System

Abstract

An object of the system according to the embodiment is to provide individual guidance at low cost and to eliminate a difference in education.SOLUTION: In one embodiment, a system includes an educational content analyzer, a tutorial provider, and an online platform. The educational content analysis unit analyzes the educational content. The individual guidance providing unit provides individual guidance based on the educational content analyzed by the educational content analysis unit. The online platform provides the tutorials provided by the tutorial provider through the online platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult to eliminate educational disparities at low cost, and there have been challenges such as limited opportunities to receive individualized instruction.

[0005] The system according to the embodiment aims to provide individualized instruction at low cost and eliminate educational disparities. [Means for solving the problem]

[0006] A system according to an embodiment includes an educational content analysis unit, a tutoring provider, and an online platform. The educational content analysis unit analyzes educational content. The tutoring provider provides tutoring based on the educational content analyzed by the educational content analysis unit. The online platform provides the tutoring provided by the tutoring provider through the online platform. [Effects of the Invention]

[0007] The system according to the embodiment can provide individualized instruction at low cost and eliminate educational disparities. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The education support system according to the embodiment of the present invention is a system that uses a low-cost teacher AI to eliminate educational disparities and provide individualized instruction. This enables the education support system to fill educational gaps caused by poverty and provide individualized instruction tailored to each individual.

[0029] An educational support system according to an embodiment includes a teacher AI, an educational content analysis unit, an individualized instruction provision unit, and an online platform. The teacher AI includes an educational content analysis unit that analyzes educational content. For example, the educational content analysis unit analyzes the contents of textbooks and reference books, extracts important points, and provides explanations. The educational content analysis unit can also analyze students' learning histories and pose questions based on their individual levels of understanding. The educational content analysis unit can also automatically incorporate the latest research papers and educational theories to constantly update teaching methods. The individualized instruction provision unit provides individualized instruction based on the educational content analyzed by the educational content analysis unit. For example, the individualized instruction provision unit can monitor students' learning progress in real time and provide feedback based on their level of understanding. The individualized instruction provision unit can also analyze students' learning histories, predict their future learning progress, and propose optimal learning plans. The individualized instruction provision unit can also use an emotion estimation function to generate personalized messages to maintain students' motivation. The online platform provides the individualized instruction provided by the individualized instruction provision unit through the online platform. For example, the online platform automatically generates educational content that can be used in offline environments to enable learning even in areas with unstable internet connections. The online platform can also analyze students' schedules and suggest optimal study times. The online platform can also add a function that uses an emotion estimation function to suggest breaks when students' concentration wanes. This enables the education support system according to the embodiment to eliminate educational disparities and provide individualized instruction using a low-cost teacher AI. For example, the generation AI analyzes educational content, provides individualized instruction, and provides learning through an online platform. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the educational content and provide individualized instruction. The generation AI provides individualized instruction based on the analysis results of the educational content and provides learning through an online platform.

[0030] The educational content analysis unit can analyze the contents of textbooks or reference books, extract and explain important points. For example, the generation AI in the educational content analysis unit analyzes the contents of textbooks or reference books, extracts and explains important points. For example, the generation AI analyzes the contents of textbooks, extracts and explains important points. The generation AI also analyzes the contents of reference books, extracts and explains important points. The generation AI also analyzes the contents of textbooks or reference books, extracts and explains important points. This enables efficient learning by extracting and explaining important points of educational content.

[0031] The individual instruction providing unit can analyze the student's learning history and pose questions according to their level of understanding. In the individual instruction providing unit, for example, the generation AI analyzes the student's learning history and poses questions according to their level of understanding. For example, the generation AI analyzes the student's learning history and poses questions according to their level of understanding. Also, the generation AI analyzes the student's learning history and poses questions according to their level of understanding. Also, the generation AI analyzes the student's learning history and poses questions according to their level of understanding. In this way, by posing questions according to the student's level of understanding, the quality of individual instruction is improved.

[0032] The individualized instruction providing unit can monitor the student's learning progress in real time and provide feedback according to the level of understanding. In the individualized instruction providing unit, for example, the generation AI monitors the student's learning progress in real time and provides feedback according to the level of understanding. For example, the generation AI monitors the student's learning progress in real time and provides feedback according to the level of understanding. Also, the generation AI monitors the student's learning progress in real time and provides feedback according to the level of understanding. Also, the generation AI monitors the student's learning progress in real time and provides feedback according to the level of understanding. In this way, the learning effectiveness is improved by monitoring the student's learning progress in real time and providing appropriate feedback.

