system

Abstract

The system according to this embodiment aims to provide learning guidance optimized for each individual student. [Solution] The system according to the embodiment comprises a generation unit, a teaching unit, and a dialogue unit. The generation unit uses a generation AI to learn teaching materials and create a teaching program. The teaching unit provides instruction to students based on the teaching program created by the generation unit. The dialogue unit interacts with students using natural language processing technology in response to the instruction provided by the teaching unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide learning guidance optimized for individual students, and there is room for improvement.

[0005] The system according to the embodiment aims to provide learning guidance optimized for individual students.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a guidance unit, and a dialogue unit. The generation unit causes the generation AI to learn teaching materials and create a guidance program. The guidance unit conducts guidance for students based on the guidance program created by the generation unit. The dialogue unit uses natural language processing technology to interact with students with respect to the guidance performed by the guidance unit.

Effects of the Invention

[0007] The system according to this embodiment can provide learning guidance optimized for each individual student. [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 subscription service according to an embodiment of the present invention is a system that allows anyone to access the best teachers by combining an AI agent and a learning guidance program. This system comprises a generation unit in which a generation AI learns teaching materials and creates a guidance program, a guidance unit in which guidance is provided to students based on the guidance program created by the generation unit, and a dialogue unit in which natural language processing technology is used to interact with students regarding the guidance provided by the guidance unit. For example, the generation AI learns in advance teaching materials and guidance information not only for English, mathematics, Japanese language, science, and social studies, but also for interdisciplinary fields and specialized fields. Based on this information, the generation AI creates a professional educational guidance program. Next, the student's personality and past learning records and grades are registered, and counseling is conducted. As a result, the AI ​​agent provides personalized guidance to each student. The AI ​​agent selects the optimal teaching method based on the student's personality and learning records and provides guidance in real time. Furthermore, the AI ​​agent uses natural language processing technology to interact with students in order to achieve human-like communication. As a result, students can feel as if they are talking to a human teacher. This mechanism makes it possible to provide high-quality education, which was previously only accessible by paying high educational fees, at an affordable price. This enables long-term, continuous support, contributing to improved student performance. For example, in English classes, the AI ​​agent provides instruction tailored to each student's personality and learning record, based on materials pre-learned by the generative AI. If a student has difficulty understanding something, the AI ​​agent uses natural language processing technology to interact with the student and help deepen their understanding. In mathematics classes, the AI ​​agent presents and explains problems best suited to the student based on an educational guidance program created by the generative AI. If a student gets stuck while solving a problem, the AI ​​agent provides real-time support to help them solve it. By combining the AI ​​agent and the learning guidance program in this way, a subscription service is realized that allows anyone to have access to the best teacher. As a result, the subscription service can provide students with optimal instruction and improve learning effectiveness.

[0029] The subscription service according to this embodiment comprises a generation unit, an instruction unit, and a dialogue unit. The generation unit uses a generation AI to learn teaching materials and create instructional programs. For example, the generation unit uses a generation AI to learn teaching materials in subjects such as English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized fields. The generation AI uses technologies such as deep learning and reinforcement learning to learn these teaching materials and create professional educational instructional programs. The instruction unit provides instruction to students based on the instructional programs created by the generation unit. The instruction unit registers students' personalities and learning records and conducts counseling. For example, the instruction unit evaluates students' personalities based on the results of personality diagnostic tests and questionnaires, and registers their learning records. Based on the instructional programs created by the generation unit, the instruction unit presents and explains problems best suited to the students. For example, the instruction unit adjusts the difficulty level of the problems according to the students' level of understanding and provides appropriate instruction. The dialogue unit uses natural language processing technology to interact with students regarding the instruction provided by the instruction unit. The dialogue unit engages in conversation with students using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The dialogue unit also uses natural language processing techniques to support students in areas they find difficult to understand. For example, the dialogue unit identifies areas that students find difficult to understand and provides additional explanations or examples. This allows the subscription service, according to this embodiment, to provide students with optimal instruction and improve learning effectiveness.

[0030] The generation unit uses a generative AI to learn from teaching materials and create instructional programs. For example, the generative AI learns from teaching materials in subjects such as English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized disciplines. The generative AI uses technologies such as deep learning and reinforcement learning to learn from these materials and create professional educational instructional programs. Specifically, the generative AI receives a large amount of teaching material data as input and analyzes the content using natural language processing technology. For example, with English materials, it learns grammatical structures, vocabulary usage, and reading and listening skills; with mathematics materials, it learns formulas, theorems, and problem-solving processes. Based on this learning, the generative AI generates customized instructional programs tailored to each student's learning level and understanding. The generative AI improves the accuracy of its instructional programs by utilizing past learning data and student feedback. For example, it analyzes how students reacted to specific problems and where they struggled, and incorporates this into the next instructional program. This allows the generation unit to always provide high-quality instructional programs based on the latest information. Furthermore, the generation unit can update the content of its instructional programs by having the generation AI continuously learn new teaching materials and educational trends, thereby providing education that is relevant to the times.

[0031] The instruction department provides instruction to students based on the instructional programs created by the generation department. The instruction department registers students' personalities and learning records and conducts counseling. For example, the instruction department evaluates students' personalities based on the results of personality diagnostic tests and questionnaires, and registers their learning records. Based on the instructional programs created by the generation department, the instruction department presents and explains problems best suited to each student. Specifically, it customizes the teaching method according to the student's personality and learning style. For example, individual instruction or studying in a quiet environment is recommended for introverted students, while group discussions and active learning are incorporated for extroverted students. The instruction department adjusts the difficulty level of problems according to the student's level of understanding and provides appropriate instruction. For example, if a student shows a high level of understanding of a particular problem, the difficulty level of the next problem is increased to provide a challenging learning environment. On the other hand, if a student stumbles, the difficulty level of the problem is lowered, and instruction is repeated from the basic content. The instruction department regularly evaluates students' learning progress and revise the instructional program as needed. In this way, the instruction department can provide the optimal learning environment for each student and maximize learning effectiveness. Furthermore, the teaching staff will strive to improve instructional content by maintaining close communication with students and parents and collecting feedback on learning.

