evaluate the importance. For example, the delivery unit may receive a prompt to adjust the level of detail based on the importance evaluated by the generative AI and adjust the level of detail. For example, the delivery unit may adopt a delivery method that includes detailed explanations for important content. For example, the delivery unit may adopt a delivery method that includes concise explanations for less important content. For example, the delivery unit may provide visual aids and supplementary materials according to the importance level. In this way, the learning support system enables effective learning by adjusting the level of detail provided based on the importance of the learning content.

[0121] The learning support system applies different delivery algorithms depending on the category of the learning content when the delivery unit delivers the learning content. For example, the delivery unit applies different delivery algorithms depending on the category of the learning content when delivering learning content using a generative AI. For example, the delivery unit receives a prompt from the generative AI to classify the category of the learning content and classifies the category. For example, the delivery unit receives a prompt from the generative AI to apply a delivery algorithm according to the category classified and applies the algorithm. For example, for science content, the delivery unit adopts a delivery method that includes experimental videos. For example, for mathematics content, the delivery unit adopts a delivery method that includes step-by-step explanations. For example, for social studies content, the delivery unit adopts a storytelling-style delivery method that includes historical background. As a result, the learning support system enables effective learning by applying a delivery algorithm according to the category of the learning content.

[0122] The learning support system estimates the student's emotions and adjusts the order in which learning content is delivered based on the estimated emotions. For example, the delivery unit uses generative AI to estimate the student's emotions and adjusts the order in which learning content is delivered based on the estimated emotions. For example, the delivery unit receives a prompt for the generative AI to estimate the student's emotions and estimates the emotions. For example, the delivery unit receives a prompt for adjusting the order in which learning content is delivered based on the emotions estimated by the generative AI and adjusts the order. For example, if the student is feeling stressed, the delivery unit will first provide content that helps them relax. For example, if the student is excited, the delivery unit will first provide content that helps them concentrate. For example, if the student is tired, the delivery unit will first provide content that allows them to take a break. In this way, the learning support system can provide a more appropriate learning environment by adjusting the order in which learning content is delivered according to the student's emotions.