[0033] Teacher AI can develop special education programs for students with specific learning disabilities. For example, a generative AI could analyze the needs of students with specific learning disabilities and automatically generate a special education program that meets those needs. For example, it could create a program for students with dyslexia (a reading disability). Generative AI could also collect learning data from students with learning disabilities and provide customized educational programs based on that data. For example, it could provide programs that include visual materials and audio commentary. Generative AI could also monitor the progress of students with learning disabilities and adjust the program as needed. For example, it could provide additional support for specific tasks. This improves the quality of learning by developing special education programs for students with specific learning disabilities.

[0034] Teacher AI can also be applied to corporate training and vocational training programs to provide skills education that meets the needs of the labor market. For example, teacher AI uses generative AI to analyze a company's needs and automatically generate training programs that meet them. For example, it could provide training on new technologies or business processes. Generative AI can also analyze labor market trends and design vocational training programs. For example, it could provide programs specialized in skills and qualifications that are in high demand. Generative AI can also monitor the progress of corporate training and vocational training and provide feedback in real time. For example, it could provide additional learning materials or practice questions based on the trainee's level of understanding. This makes it possible to apply this technology to corporate training and vocational training programs to provide skills education that meets the needs of the labor market.

[0035] The educational content analysis unit automatically incorporates the latest research papers and educational theories, allowing teaching methods to be constantly updated. In this case, for example, the generative AI automatically collects the latest educational research papers, analyzes their contents, and reflects them in teaching methods. For example, it incorporates new educational theories and effective teaching techniques. The generative AI also builds a system that updates teaching methods in line with advances in educational theory. For example, it regularly reviews educational theories and improves teaching methods. When analyzing educational content, the generative AI also optimizes teaching methods based on the latest research results. For example, it suggests effective learning methods and activities. This allows teaching methods to be constantly optimized by incorporating the latest research papers and educational theories.

[0036] The educational content analysis unit can analyze a student's learning style and provide the optimal teaching method accordingly. In the educational content analysis unit, for example, a generative AI analyzes a student's learning style and provides the appropriate teaching method accordingly. For example, a visual learner is provided with teaching materials that make extensive use of diagrams and graphs. The generative AI also generates customized educational content based on the student's learning style. For example, an auditory learner is provided with teaching materials that include audio commentary. The generative AI also monitors a student's learning style in real time and suggests the optimal teaching method. For example, a tactile learner is provided with hands-on activities. This improves learning effectiveness by providing teaching methods that suit the student's learning style.

[0037] The educational content analysis unit can automatically generate multilingual educational content that corresponds to different languages ​​and cultural areas. The educational content analysis unit, for example, builds a system in which a generation AI automatically generates educational content that corresponds to different languages ​​and cultural areas. For example, it provides teaching materials that correspond to multiple languages, such as English, French, and Chinese. The generation AI also automatically creates multilingual educational content and provides it to students from different cultural areas. For example, it provides teaching materials that take cultural backgrounds into consideration. The generation AI also generates multilingual educational content in real time and adapts it to students from different languages ​​and cultural areas. For example, it provides education that transcends language barriers. This allows for the provision of multilingual educational content that corresponds to different languages ​​and cultural areas, thereby realizing global education.

[0038] The educational content analysis unit can also analyze educational content in non-academic fields such as sports and arts and provide teaching methods. For example, the educational content analysis unit uses a generative AI to analyze educational content in non-academic fields such as sports and arts and provide optimal teaching methods. For example, it supports technical sports instruction and artistic creative activities. The generative AI also automatically analyzes educational content in non-academic fields and provides customized teaching methods. For example, it provides music and art lessons. The generative AI also analyzes educational content in non-academic fields in real time and optimizes teaching methods. For example, it proposes sports training plans and artistic creative processes. In this way, comprehensive education is realized by analyzing educational content in non-academic fields such as sports and arts and providing teaching methods.

[0039] The individualized instruction providing unit can analyze the student's learning history, predict future learning progress, and propose an optimal learning plan. The individualized instruction providing unit, for example, builds a system in which a generation AI analyzes the student's learning history and predicts future learning progress. For example, it proposes an optimal learning plan based on past learning data. The generation AI also predicts future learning progress based on the student's learning history and provides a customized learning plan. For example, it proposes a plan that focuses on specific subjects or skills. The generation AI also analyzes the student's learning history in real time, predicts future learning progress, and proposes an optimal learning plan. For example, it adjusts the plan according to learning progress. In this way, by analyzing the student's learning history, predicting future learning progress, and proposing an optimal learning plan, learning effectiveness is improved.