[0032] The Dialogue Department interacts with students using natural language processing technology in response to instruction provided by the Instruction Department. The Dialogue Department uses natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis to engage in dialogue with students. Specifically, it analyzes questions and answers submitted by students to understand their intent and level of comprehension. For example, if a student asks, "I don't understand this problem," the Dialogue Department analyzes the question and identifies which part is misunderstood. The Dialogue Department then uses natural language processing technology to interact with students to support them in areas where they find it difficult to understand. For example, if a student struggles with a particular math problem, the Dialogue Department provides an explanation of the problem and, if necessary, offers additional examples or hints. The Dialogue Department analyzes student responses in real time and provides appropriate feedback. For example, it checks whether the student understands the explanation and, if not, explains it using a different approach. The Dialogue Department works in conjunction with generative AI to provide customized dialogue based on the student's learning history and level of comprehension. This allows the Dialogue Department to provide personalized support to each student, improving learning effectiveness. Furthermore, the Dialogue Department accumulates student learning data and analyzes long-term learning trends and patterns to improve instructional content. This allows the Dialogue Department to consistently provide high-quality dialogue based on the latest information, supporting students' learning.

[0033] The generation unit can learn teaching materials in English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized fields. For example, the generation unit's generation AI learns teaching materials such as English grammar, calculus in mathematics, and physics in science. The generation unit's generation AI then creates professional educational instruction programs based on these teaching materials. The generation unit's generation AI uses technologies such as deep learning and reinforcement learning to learn teaching materials and create instruction programs. This allows the generation unit to handle a wide range of educational content by learning teaching materials in diverse fields. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit creates instruction programs based on the teaching materials learned by the generation AI.

[0034] The instruction department can register students' personalities and learning records and conduct counseling. For example, the instruction department can evaluate students' personalities based on the results of personality diagnostic tests and questionnaires, and register their learning records. Based on the instruction program created by the generation department, the instruction department can present and explain problems best suited to the student. For example, the instruction department can adjust the difficulty level of the problems according to the student's level of understanding and provide appropriate instruction. In this way, by registering students' personalities and learning records and conducting counseling, the instruction department can provide individually optimized instruction. Some or all of the above processes in the instruction department may be performed using AI, for example, or not using AI. For example, the instruction department can evaluate students' personalities based on the results of personality diagnostic tests and questionnaires, and register their learning records.

[0035] The dialogue unit can interact with students using natural language processing (NLP) techniques. The dialogue unit engages in dialogue with students using NLP techniques such as morphological analysis, grammatical analysis, and semantic analysis. The dialogue unit also uses NLP techniques to support students in areas they find difficult to understand. For example, the dialogue unit can identify areas students find difficult to understand and provide additional explanations or examples. This allows the dialogue unit to make interactions with students more natural and human-like by using NLP techniques. Some or all of the above-described processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit engages in dialogue with students using NLP techniques such as morphological analysis, grammatical analysis, and semantic analysis.

[0036] The instruction unit can present and explain problems best suited to students based on the instructional program created by the generation unit. For example, the instruction unit can adjust the difficulty level of the problems according to the students' level of understanding and provide appropriate instruction. The instruction unit presents and explains problems best suited to students based on the instructional program created by the generation unit. For example, the instruction unit can adjust the difficulty level of the problems according to the students' level of understanding and provide appropriate instruction. This allows the instruction unit to provide students with problems best suited to them and deepen their understanding by providing instruction based on the instructional program created by the generation unit. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit presents and explains problems best suited to students based on the instructional program created by the generation unit.

[0037] The dialogue unit can interact with students using natural language processing technology to support them in areas they find difficult to understand. For example, the dialogue unit can identify areas that students find difficult to understand and provide additional explanations or examples. The dialogue unit uses natural language processing technology to support students in areas they find difficult to understand. For example, the dialogue unit can identify areas that students find difficult to understand and provide additional explanations or examples. In this way, the dialogue unit can improve students' understanding by supporting them in areas they find difficult to understand. Some or all of the processing described above in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit uses natural language processing technology to support students in areas they find difficult to understand.

[0038] The generation unit can, when generating learning materials, refer to the student's past learning history to include content that reinforces individual weaknesses. For example, the generation unit uses a generating AI to generate learning materials that include similar problems based on problems the student has previously answered incorrectly. The generation unit uses a generating AI to generate learning materials that include many problems in areas where the student struggles, allowing them to focus their learning on those areas. The generation unit analyzes the student's past performance, and the generating AI generates learning materials that include content necessary for improving performance. In this way, the generation unit can provide learning materials that reinforce individual weaknesses by referring to the student's past learning history. Some or all of the above processes in the generation unit may be performed using a generating AI, for example, or without using a generating AI. For example, the generation unit refers to the student's past learning history to generate learning materials that include content that reinforces individual weaknesses.

[0039] The generation unit can update the content of the instructional program by incorporating the latest educational research findings when generating teaching materials. For example, the generation unit can generate teaching materials that incorporate new teaching methods based on the latest educational research findings using a generation AI. The generation unit can refer to announcements from educational research institutions and generate teaching materials that reflect those contents using a generation AI. The generation unit can incorporate the latest trends in pedagogy and generate teaching materials based on them using a generation AI. In this way, the generation unit can keep the content of the instructional program up-to-date by incorporating the latest educational research findings. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can generate teaching materials that incorporate new teaching methods based on the latest educational research findings using a generation AI.

[0040] The generation unit can include content corresponding to the regional educational curriculum when generating teaching materials. For example, the generation unit generates teaching materials appropriate for each region based on the regional educational curriculum. The generation unit reflects the regional educational policies, and the generation AI generates teaching materials in line with those policies. The generation unit refers to the teaching content of regional educational institutions, and the generation AI generates teaching materials that include that content. In this way, the generation unit enables education appropriate to each region by providing teaching materials that correspond to the regional educational curriculum. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit generates teaching materials appropriate for each region based on the regional educational curriculum.

[0041] The generation unit can create learning materials that cater to different learning styles (visual, auditory, and experiential) during the generation of materials. For example, the generation unit can use a generating AI to create materials that include many diagrams and graphs for visual learners. For auditory learners, the generation unit can use a generating AI to create materials that include audio explanations. For experiential learners, the generation unit can use a generating AI to create materials that include practical exercises. In this way, the generation unit can enhance students' learning effectiveness by providing materials that cater to different learning styles. Some or all of the above-described processes in the generation unit may be performed using a generating AI, for example, or without using a generating AI. For example, the generation unit can use a generating AI to create materials that include many diagrams and graphs for visual learners.