[0123] The learning support system prioritizes the provision of learning content based on the student's submission timing when the provision unit provides learning content. The provision unit uses, for example, a generative AI to determine the priority of provision based on the student's submission timing when providing learning content. The provision unit receives, for example, a prompt from the generative AI to evaluate the student's submission timing and evaluates the submission timing. The provision unit receives, for example, a prompt to determine the priority of provision based on the submission timing evaluated by the generative AI and determines the priority. The provision unit provides, for example, content with an approaching submission deadline first. The provision unit provides, for example, content with a distant submission deadline later. The provision unit adjusts the order in which learning content is provided according to the submission deadline. As a result, the learning support system enables effective learning by determining the priority of provision based on submission timing.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The generation unit creates an optimal learning curriculum based on the student's interests and goals. The generation unit identifies the student's interests and goals from surveys, interviews, and past learning history, and creates a learning curriculum based on that. Using the generation AI, it is also possible to receive prompts to generate a learning curriculum based on the student's interests and goals and generate an optimal learning curriculum. Step 2: The providing unit provides learning content based on the learning curriculum generated by the generating unit. The providing unit may or may not use AI to provide learning content based on the generated learning curriculum. Step 3: The analysis unit analyzes students' learning styles and provides optimal learning materials. The analysis unit classifies students' learning styles into visual, auditory, experiential, etc., and provides materials accordingly. It can also use generative AI to receive prompts to analyze students' learning styles and provide optimal learning materials.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the generation unit, provision unit, analysis unit, evaluation unit, support unit, and sharing unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the generation 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 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 analysis 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 evaluation 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 support 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 sharing 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.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the generation unit, supply unit, analysis unit, evaluation unit, support unit, and sharing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the generation 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 supply 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 analysis 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 evaluation 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 support 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 sharing 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.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the generation unit, provision unit, analysis unit, evaluation unit, support unit, and sharing unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 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 unit 12. The analysis 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 unit 12. The evaluation 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 unit 12. The support 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 unit 12. The sharing 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 unit 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.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the generation unit, supply unit, analysis unit, evaluation unit, support unit, and sharing unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The supply unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The sharing unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A generation unit that creates the optimal learning curriculum based on students' interests and goals, A providing unit that provides learning content based on the learning curriculum generated by the generation unit, It includes an analysis unit that analyzes students' learning styles and provides optimal learning materials. A system characterized by the following features. (Note 2) It includes an evaluation unit that analyzes learning data in real time and adjusts the next learning content according to the student's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 3) To foster student autonomy, the school has a support department that assists with goal setting and maintaining motivation. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a shared section to share students' learning progress with teachers and propose necessary support. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The system estimates students' emotions and adjusts the learning curriculum content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Analyze students' past learning history and generate an optimal learning curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When generating a learning curriculum, adjust the curriculum to take into account the students' current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is The system estimates students' emotions and prioritizes the learning curriculum based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating the learning curriculum, the system prioritizes incorporating highly relevant content by considering students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating the learning curriculum, analyze students' social media activity and incorporate relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, The system estimates students' emotions and adjusts the way learning content is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing learning materials, adjust the level of detail based on the importance of the learning material. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When providing learning content, different delivery algorithms are applied depending on the category of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, The system estimates students' emotions and adjusts the order in which learning content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing learning materials, the priority of provision will be determined based on when students submit them. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing learning materials, adjust the order of presentation based on the relevance of the learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We estimate students' emotions and adjust the learning style analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing learning styles, we improve the accuracy of the analysis by referring to students' past learning data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing learning styles, the analysis takes into account the attribute information of the student submitting the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The system estimates students' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When analyzing learning styles, the geographical distribution of students should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, When analyzing learning styles, we improve the accuracy of the analysis by referring to relevant literature on the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, The system estimates students' emotions and adjusts how evaluations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation process, past evaluation data is referenced to predict the current evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During evaluation, different evaluation methods are applied to each category of learning content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, The system estimates students' emotions and adjusts the importance of assessments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, During evaluation, analyze how evaluations change based on when learning materials are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, During evaluation, the evaluation is analyzed by referring to market data related to the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is The system estimates students' emotions and adjusts goal setting and motivation maintenance methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support sessions, we analyze students' past learning behaviors to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is When providing support, customize the support methods based on the student's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is The system estimates students' emotions and prioritizes support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is When providing support, the most suitable support method will be selected considering the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is During support sessions, we analyze students' social media activity and propose support strategies. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned shared portion is, The system estimates students' emotions and adjusts how learning progress is shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shared portion is, When sharing, the system will refer to students' past learning history to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned shared portion is, When sharing, adjust the content to take into account the students' current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned shared portion is, The system estimates students' emotions and determines the priority of shared activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned shared portion is, When sharing, the optimal sharing method will be selected, taking into account the students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned shared portion is, When sharing, we analyze students' social media activity and adjust the content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that creates the optimal learning curriculum based on students' interests and goals, A providing unit that provides learning content based on the learning curriculum generated by the generation unit, It includes an analysis unit that analyzes students' learning styles and provides optimal learning materials. A system characterized by the following features.

2. It includes an evaluation unit that analyzes learning data in real time and adjusts the next learning content according to the student's level of understanding. The system according to feature 1.

3. To foster student autonomy, the school has a support department that assists with goal setting and maintaining motivation. The system according to feature 1.

4. It includes a shared section to share students' learning progress with teachers and propose necessary support. The system according to feature 1.

5. The generating unit is The system estimates students' emotions and adjusts the learning curriculum content based on those estimated emotions. The system according to feature 1.

6. The generating unit is Analyze students' past learning history and generate an optimal learning curriculum. The system according to feature 1.

7. The generating unit is When generating a learning curriculum, adjust the curriculum to take into account the students' current learning progress. The system according to feature 1.

8. The generating unit is The system estimates students' emotions and prioritizes the learning curriculum based on those estimated emotions. The system according to feature 1.

9. The generating unit is When generating the learning curriculum, the system prioritizes incorporating highly relevant content by considering students' geographical location information. The system according to feature 1.

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