[0040] The individualized instruction providing unit can compare a student's learning data with other students and provide feedback on their relative learning status. The individualized instruction providing unit, for example, builds a system in which a generating AI compares a student's learning data with other students and provides feedback on their relative learning status. For example, it evaluates learning progress by comparing with students in the same grade or class. The generating AI also provides comparison results with other students based on the student's learning data and provides feedback on their relative learning status. For example, it shows the student's relative position in a specific subject or skill. The generating AI also analyzes the student's learning data in real time and provides feedback on the comparison results with other students. For example, it compares learning progress and level of understanding with other students and evaluates them. In this way, by comparing the student's learning data with other students and providing feedback on their relative learning status, motivation to learn is improved.

[0041] The individualized instruction provision unit can analyze students' learning data and propose optimal combinations for group learning and pair learning. The individualized instruction provision unit, for example, builds a system in which a generation AI analyzes students' learning data and proposes optimal combinations for group learning and pair learning. For example, it determines combinations based on learning style and level of comprehension. The generation AI also proposes optimal groups and pairs based on students' learning history to support effective learning. For example, it pairs students with complementary skills. The generation AI also analyzes students' learning data in real time and proposes optimal combinations for group learning and pair learning. For example, it adjusts combinations according to learning progress and level of comprehension. In this way, learning effectiveness is improved by analyzing students' learning data and proposing optimal combinations for group learning and pair learning.

[0042] The individual instruction provision department develops support tools for private tutors and cram school instructors, thereby improving the quality of individual instruction. For example, the individual instruction provision department builds a system in which the generative AI develops support tools for private tutors and cram school instructors, thereby improving the quality of individual instruction. For example, the generative AI suggests optimal teaching methods based on students' learning data. In addition, private tutors and cram school instructors can use the generative AI to monitor students' learning progress in real time and provide effective feedback. For example, the generative AI provides additional teaching materials and practice problems according to the level of understanding. In addition, the generative AI provides customized teaching plans for private tutors and cram school instructors, thereby improving the quality of individual instruction. For example, it suggests teaching methods according to the student's learning style and needs. In this way, the quality of individual instruction is improved by developing support tools for private tutors and cram school instructors.

[0043] Online platforms can automatically generate educational content that can be used in offline environments, enabling learning even in areas with unstable internet connections. For example, online platforms build systems in which generative AI automatically generates educational content that can be used in offline environments. For example, it provides downloadable learning materials and apps. The generative AI also provides educational programs that operate offline to enable learning in areas with unstable internet connections. For example, it uses learning materials stored on local devices. The generative AI also generates educational content that can be used offline in real time and provides it to students in areas with unstable internet connections. For example, it records offline learning progress and synchronizes it when the connection is restored. This allows learning to be done even in areas with unstable internet connections by providing educational content that can be used in offline environments.

[0044] The online platform can analyze students' schedules and suggest optimal study times. The online platform, for example, builds a system in which a generative AI analyzes students' schedules and suggests optimal study times. For example, it adjusts study times to match school and club activity schedules. The generative AI also suggests optimal study times based on students' schedule data, supporting effective learning. For example, it recommends studying during times when students are most likely to concentrate. The generative AI also analyzes students' schedules in real time and suggests optimal study times. For example, it adjusts study times according to their learning progress and level of understanding. In this way, analyzing students' schedules and suggesting optimal study times improves learning effectiveness.

[0045] Online platforms can develop educational apps for mobile devices or wearable devices. For example, online platforms use generative AI to develop educational apps for mobile devices, enabling learning anytime, anywhere. For example, they provide apps that can be used on smartphones and tablets. Generative AI also develops educational apps for wearable devices, improving learning convenience. For example, they provide apps that can be used on smartwatches and fitness trackers. Generative AI also develops educational apps for mobile devices and wearable devices in real time, increasing learning flexibility. For example, they provide apps that can be used offline. This improves learning convenience by developing educational apps for mobile devices and wearable devices.

[0046] Online platforms can build online learning communities that connect students in remote locations. For example, generative AI can build online learning communities that connect students in remote locations, promoting the sharing and collaboration of learning. For example, it can provide video chat and forum functions. Generative AI can also develop platforms that allow students in remote locations to share their learning online. For example, it can provide tools for working on projects together. Generative AI can also build online learning communities that connect students in remote locations in real time, promoting mutual support in learning. For example, it can provide a function for sharing learning progress. In this way, building online learning communities that connect students in remote locations promotes the sharing and collaboration of learning.