[0042] The instructional department can assess students' real-time understanding during instruction and immediately modify the instructional content. For example, if a student does not understand, the AI ​​will repeat the explanation. If the student understands, the AI ​​will move on to the next step. If the student only partially understands, the AI ​​will provide supplementary explanations. This allows the instructional department to provide effective instruction by changing the instructional content according to the student's level of understanding. Some or all of the above processes in the instructional department may be performed using AI, for example, or without AI. For example, the instructional department assesses students' real-time understanding during instruction and immediately modifies the instructional content.

[0043] The instructional unit can adjust the pace of instruction to match the student's learning pace. For example, if a student is learning slowly, the AI ​​will slow down the instructional pace. If a student is learning quickly, the AI ​​will speed up the instructional pace. If a student is learning at a moderate pace, the AI ​​will adjust the instructional pace. In this way, the instructional unit can provide the student with an optimal learning environment by adjusting the instructional pace to match the student's learning pace. Some or all of the above processing in the instructional unit may be performed using AI, for example, or without AI. For example, the instructional unit adjusts the pace of instruction to match the student's learning pace during instruction.

[0044] The instructional department can incorporate relevant topics based on students' interests and concerns during instruction. For example, the instructional department can use AI to adjust the instructional content based on topics that students are interested in. The instructional department can use AI to incorporate relevant topics based on students' interests. The instructional department can use AI to include relevant topics in the instructional content to engage students' interest. In this way, the instructional department can increase students' motivation to learn by adjusting the instructional content based on students' interests and concerns. Some or all of the above processes in the instructional department may be performed using AI, for example, or without AI. For example, the instructional department can incorporate relevant topics based on students' interests and concerns during instruction.

[0045] The instructional department can incorporate elements of group learning during instruction to promote cooperation among students. For example, the instructional department can use AI to adjust instructional content so that students work together to solve problems. The instructional department can use AI to promote cooperation among students by incorporating group discussions. The instructional department can use AI to adjust instructional methods so that students learn together. In this way, the instructional department can promote cooperation among students and enhance learning effectiveness by incorporating elements of group learning. Some or all of the above processes in the instructional department may be performed using AI, for example, or without AI. For example, the instructional department can use elements of group learning during instruction to promote cooperation among students.

[0046] The dialogue unit can provide individualized feedback by referring to the student's past dialogue history during a conversation. For example, the dialogue unit can provide individualized feedback based on the questions the student has asked in the past. The dialogue unit refers to the student's past dialogue history and the AI ​​provides feedback based on that content. The dialogue unit provides individualized feedback based on the instruction the student has received in the past. In this way, the dialogue unit can provide individualized feedback by referring to the student's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can provide individualized feedback by referring to the student's past dialogue history during a conversation.

[0047] The dialogue unit can provide additional explanations and examples during the dialogue, depending on the student's level of understanding. For example, if the student does not understand, the AI ​​will provide additional explanations. If the student partially understands, the AI ​​will provide specific examples. If the student understands, the AI ​​will proceed to the next step. In this way, the dialogue unit can deepen understanding by providing additional explanations and examples according to the student's level of understanding. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit provides additional explanations and examples during the dialogue, depending on the student's level of understanding.

[0048] The dialogue unit can provide dialogue content that corresponds to different languages ​​and cultures during a dialogue. For example, the AI ​​in the dialogue unit provides dialogue content based on the student's native language. The AI ​​in the dialogue unit provides appropriate dialogue content considering the student's cultural background. If the student uses a different language, the AI ​​in the dialogue unit provides dialogue content corresponding to that language. In this way, the dialogue unit can accommodate a wider variety of students by providing dialogue content that corresponds to different languages ​​and cultures. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit provides dialogue content that corresponds to different languages ​​and cultures during a dialogue.

[0049] The dialogue unit can supplement explanations using visual aids (diagrams and graphs) during dialogue. For example, the dialogue unit uses AI to supplement explanations using diagrams so that students can easily understand them. The dialogue unit uses AI to supplement explanations using graphs so that students can visually understand them. The dialogue unit uses AI to supplement explanations using visual aids so that students can understand them concretely. In this way, the dialogue unit can deepen students' understanding by using visual aids. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit uses visual aids (diagrams and graphs) to supplement explanations during dialogue.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can incorporate interactive elements tailored to students' learning styles when generating learning materials. For example, for visual learners, the generation AI can generate materials that include videos and animations. For auditory learners, the generation AI can generate materials in the form of audio guides or podcasts. For experiential learners, the generation AI can generate materials that utilize simulations or virtual reality. This allows the generation unit to enhance learning effectiveness by providing interactive learning materials that match students' learning styles.

[0052] The instruction department can monitor students' learning progress in real time and automatically adjust learning plans as needed. For example, if a student is ahead of schedule, the department can introduce the next learning material earlier. Conversely, if a student is falling behind, the department can revise the learning plan and provide remedial lessons or additional practice exercises. Furthermore, if a student is struggling in a particular area, the department can restructure the learning plan to focus on that area. This allows the department to provide flexible learning plans tailored to each student's progress.

[0053] The generation unit can incorporate the latest educational technologies when generating learning materials, thereby providing a more effective learning experience. For example, the generation AI can use adaptive learning technology to adjust the difficulty level of the materials according to the student's level of understanding. The generation AI can incorporate gamification elements to generate game-style learning materials that make learning fun. The generation AI can use virtual reality (VR) and augmented reality (AR) technologies to provide a learning environment that closely resembles real-world experiences. In this way, the generation unit can provide a more effective and engaging learning experience by incorporating the latest educational technologies.

[0054] The teaching staff can analyze students' learning history and predict their future learning plans. For example, they can analyze what kinds of problems students have struggled with in the past and take early action if similar problems arise. They can also analyze students' learning pace and use that information to create future learning plans. They can analyze students' performance trends and provide specific advice for improving their grades. As a result, the teaching staff can predict students' future learning plans based on their learning history and provide more effective instruction.

[0055] The generation unit can include content that accommodates students with different cultures and backgrounds when generating teaching materials. For example, the generation AI can generate teaching materials that incorporate elements of multicultural education. The generation AI can generate teaching materials in different languages, providing learning that transcends language barriers. The generation AI can generate teaching materials that take into account different cultural backgrounds, providing learning that respects cultural diversity. As a result, the generation unit can meet a wider range of learning needs by providing teaching materials that accommodate students with different cultures and backgrounds.

[0056] The generation unit can incorporate the latest educational technologies when generating learning materials, thereby providing a more effective learning experience. For example, the generation AI can use adaptive learning technology to adjust the difficulty level of the materials according to the student's level of understanding. The generation AI can incorporate gamification elements to generate game-style learning materials that make learning fun. The generation AI can use virtual reality (VR) and augmented reality (AR) technologies to provide a learning environment that closely resembles real-world experiences. In this way, the generation unit can provide a more effective and engaging learning experience by incorporating the latest educational technologies.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The generation unit uses a generation AI to learn from teaching materials and create instructional programs. The generation AI learns from teaching materials in English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized fields, and uses technologies such as deep learning and reinforcement learning to create professional educational instructional programs. Step 2: The instruction department provides instruction to students based on the instructional program created by the generation department. The instruction department registers students' personalities and learning records and conducts counseling. For example, it evaluates students' personalities based on the results of personality diagnostic tests and questionnaires, and registers their learning records. The instruction department presents students with the most suitable problems, provides explanations, adjusts the difficulty level of the problems according to the students' level of understanding, and provides appropriate instruction. Step 3: The Dialogue Team interacts with students using natural language processing techniques in response to instruction provided by the Instruction Team. The Dialogue Team uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis to interact with students. The Dialogue Team identifies areas that students find difficult to understand and supports their comprehension by providing additional explanations and examples.

[0059] (Example of form 2) The subscription service according to an embodiment of the present invention is a system that allows anyone to access the best teachers by combining an AI agent and a learning guidance program. This system comprises a generation unit in which a generation AI learns teaching materials and creates a guidance program, a guidance unit in which guidance is provided to students based on the guidance program created by the generation unit, and a dialogue unit in which natural language processing technology is used to interact with students regarding the guidance provided by the guidance unit. For example, the generation AI learns in advance teaching materials and guidance information not only for English, mathematics, Japanese language, science, and social studies, but also for interdisciplinary fields and specialized fields. Based on this information, the generation AI creates a professional educational guidance program. Next, the student's personality and past learning records and grades are registered, and counseling is conducted. As a result, the AI ​​agent provides personalized guidance to each student. The AI ​​agent selects the optimal teaching method based on the student's personality and learning records and provides guidance in real time. Furthermore, the AI ​​agent uses natural language processing technology to interact with students in order to achieve human-like communication. As a result, students can feel as if they are talking to a human teacher. This mechanism makes it possible to provide high-quality education, which was previously only accessible by paying high educational fees, at an affordable price. This enables long-term, continuous support, contributing to improved student performance. For example, in English classes, the AI ​​agent provides instruction tailored to each student's personality and learning record, based on materials pre-learned by the generative AI. If a student has difficulty understanding something, the AI ​​agent uses natural language processing technology to interact with the student and help deepen their understanding. In mathematics classes, the AI ​​agent presents and explains problems best suited to the student based on an educational guidance program created by the generative AI. If a student gets stuck while solving a problem, the AI ​​agent provides real-time support to help them solve it. By combining the AI ​​agent and the learning guidance program in this way, a subscription service is realized that allows anyone to have access to the best teacher. As a result, the subscription service can provide students with optimal instruction and improve learning effectiveness.

[0060] The subscription service according to this embodiment comprises a generation unit, an instruction unit, and a dialogue unit. The generation unit uses a generation AI to learn teaching materials and create instructional programs. For example, the generation unit uses a generation AI to learn teaching materials in subjects such as English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized fields. The generation AI uses technologies such as deep learning and reinforcement learning to learn these teaching materials and create professional educational instructional programs. The instruction unit provides instruction to students based on the instructional programs created by the generation unit. The instruction unit registers students' personalities and learning records and conducts counseling. For example, the instruction unit evaluates students' personalities based on the results of personality diagnostic tests and questionnaires, and registers their learning records. Based on the instructional programs created by the generation unit, the instruction unit presents and explains problems best suited to the students. For example, the instruction unit adjusts the difficulty level of the problems according to the students' level of understanding and provides appropriate instruction. The dialogue unit uses natural language processing technology to interact with students regarding the instruction provided by the instruction unit. The dialogue unit engages in conversation with students using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The dialogue unit also uses natural language processing techniques to support students in areas they find difficult to understand. For example, the dialogue unit identifies areas that students find difficult to understand and provides additional explanations or examples. This allows the subscription service, according to this embodiment, to provide students with optimal instruction and improve learning effectiveness.

[0061] The generation unit uses a generative AI to learn from teaching materials and create instructional programs. For example, the generative AI learns from teaching materials in subjects such as English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized disciplines. The generative AI uses technologies such as deep learning and reinforcement learning to learn from these materials and create professional educational instructional programs. Specifically, the generative AI receives a large amount of teaching material data as input and analyzes the content using natural language processing technology. For example, with English materials, it learns grammatical structures, vocabulary usage, and reading and listening skills; with mathematics materials, it learns formulas, theorems, and problem-solving processes. Based on this learning, the generative AI generates customized instructional programs tailored to each student's learning level and understanding. The generative AI improves the accuracy of its instructional programs by utilizing past learning data and student feedback. For example, it analyzes how students reacted to specific problems and where they struggled, and incorporates this into the next instructional program. This allows the generation unit to always provide high-quality instructional programs based on the latest information. Furthermore, the generation unit can update the content of its instructional programs by having the generation AI continuously learn new teaching materials and educational trends, thereby providing education that is relevant to the times.