[0047] Teacher AI can provide free or low-cost educational programs tailored to economically disadvantaged families. For example, Teacher AI builds a system in which Generator AI provides free or low-cost educational programs tailored to economically disadvantaged families. For example, it provides free online teaching materials and lessons. Generator AI also designs low-cost educational programs so that students from economically disadvantaged families can receive a high-quality education. For example, it provides programs using sponsorships and donations. Generator AI also provides educational programs tailored to economically disadvantaged families in real time, thereby eliminating educational disparities. For example, it provides online courses that can be used at low cost. This eliminates educational disparities by providing free or low-cost educational programs tailored to economically disadvantaged families.

[0048] The teaching AI can analyze regional educational disparity data and provide special educational support to areas that need it most. For example, the generating AI analyzes regional educational disparity data and builds a system to provide special educational support to areas that need it most. For example, it provides programs specialized for areas that lack educational resources. The generating AI also provides special educational support based on regional educational disparity data to eliminate educational disparities. For example, it holds educational events and workshops in specific areas. The generating AI also analyzes regional educational disparity data in real time and provides special educational support to areas that need it most. For example, it optimizes the allocation of educational resources. This eliminates educational disparities by analyzing regional educational disparity data and providing special educational support to areas that need it most.

[0049] Teacher AI can automatically generate policy recommendations aimed at eliminating educational disparities. For example, Teacher AI will build a system in which Generator AI analyzes data on educational disparities and automatically generates policy recommendations based on the results. For example, it will propose the reallocation of educational resources or the introduction of new educational programs. Generator AI will also automatically create policy recommendations aimed at eliminating educational disparities and provide them to governments and educational institutions. For example, it will propose support measures for specific regions or schools. Generator AI will also analyze data on educational disparities in real time and automatically generate policy recommendations. For example, it will visualize the current state of educational disparities and propose specific solutions. In this way, by automatically generating policy recommendations aimed at eliminating educational disparities, it will propose effective policies.

[0050] Teacher AI can collaborate with companies and non-profit organizations to jointly develop educational support programs. For example, Teacher AI will build a system in which Generative AI collaborates with companies and non-profit organizations to jointly develop educational support programs. For example, it will provide educational programs by utilizing corporate resources. Generative AI will also collaborate with companies and non-profit organizations to design educational support programs and eliminate educational disparities. For example, it will provide programs that utilize corporate expertise and technology. Generative AI will also collaborate with companies and non-profit organizations to develop educational support programs in real time. For example, it will jointly hold educational events and workshops. In this way, by collaborating with companies and non-profit organizations and jointly developing educational support programs, it will eliminate educational disparities.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The education support system can also include a health management unit that monitors students' health. For example, the health management unit can analyze students' sleep patterns and meal records and suggest healthy lifestyle habits. The health management unit can also detect lack of exercise and suggest appropriate exercise programs. Furthermore, the health management unit can monitor stress levels and suggest relaxation methods. This allows for comprehensive management of students' health and improves learning effectiveness.

[0053] The education support system can further include a creativity development module to foster students' creativity. For example, the creativity development module can provide a platform where students can freely present their ideas. The creativity development module can also suggest creative assignments and projects to bring out students' creativity. Furthermore, the creativity development module can evaluate students' work and provide feedback. This can foster students' creativity and broaden the scope of their learning.

[0054] The education support system may further include a social skills development section for cultivating students' social skills. For example, the social skills development section may propose group work in which students work together to tackle a task. The social skills development section may also provide workshops for improving communication skills. Furthermore, the social skills development section may provide programs for cultivating students' leadership skills. This will help develop students' social skills and put them to use in their future social lives.

[0055] The education support system can further include a career support section that supports students' career development. For example, the career support section analyzes students' interests and aptitudes and suggests the most suitable occupation. The career support section can also provide work experience programs to allow students to experience actual workplaces. Furthermore, the career support section can support job hunting activities and provide practice in creating resumes and interviews. This can support students' career development and help them choose their future occupations.