[0062] The instruction department provides instruction to students based on the instructional programs created by the generation department. The instruction department registers students' personalities and learning records and conducts counseling. For example, the instruction department evaluates students' personalities based on the results of personality diagnostic tests and questionnaires, and registers their learning records. Based on the instructional programs created by the generation department, the instruction department presents and explains problems best suited to each student. Specifically, it customizes the teaching method according to the student's personality and learning style. For example, individual instruction or studying in a quiet environment is recommended for introverted students, while group discussions and active learning are incorporated for extroverted students. The instruction department adjusts the difficulty level of problems according to the student's level of understanding and provides appropriate instruction. For example, if a student shows a high level of understanding of a particular problem, the difficulty level of the next problem is increased to provide a challenging learning environment. On the other hand, if a student stumbles, the difficulty level of the problem is lowered, and instruction is repeated from the basic content. The instruction department regularly evaluates students' learning progress and revise the instructional program as needed. In this way, the instruction department can provide the optimal learning environment for each student and maximize learning effectiveness. Furthermore, the teaching staff will strive to improve instructional content by maintaining close communication with students and parents and collecting feedback on learning.

[0063] The Dialogue Department interacts with students using natural language processing technology in response to instruction provided by the Instruction Department. The Dialogue Department uses natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis to engage in dialogue with students. Specifically, it analyzes questions and answers submitted by students to understand their intent and level of comprehension. For example, if a student asks, "I don't understand this problem," the Dialogue Department analyzes the question and identifies which part is misunderstood. The Dialogue Department then uses natural language processing technology to interact with students to support them in areas where they find it difficult to understand. For example, if a student struggles with a particular math problem, the Dialogue Department provides an explanation of the problem and, if necessary, offers additional examples or hints. The Dialogue Department analyzes student responses in real time and provides appropriate feedback. For example, it checks whether the student understands the explanation and, if not, explains it using a different approach. The Dialogue Department works in conjunction with generative AI to provide customized dialogue based on the student's learning history and level of comprehension. This allows the Dialogue Department to provide personalized support to each student, improving learning effectiveness. Furthermore, the Dialogue Department accumulates student learning data and analyzes long-term learning trends and patterns to improve instructional content. This allows the Dialogue Department to consistently provide high-quality dialogue based on the latest information, supporting students' learning.

[0064] The generation unit can learn teaching materials in English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized fields. For example, the generation unit's generation AI learns teaching materials such as English grammar, calculus in mathematics, and physics in science. The generation unit's generation AI then creates professional educational instruction programs based on these teaching materials. The generation unit's generation AI uses technologies such as deep learning and reinforcement learning to learn teaching materials and create instruction programs. This allows the generation unit to handle a wide range of educational content by learning teaching materials in diverse fields. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit creates instruction programs based on the teaching materials learned by the generation AI.

[0065] The instruction department can register students' personalities and learning records and conduct counseling. For example, the instruction department can evaluate students' personalities based on the results of personality diagnostic tests and questionnaires, and register their learning records. Based on the instruction program created by the generation department, the instruction department can present and explain problems best suited to the student. For example, the instruction department can adjust the difficulty level of the problems according to the student's level of understanding and provide appropriate instruction. In this way, by registering students' personalities and learning records and conducting counseling, the instruction department can provide individually optimized instruction. Some or all of the above processes in the instruction department may be performed using AI, for example, or not using AI. For example, the instruction department can evaluate students' personalities based on the results of personality diagnostic tests and questionnaires, and register their learning records.

[0066] The dialogue unit can interact with students using natural language processing (NLP) techniques. The dialogue unit engages in dialogue with students using NLP techniques such as morphological analysis, grammatical analysis, and semantic analysis. The dialogue unit also uses NLP techniques to support students in areas they find difficult to understand. For example, the dialogue unit can identify areas students find difficult to understand and provide additional explanations or examples. This allows the dialogue unit to make interactions with students more natural and human-like by using NLP techniques. Some or all of the above-described processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit engages in dialogue with students using NLP techniques such as morphological analysis, grammatical analysis, and semantic analysis.

[0067] The instruction unit can present and explain problems best suited to students based on the instructional program created by the generation unit. For example, the instruction unit can adjust the difficulty level of the problems according to the students' level of understanding and provide appropriate instruction. The instruction unit presents and explains problems best suited to students based on the instructional program created by the generation unit. For example, the instruction unit can adjust the difficulty level of the problems according to the students' level of understanding and provide appropriate instruction. This allows the instruction unit to provide students with problems best suited to them and deepen their understanding by providing instruction based on the instructional program created by the generation unit. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit presents and explains problems best suited to students based on the instructional program created by the generation unit.

[0068] The dialogue unit can interact with students using natural language processing technology to support them in areas they find difficult to understand. For example, the dialogue unit can identify areas that students find difficult to understand and provide additional explanations or examples. The dialogue unit uses natural language processing technology to support students in areas they find difficult to understand. For example, the dialogue unit can identify areas that students find difficult to understand and provide additional explanations or examples. In this way, the dialogue unit can improve students' understanding by supporting them in areas they find difficult to understand. Some or all of the processing described above in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit uses natural language processing technology to support students in areas they find difficult to understand.

[0069] The generation unit can estimate students' emotions and adjust the difficulty level of the learning materials based on the estimated emotions. For example, if a student is stressed, the generation unit will generate learning materials that contain many easy problems. If a student is relaxed, the generation unit will generate learning materials that contain difficult problems. If a student is excited, the generation unit will generate learning materials that contain interesting problems. This makes it easier for the generation unit to maintain students' motivation to learn by adjusting the difficulty level of the learning materials based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit estimates students' emotions and adjusts the difficulty level of the learning materials based on the estimated emotions.

[0070] The generation unit can, when generating learning materials, refer to the student's past learning history to include content that reinforces individual weaknesses. For example, the generation unit uses a generating AI to generate learning materials that include similar problems based on problems the student has previously answered incorrectly. The generation unit uses a generating AI to generate learning materials that include many problems in areas where the student struggles, allowing them to focus their learning on those areas. The generation unit analyzes the student's past performance, and the generating AI generates learning materials that include content necessary for improving performance. In this way, the generation unit can provide learning materials that reinforce individual weaknesses by referring to the student's past learning history. Some or all of the above processes in the generation unit may be performed using a generating AI, for example, or without using a generating AI. For example, the generation unit refers to the student's past learning history to generate learning materials that include content that reinforces individual weaknesses.

[0071] The generation unit can update the content of the instructional program by incorporating the latest educational research findings when generating teaching materials. For example, the generation unit can generate teaching materials that incorporate new teaching methods based on the latest educational research findings using a generation AI. The generation unit can refer to announcements from educational research institutions and generate teaching materials that reflect those contents using a generation AI. The generation unit can incorporate the latest trends in pedagogy and generate teaching materials based on them using a generation AI. In this way, the generation unit can keep the content of the instructional program up-to-date by incorporating the latest educational research findings. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can generate teaching materials that incorporate new teaching methods based on the latest educational research findings using a generation AI.

[0072] The generation unit can estimate the student's emotions and adjust the order in which the learning materials are presented based on the estimated emotions. For example, if the student is stressed, the generation unit adjusts the order so that the generating AI starts with easy problems. If the student is relaxed, the generation unit adjusts the order so that the generating AI presents more difficult problems first. If the student is excited, the generation unit adjusts the order so that the generating AI presents interesting problems first. In this way, the generation unit can improve the student's learning efficiency by adjusting the order in which the learning materials are presented based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generating AI, for example, or without a generating AI. For example, the generation unit estimates the student's emotions and adjusts the order in which the learning materials are presented based on the estimated emotions.

[0073] The generation unit can include content corresponding to the regional educational curriculum when generating teaching materials. For example, the generation unit generates teaching materials appropriate for each region based on the regional educational curriculum. The generation unit reflects the regional educational policies, and the generation AI generates teaching materials in line with those policies. The generation unit refers to the teaching content of regional educational institutions, and the generation AI generates teaching materials that include that content. In this way, the generation unit enables education appropriate to each region by providing teaching materials that correspond to the regional educational curriculum. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit generates teaching materials appropriate for each region based on the regional educational curriculum.

[0074] The generation unit can create learning materials that cater to different learning styles (visual, auditory, and experiential) during the generation of materials. For example, the generation unit can use a generating AI to create materials that include many diagrams and graphs for visual learners. For auditory learners, the generation unit can use a generating AI to create materials that include audio explanations. For experiential learners, the generation unit can use a generating AI to create materials that include practical exercises. In this way, the generation unit can enhance students' learning effectiveness by providing materials that cater to different learning styles. Some or all of the above-described processes in the generation unit may be performed using a generating AI, for example, or without using a generating AI. For example, the generation unit can use a generating AI to create materials that include many diagrams and graphs for visual learners.

[0075] The instruction department can estimate students' emotions and adjust its teaching methods based on those estimates. For example, if a student is stressed, the AI ​​can provide instruction in a gentle tone. If a student is relaxed, the AI ​​can provide detailed explanations. If a student is agitated, the AI ​​can provide instruction at a brisk pace. This allows the instruction department to provide optimal instruction for each student by adjusting its teaching methods based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction department may be performed using AI or not. For example, the instruction department estimates students' emotions and adjusts its teaching methods based on those estimates.

[0076] The instructional department can assess students' real-time understanding during instruction and immediately modify the instructional content. For example, if a student does not understand, the AI ​​will repeat the explanation. If the student understands, the AI ​​will move on to the next step. If the student only partially understands, the AI ​​will provide supplementary explanations. This allows the instructional department to provide effective instruction by changing the instructional content according to the student's level of understanding. Some or all of the above processes in the instructional department may be performed using AI, for example, or without AI. For example, the instructional department assesses students' real-time understanding during instruction and immediately modifies the instructional content.

[0077] The instructional unit can adjust the pace of instruction to match the student's learning pace. For example, if a student is learning slowly, the AI ​​will slow down the instructional pace. If a student is learning quickly, the AI ​​will speed up the instructional pace. If a student is learning at a moderate pace, the AI ​​will adjust the instructional pace. In this way, the instructional unit can provide the student with an optimal learning environment by adjusting the instructional pace to match the student's learning pace. Some or all of the above processing in the instructional unit may be performed using AI, for example, or without AI. For example, the instructional unit adjusts the pace of instruction to match the student's learning pace during instruction.

[0078] The instruction department can estimate students' emotions and adjust the timing of instruction based on those estimates. For example, if a student is stressed, the instruction department can delay the instruction using AI. If a student is relaxed, the instruction department can advance the instruction using AI. If a student is excited, the instruction department can adjust the timing of instruction using AI. This allows the instruction department to provide effective instruction by adjusting the timing of instruction based on students' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction department may be performed using AI or not using AI. For example, the instruction department estimates students' emotions and adjusts the timing of instruction based on those estimates.

[0079] The instructional department can incorporate relevant topics based on students' interests and concerns during instruction. For example, the instructional department can use AI to adjust the instructional content based on topics that students are interested in. The instructional department can use AI to incorporate relevant topics based on students' interests. The instructional department can use AI to include relevant topics in the instructional content to engage students' interest. In this way, the instructional department can increase students' motivation to learn by adjusting the instructional content based on students' interests and concerns. Some or all of the above processes in the instructional department may be performed using AI, for example, or without AI. For example, the instructional department can incorporate relevant topics based on students' interests and concerns during instruction.

[0080] The instructional department can incorporate elements of group learning during instruction to promote cooperation among students. For example, the instructional department can use AI to adjust instructional content so that students work together to solve problems. The instructional department can use AI to promote cooperation among students by incorporating group discussions. The instructional department can use AI to adjust instructional methods so that students learn together. In this way, the instructional department can promote cooperation among students and enhance learning effectiveness by incorporating elements of group learning. Some or all of the above processes in the instructional department may be performed using AI, for example, or without AI. For example, the instructional department can use elements of group learning during instruction to promote cooperation among students.

[0081] The dialogue unit can estimate the student's emotions and adjust the tone and expression of the dialogue based on the estimated emotions. For example, if the student is stressed, the AI ​​will engage in dialogue in a gentle tone. If the student is relaxed, the AI ​​will engage in dialogue in a friendly tone. If the student is excited, the AI ​​will engage in dialogue in an energetic tone. This allows the dialogue unit to engage in more appropriate dialogue by adjusting the tone and expression of the dialogue based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not using AI. For example, the dialogue unit estimates the student's emotions and adjusts the tone and expression of the dialogue based on the estimated emotions.