[0056] The education support system can further include an environmental education section to raise students' environmental awareness. For example, the environmental education section can provide students with knowledge about environmental issues. The environmental education section can also provide opportunities for students to participate in environmental protection activities and encourage students to actually take action. Furthermore, the environmental education section can provide students with advice on how to develop environmentally conscious lifestyles. This can raise students' environmental awareness and contribute to the realization of a sustainable society.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The educational content analysis unit analyzes educational content. For example, it analyzes the contents of textbooks and reference books, extracts important points, and provides explanations. It can also analyze students' learning histories and present questions tailored to each student's level of understanding. It can also automatically incorporate the latest research papers and educational theories to constantly update teaching methods. Step 2: The individualized instruction provider provides individualized instruction based on the educational content analyzed by the educational content analyzer. For example, it can monitor the student's learning progress in real time and provide feedback based on their level of understanding. It can also analyze the student's learning history, predict their future learning progress, and propose optimal learning plans. Furthermore, it can use its emotion estimation function to generate personalized messages to maintain the student's motivation. Step 3: The online platform provides the individualized instruction provided by the individualized instruction provider through the online platform. For example, to enable learning in areas with unstable internet connections, it can automatically generate educational content that can be used in offline environments. It can also analyze students' schedules and suggest optimal study times. Furthermore, it can add a function that uses emotion estimation to suggest breaks when students' concentration wanes.

[0059] (Example 2) The education support system according to the embodiment of the present invention is a system that uses a low-cost teacher AI to eliminate educational disparities and provide individualized instruction. This enables the education support system to fill educational gaps caused by poverty and provide individualized instruction tailored to each individual.

[0060] An educational support system according to an embodiment includes a teacher AI, an educational content analysis unit, an individualized instruction provision unit, and an online platform. The teacher AI includes an educational content analysis unit that analyzes educational content. For example, the educational content analysis unit analyzes the contents of textbooks and reference books, extracts important points, and provides explanations. The educational content analysis unit can also analyze students' learning histories and pose questions based on their individual levels of understanding. The educational content analysis unit can also automatically incorporate the latest research papers and educational theories to constantly update teaching methods. The individualized instruction provision unit provides individualized instruction based on the educational content analyzed by the educational content analysis unit. For example, the individualized instruction provision unit can monitor students' learning progress in real time and provide feedback based on their level of understanding. The individualized instruction provision unit can also analyze students' learning histories, predict their future learning progress, and propose optimal learning plans. The individualized instruction provision unit can also use an emotion estimation function to generate personalized messages to maintain students' motivation. The online platform provides the individualized instruction provided by the individualized instruction provision unit through the online platform. For example, the online platform automatically generates educational content that can be used in offline environments to enable learning even in areas with unstable internet connections. The online platform can also analyze students' schedules and suggest optimal study times. The online platform can also add a function that uses an emotion estimation function to suggest breaks when students' concentration wanes. This enables the education support system according to the embodiment to eliminate educational disparities and provide individualized instruction using a low-cost teacher AI. For example, the generation AI analyzes educational content, provides individualized instruction, and provides learning through an online platform. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the educational content and provide individualized instruction. The generation AI provides individualized instruction based on the analysis results of the educational content and provides learning through an online platform.

[0061] The educational content analysis unit can analyze the contents of textbooks or reference books, extract and explain important points. For example, the generation AI in the educational content analysis unit analyzes the contents of textbooks or reference books, extracts and explains important points. For example, the generation AI analyzes the contents of textbooks, extracts and explains important points. The generation AI also analyzes the contents of reference books, extracts and explains important points. The generation AI also analyzes the contents of textbooks or reference books, extracts and explains important points. This enables efficient learning by extracting and explaining important points of educational content.

[0062] The individual instruction providing unit can analyze the student's learning history and pose questions according to their level of understanding. In the individual instruction providing unit, for example, the generation AI analyzes the student's learning history and poses questions according to their level of understanding. For example, the generation AI analyzes the student's learning history and poses questions according to their level of understanding. Also, the generation AI analyzes the student's learning history and poses questions according to their level of understanding. Also, the generation AI analyzes the student's learning history and poses questions according to their level of understanding. In this way, by posing questions according to the student's level of understanding, the quality of individual instruction is improved.

[0063] The individualized instruction providing unit can monitor the student's learning progress in real time and provide feedback according to the level of understanding. In the individualized instruction providing unit, for example, the generation AI monitors the student's learning progress in real time and provides feedback according to the level of understanding. For example, the generation AI monitors the student's learning progress in real time and provides feedback according to the level of understanding. Also, the generation AI monitors the student's learning progress in real time and provides feedback according to the level of understanding. Also, the generation AI monitors the student's learning progress in real time and provides feedback according to the level of understanding. In this way, the learning effectiveness is improved by monitoring the student's learning progress in real time and providing appropriate feedback.

[0064] The online platform can monitor students' motivation to learn and stress levels, and suggest encouragement or breaks at appropriate times. For example, the online platform uses a generative AI to analyze students' facial expressions and voices to monitor their motivation to learn and stress levels in real time. For example, it can detect the student's emotional state using a camera or microphone. The generative AI can then use the student's emotional data to send encouraging messages or suggest breaks at appropriate times. For example, it can suggest taking a break when their concentration is waning. The generative AI can also use its emotion estimation function to generate personalized messages to increase students' motivation to learn. For example, it can provide positive feedback or words of encouragement. This allows the platform to monitor students' motivation to learn and stress levels, and suggest encouragement or breaks at appropriate times, thereby improving learning effectiveness.