[0082] The dialogue unit can provide individualized feedback by referring to the student's past dialogue history during a conversation. For example, the dialogue unit can provide individualized feedback based on the questions the student has asked in the past. The dialogue unit refers to the student's past dialogue history and the AI ​​provides feedback based on that content. The dialogue unit provides individualized feedback based on the instruction the student has received in the past. In this way, the dialogue unit can provide individualized feedback by referring to the student's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can provide individualized feedback by referring to the student's past dialogue history during a conversation.

[0083] The dialogue unit can provide additional explanations and examples during the dialogue, depending on the student's level of understanding. For example, if the student does not understand, the AI ​​will provide additional explanations. If the student partially understands, the AI ​​will provide specific examples. If the student understands, the AI ​​will proceed to the next step. In this way, the dialogue unit can deepen understanding by providing additional explanations and examples according to the student's level of understanding. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit provides additional explanations and examples during the dialogue, depending on the student's level of understanding.

[0084] The dialogue unit can estimate the student's emotions and adjust the frequency of dialogue based on the estimated emotions. For example, if the student is stressed, the AI ​​will reduce the frequency of dialogue. If the student is relaxed, the AI ​​will increase the frequency of dialogue. If the student is excited, the AI ​​will adjust the frequency of dialogue. This allows the dialogue unit to have more appropriate conversations by adjusting the frequency of dialogue based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not using AI. For example, the dialogue unit estimates the student's emotions and adjusts the frequency of dialogue based on the estimated emotions.

[0085] The dialogue unit can provide dialogue content that corresponds to different languages ​​and cultures during a dialogue. For example, the AI ​​in the dialogue unit provides dialogue content based on the student's native language. The AI ​​in the dialogue unit provides appropriate dialogue content considering the student's cultural background. If the student uses a different language, the AI ​​in the dialogue unit provides dialogue content corresponding to that language. In this way, the dialogue unit can accommodate a wider variety of students by providing dialogue content that corresponds to different languages ​​and cultures. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit provides dialogue content that corresponds to different languages ​​and cultures during a dialogue.

[0086] The dialogue unit can supplement explanations using visual aids (diagrams and graphs) during dialogue. For example, the dialogue unit uses AI to supplement explanations using diagrams so that students can easily understand them. The dialogue unit uses AI to supplement explanations using graphs so that students can visually understand them. The dialogue unit uses AI to supplement explanations using visual aids so that students can understand them concretely. In this way, the dialogue unit can deepen students' understanding by using visual aids. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit uses visual aids (diagrams and graphs) to supplement explanations during dialogue.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The generation unit can incorporate interactive elements tailored to students' learning styles when generating learning materials. For example, for visual learners, the generation AI can generate materials that include videos and animations. For auditory learners, the generation AI can generate materials in the form of audio guides or podcasts. For experiential learners, the generation AI can generate materials that utilize simulations or virtual reality. This allows the generation unit to enhance learning effectiveness by providing interactive learning materials that match students' learning styles.

[0089] The instruction department can monitor students' learning progress in real time and automatically adjust learning plans as needed. For example, if a student is ahead of schedule, the department can introduce the next learning material earlier. Conversely, if a student is falling behind, the department can revise the learning plan and provide remedial lessons or additional practice exercises. Furthermore, if a student is struggling in a particular area, the department can restructure the learning plan to focus on that area. This allows the department to provide flexible learning plans tailored to each student's progress.

[0090] The dialogue unit can estimate students' emotions and personalize the content of the dialogue based on those estimates. For example, if a student is feeling anxious, the dialogue unit can offer words of encouragement and reassurance. If a student is confident, the dialogue unit can present challenging questions or high-difficulty problems. If a student is tired, the dialogue unit can suggest light topics or breaks to help them relax. In this way, the dialogue unit can help maintain students' motivation to learn by providing appropriate dialogue tailored to their emotions.

[0091] The generation unit can incorporate the latest educational technologies when generating learning materials, thereby providing a more effective learning experience. For example, the generation AI can use adaptive learning technology to adjust the difficulty level of the materials according to the student's level of understanding. The generation AI can incorporate gamification elements to generate game-style learning materials that make learning fun. The generation AI can use virtual reality (VR) and augmented reality (AR) technologies to provide a learning environment that closely resembles real-world experiences. In this way, the generation unit can provide a more effective and engaging learning experience by incorporating the latest educational technologies.

[0092] The teaching staff can analyze students' learning history and predict their future learning plans. For example, they can analyze what kinds of problems students have struggled with in the past and take early action if similar problems arise. They can also analyze students' learning pace and use that information to create future learning plans. They can analyze students' performance trends and provide specific advice for improving their grades. As a result, the teaching staff can predict students' future learning plans based on their learning history and provide more effective instruction.

[0093] The dialogue unit can estimate a student's emotions and adjust the content of the dialogue based on those emotions. For example, if a student is feeling down, the dialogue unit can offer words of encouragement or positive feedback. If a student is excited, the dialogue unit can offer advice to calm down or suggest ways to relax. If a student is not concentrating, the dialogue unit can suggest activities or breaks to improve their concentration. In this way, the dialogue unit can enhance learning effectiveness by conducting appropriate dialogues that are tailored to the student's emotions.

[0094] The generation unit can include content that accommodates students with different cultures and backgrounds when generating teaching materials. For example, the generation AI can generate teaching materials that incorporate elements of multicultural education. The generation AI can generate teaching materials in different languages, providing learning that transcends language barriers. The generation AI can generate teaching materials that take into account different cultural backgrounds, providing learning that respects cultural diversity. As a result, the generation unit can meet a wider range of learning needs by providing teaching materials that accommodate students with different cultures and backgrounds.

[0095] The instructional staff can estimate students' emotions and adjust the pace of instruction based on those estimates. For example, if a student is stressed, the instructional staff can slow down the pace of instruction. If a student is relaxed, the instructional staff can speed up the pace of instruction. If a student is excited, the instructional staff can adjust the pace of instruction. This allows the instructional staff to enhance learning effectiveness by providing an appropriate pace of instruction that matches the students' emotions.

[0096] The dialogue unit can estimate students' emotions and personalize the content of the dialogue based on those estimates. For example, if a student is feeling anxious, the dialogue unit can offer words of encouragement and reassurance. If a student is confident, the dialogue unit can present challenging questions or high-difficulty problems. If a student is tired, the dialogue unit can suggest light topics or breaks to help them relax. In this way, the dialogue unit can help maintain students' motivation to learn by providing appropriate dialogue tailored to their emotions.

[0097] The generation unit can incorporate the latest educational technologies when generating learning materials, thereby providing a more effective learning experience. For example, the generation AI can use adaptive learning technology to adjust the difficulty level of the materials according to the student's level of understanding. The generation AI can incorporate gamification elements to generate game-style learning materials that make learning fun. The generation AI can use virtual reality (VR) and augmented reality (AR) technologies to provide a learning environment that closely resembles real-world experiences. In this way, the generation unit can provide a more effective and engaging learning experience by incorporating the latest educational technologies.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The generation unit uses a generation AI to learn from teaching materials and create instructional programs. The generation AI learns from teaching materials in English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized fields, and uses technologies such as deep learning and reinforcement learning to create professional educational instructional programs. Step 2: The instruction department provides instruction to students based on the instructional program created by the generation department. The instruction department registers students' personalities and learning records and conducts counseling. For example, it evaluates students' personalities based on the results of personality diagnostic tests and questionnaires, and registers their learning records. The instruction department presents students with the most suitable problems, provides explanations, adjusts the difficulty level of the problems according to the students' level of understanding, and provides appropriate instruction. Step 3: The Dialogue Team interacts with students using natural language processing techniques in response to instruction provided by the Instruction Team. The Dialogue Team uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis to interact with students. The Dialogue Team identifies areas that students find difficult to understand and supports their comprehension by providing additional explanations and examples.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the generation unit, instruction unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generation AI learns teaching materials and creates instruction programs. The instruction unit is implemented by the control unit 46A of the smart device 14, where it provides optimal instruction to students. The dialogue unit is implemented by the control unit 46A of the smart device 14, where it interacts with students using natural language processing technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the generation unit, instruction unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generation AI learns the teaching materials and creates an instruction program. The instruction unit is implemented by the control unit 46A of the smart glasses 214, where it provides optimal instruction to the student. The dialogue unit is implemented by the control unit 46A of the smart glasses 214, where it interacts with the student using natural language processing technology. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the generation unit, instruction unit, and dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generation AI learns teaching materials and creates instruction programs. The instruction unit is implemented by the control unit 46A of the headset terminal 314, where it provides optimal instruction to students. The dialogue unit is implemented by the control unit 46A of the headset terminal 314, where it interacts with students using natural language processing technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In 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.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 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.

[0152] Each of the multiple elements described above, including the generation unit, instruction unit, and dialogue unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generation AI learns teaching materials and creates instructional programs. The instruction unit is implemented by the control unit 46A of the robot 414, where it provides optimal instruction to students. The dialogue unit is implemented by the control unit 46A of the robot 414, where it interacts with students using natural language processing technology. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) The generation unit uses a generation AI to learn teaching materials and create instructional programs, An instruction unit that provides instruction to students based on the instruction program created by the generation unit, The system includes a dialogue unit that uses natural language processing technology to interact with students in response to the instruction provided by the aforementioned instruction unit. A system characterized by the following features. (Note 2) The generating unit is Students will study materials in English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized subjects. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned leadership, Register students' personalities and learning records, and conduct counseling sessions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned dialogue unit, Interact with students using natural language processing technology. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned leadership, Based on the instructional program created by the generation unit, the system presents and explains problems best suited to each student. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, To support students in areas they find difficult to understand, we use natural language processing technology to interact with them. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The system estimates students' emotions and adjusts the difficulty level of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When generating learning materials, refer to students' past learning history to include content that addresses their individual weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When creating teaching materials, we incorporate the latest educational research findings and update the content of our instructional programs. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates students' emotions and adjusts the order in which teaching materials are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating teaching materials, include content that corresponds to the educational curriculum of each region. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating learning materials, create materials that accommodate different learning styles. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned leadership, The system estimates students' emotions and adjusts teaching methods based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned leadership, During instruction, the system assesses students' real-time understanding and immediately modifies the instruction content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned leadership, During instruction, adjust the pace of instruction to match the student's learning pace. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned leadership, The system estimates the students' emotions and adjusts the timing of instruction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned leadership, When teaching, incorporate relevant topics based on students' interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned leadership, During instruction, incorporate elements of group learning to promote cooperation among students. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dialogue unit, The system estimates the students' emotions and adjusts the tone and expression of the dialogue based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned dialogue unit, During the conversation, refer to the student's past conversation history to provide individualized feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned dialogue unit, During the discussion, provide additional explanations and examples according to the students' level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned dialogue unit, The system estimates the students' emotions and adjusts the frequency of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, During conversations, provide dialogue content that is compatible with different languages ​​and cultures. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, Use visual aids to reinforce explanations during dialogue. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The generation unit uses a generation AI to learn from teaching materials and create instructional programs, An instruction unit that provides instruction to students based on the instruction program created by the generation unit, The system includes a dialogue unit that uses natural language processing technology to interact with students in response to the instruction provided by the aforementioned instruction unit. A system characterized by the following features.

2. The generating unit is Students will study materials in English, mathematics, Japanese language, science, social studies, interdisciplinary fields, and specialized subjects. The system according to feature 1.

3. The aforementioned leadership, Register students' personalities and learning records, and conduct counseling sessions. The system according to feature 1.

4. The aforementioned dialogue unit, Interact with students using natural language processing technology. The system according to feature 1.

5. The aforementioned leadership, Based on the instructional program created by the aforementioned generation unit, the unit presents and explains problems best suited to each student. The system according to feature 1.

6. The aforementioned dialogue unit, To support students in areas they find difficult to understand, we use natural language processing technology to interact with them. The system according to feature 1.

7. The generating unit is The system estimates students' emotions and adjusts the difficulty level of the learning materials based on those estimated emotions. The system according to feature 1.

8. The generating unit is When generating learning materials, refer to students' past learning history to include content that addresses their individual weaknesses. The system according to feature 1.

Images