[0065] Teacher AI can develop special education programs for students with specific learning disabilities. For example, a generative AI could analyze the needs of students with specific learning disabilities and automatically generate a special education program that meets those needs. For example, it could create a program for students with dyslexia (a reading disability). Generative AI could also collect learning data from students with learning disabilities and provide customized educational programs based on that data. For example, it could provide programs that include visual materials and audio commentary. Generative AI could also monitor the progress of students with learning disabilities and adjust the program as needed. For example, it could provide additional support for specific tasks. This improves the quality of learning by developing special education programs for students with specific learning disabilities.

[0066] Teacher AI can also be applied to corporate training and vocational training programs to provide skills education that meets the needs of the labor market. For example, teacher AI uses generative AI to analyze a company's needs and automatically generate training programs that meet them. For example, it could provide training on new technologies or business processes. Generative AI can also analyze labor market trends and design vocational training programs. For example, it could provide programs specialized in skills and qualifications that are in high demand. Generative AI can also monitor the progress of corporate training and vocational training and provide feedback in real time. For example, it could provide additional learning materials or practice questions based on the trainee's level of understanding. This makes it possible to apply this technology to corporate training and vocational training programs to provide skills education that meets the needs of the labor market.

[0067] The teacher AI can be equipped with an emotion estimation function and develop an application that supports parent-child learning at home. For example, the teacher AI can develop an application equipped with an emotion estimation function to support parent-child learning. For example, it can enable parents to monitor their children's learning progress and provide appropriate support. The generative AI can also analyze emotional data during parent-child learning sessions and provide encouragement and advice at the appropriate time. For example, it can suggest a break when a child loses concentration. The generative AI can also record the progress of parent-child learning and provide a customized learning plan based on that data. For example, it can suggest tasks and activities that parents and children should work on together. In this way, by developing an application equipped with an emotion estimation function, it becomes possible to support parent-child learning at home.

[0068] The educational content analysis unit automatically incorporates the latest research papers and educational theories, allowing teaching methods to be constantly updated. In this case, for example, the generative AI automatically collects the latest educational research papers, analyzes their contents, and reflects them in teaching methods. For example, it incorporates new educational theories and effective teaching techniques. The generative AI also builds a system that updates teaching methods in line with advances in educational theory. For example, it regularly reviews educational theories and improves teaching methods. When analyzing educational content, the generative AI also optimizes teaching methods based on the latest research results. For example, it suggests effective learning methods and activities. This allows teaching methods to be constantly optimized by incorporating the latest research papers and educational theories.

[0069] The educational content analysis unit can analyze a student's learning style and provide the optimal teaching method accordingly. In the educational content analysis unit, for example, a generative AI analyzes a student's learning style and provides the appropriate teaching method accordingly. For example, a visual learner is provided with teaching materials that make extensive use of diagrams and graphs. The generative AI also generates customized educational content based on the student's learning style. For example, an auditory learner is provided with teaching materials that include audio commentary. The generative AI also monitors a student's learning style in real time and suggests the optimal teaching method. For example, a tactile learner is provided with hands-on activities. This improves learning effectiveness by providing teaching methods that suit the student's learning style.

[0070] The educational content analysis unit can use the emotion estimation function to analyze students' interests and concerns and provide educational content based on that. The educational content analysis unit, for example, uses the emotion estimation function to analyze students' interests and concerns and provide educational content based on that. For example, it provides teaching materials related to topics that interest students. The generation AI also generates customized educational content based on students' emotional data. For example, it provides teaching materials that include topics and activities that pique students' interest. The generation AI also uses the emotion estimation function to provide educational content that piques students' interest in real time. For example, it provides additional materials and assignments related to content that the student has shown interest in. In this way, providing educational content based on students' interests and concerns increases their motivation to learn.

[0071] The educational content analysis unit can automatically generate multilingual educational content that corresponds to different languages ​​and cultural areas. The educational content analysis unit, for example, builds a system in which a generation AI automatically generates educational content that corresponds to different languages ​​and cultural areas. For example, it provides teaching materials that correspond to multiple languages, such as English, French, and Chinese. The generation AI also automatically creates multilingual educational content and provides it to students from different cultural areas. For example, it provides teaching materials that take cultural backgrounds into consideration. The generation AI also generates multilingual educational content in real time and adapts it to students from different languages ​​and cultural areas. For example, it provides education that transcends language barriers. This allows for the provision of multilingual educational content that corresponds to different languages ​​and cultural areas, thereby realizing global education.

[0072] The educational content analysis unit can also analyze educational content in non-academic fields such as sports and arts and provide teaching methods. For example, the educational content analysis unit uses a generative AI to analyze educational content in non-academic fields such as sports and arts and provide optimal teaching methods. For example, it supports technical sports instruction and artistic creative activities. The generative AI also automatically analyzes educational content in non-academic fields and provides customized teaching methods. For example, it provides music and art lessons. The generative AI also analyzes educational content in non-academic fields in real time and optimizes teaching methods. For example, it proposes sports training plans and artistic creative processes. In this way, comprehensive education is realized by analyzing educational content in non-academic fields such as sports and arts and providing teaching methods.

[0073] The educational content analysis unit can use the emotion estimation function to evaluate the effectiveness of educational content and select the most effective content. The educational content analysis unit, for example, uses the emotion estimation function to build a system for evaluating the effectiveness of educational content. For example, it measures the effectiveness of content based on students' emotional responses. The generation AI also evaluates the effectiveness of educational content based on emotional data and selects the most effective content. For example, it prioritizes providing content with a high number of positive emotional responses. The generation AI also uses the emotion estimation function to evaluate the effectiveness of educational content in real time and select the optimal content. For example, it adjusts the content according to changes in students' emotions. In this way, the effectiveness of educational content is evaluated and the most effective content is selected, maximizing learning effectiveness.

[0074] The individualized instruction providing unit can analyze the student's learning history, predict future learning progress, and propose an optimal learning plan. The individualized instruction providing unit, for example, builds a system in which a generation AI analyzes the student's learning history and predicts future learning progress. For example, it proposes an optimal learning plan based on past learning data. The generation AI also predicts future learning progress based on the student's learning history and provides a customized learning plan. For example, it proposes a plan that focuses on specific subjects or skills. The generation AI also analyzes the student's learning history in real time, predicts future learning progress, and proposes an optimal learning plan. For example, it adjusts the plan according to learning progress. In this way, by analyzing the student's learning history, predicting future learning progress, and proposing an optimal learning plan, learning effectiveness is improved.

[0075] The individualized instruction providing unit can compare a student's learning data with other students and provide feedback on their relative learning status. The individualized instruction providing unit, for example, builds a system in which a generating AI compares a student's learning data with other students and provides feedback on their relative learning status. For example, it evaluates learning progress by comparing with students in the same grade or class. The generating AI also provides comparison results with other students based on the student's learning data and provides feedback on their relative learning status. For example, it shows the student's relative position in a specific subject or skill. The generating AI also analyzes the student's learning data in real time and provides feedback on the comparison results with other students. For example, it compares learning progress and level of understanding with other students and evaluates them. In this way, by comparing the student's learning data with other students and providing feedback on their relative learning status, motivation to learn is improved.

[0076] The individual instruction providing unit can use the emotion estimation function to generate personalized messages to maintain student motivation. The individual instruction providing unit, for example, builds a system that uses the emotion estimation function to generate personalized messages to maintain student motivation. For example, it provides encouraging messages according to the student's emotional state. The generation AI also generates personalized messages based on the student's emotional data to maintain motivation. For example, it provides positive feedback and words of encouragement. The generation AI also uses the emotion estimation function to generate messages to maintain student motivation in real time. For example, it provides encouraging messages when the student has negative emotions about learning. In this way, personalized messages to maintain student motivation are generated, thereby improving learning effectiveness.

[0077] The individualized instruction provision unit can analyze students' learning data and propose optimal combinations for group learning and pair learning. The individualized instruction provision unit, for example, builds a system in which a generation AI analyzes students' learning data and proposes optimal combinations for group learning and pair learning. For example, it determines combinations based on learning style and level of comprehension. The generation AI also proposes optimal groups and pairs based on students' learning history to support effective learning. For example, it pairs students with complementary skills. The generation AI also analyzes students' learning data in real time and proposes optimal combinations for group learning and pair learning. For example, it adjusts combinations according to learning progress and level of comprehension. In this way, learning effectiveness is improved by analyzing students' learning data and proposing optimal combinations for group learning and pair learning.

[0078] The individual instruction provision department develops support tools for private tutors and cram school instructors, thereby improving the quality of individual instruction. For example, the individual instruction provision department builds a system in which the generative AI develops support tools for private tutors and cram school instructors, thereby improving the quality of individual instruction. For example, the generative AI suggests optimal teaching methods based on students' learning data. In addition, private tutors and cram school instructors can use the generative AI to monitor students' learning progress in real time and provide effective feedback. For example, the generative AI provides additional teaching materials and practice problems according to the level of understanding. In addition, the generative AI provides customized teaching plans for private tutors and cram school instructors, thereby improving the quality of individual instruction. For example, it suggests teaching methods according to the student's learning style and needs. In this way, the quality of individual instruction is improved by developing support tools for private tutors and cram school instructors.

[0079] The individualized instruction providing unit can use the emotion estimation function to analyze the emotional compatibility between students and suggest the most suitable study partner. The individualized instruction providing unit, for example, uses the emotion estimation function to analyze the emotional compatibility between students and build a system that suggests the most suitable study partner. For example, it pairs students who get along well with each other based on emotional data. The generation AI also suggests the most suitable study partner based on the students' emotional data to support effective learning. For example, it pairs students who have a positive influence on each other. The generation AI also uses the emotion estimation function to analyze the emotional compatibility between students in real time and suggest the most suitable study partner. For example, it adjusts the partner according to the learning progress and level of understanding. In this way, the learning effectiveness is improved by analyzing the emotional compatibility between students and suggesting the most suitable study partner.

[0080] Online platforms can automatically generate educational content that can be used in offline environments, enabling learning even in areas with unstable internet connections. For example, online platforms build systems in which generative AI automatically generates educational content that can be used in offline environments. For example, it provides downloadable learning materials and apps. The generative AI also provides educational programs that operate offline to enable learning in areas with unstable internet connections. For example, it uses learning materials stored on local devices. The generative AI also generates educational content that can be used offline in real time and provides it to students in areas with unstable internet connections. For example, it records offline learning progress and synchronizes it when the connection is restored. This allows learning to be done even in areas with unstable internet connections by providing educational content that can be used in offline environments.

[0081] The online platform can analyze students' schedules and suggest optimal study times. The online platform, for example, builds a system in which a generative AI analyzes students' schedules and suggests optimal study times. For example, it adjusts study times to match school and club activity schedules. The generative AI also suggests optimal study times based on students' schedule data, supporting effective learning. For example, it recommends studying during times when students are most likely to concentrate. The generative AI also analyzes students' schedules in real time and suggests optimal study times. For example, it adjusts study times according to their learning progress and level of understanding. In this way, analyzing students' schedules and suggesting optimal study times improves learning effectiveness.

[0082] The online platform can add a function that uses the emotion estimation function to suggest a break when a student's concentration wanes. For example, the online platform uses the emotion estimation function to build a system that suggests a break when a student's concentration wanes. For example, it analyzes a student's facial expressions and voice to detect a decline in concentration. The generation AI then detects a decline in concentration based on the student's emotional data and suggests a break at an appropriate time. For example, it displays a message encouraging the student to take a break at regular intervals. The generation AI also uses the emotion estimation function to provide a function in real time that suggests a break when a student's concentration wanes. For example, it suggests a relaxation activity when concentration wanes. This improves learning effectiveness by suggesting a break when a student's concentration wanes.

[0083] Online platforms can develop educational apps for mobile devices or wearable devices. For example, online platforms use generative AI to develop educational apps for mobile devices, enabling learning anytime, anywhere. For example, they provide apps that can be used on smartphones and tablets. Generative AI also develops educational apps for wearable devices, improving learning convenience. For example, they provide apps that can be used on smartwatches and fitness trackers. Generative AI also develops educational apps for mobile devices and wearable devices in real time, increasing learning flexibility. For example, they provide apps that can be used offline. This improves learning convenience by developing educational apps for mobile devices and wearable devices.

[0084] Online platforms can build online learning communities that connect students in remote locations. For example, generative AI can build online learning communities that connect students in remote locations, promoting the sharing and collaboration of learning. For example, it can provide video chat and forum functions. Generative AI can also develop platforms that allow students in remote locations to share their learning online. For example, it can provide tools for working on projects together. Generative AI can also build online learning communities that connect students in remote locations in real time, promoting mutual support in learning. For example, it can provide a function for sharing learning progress. In this way, building online learning communities that connect students in remote locations promotes the sharing and collaboration of learning.

[0085] The online platform can use the emotion estimation function to introduce gamification elements to increase students' motivation to learn. The online platform, for example, builds a system that uses the emotion estimation function to introduce gamification elements to increase students' motivation to learn. For example, points or badges are awarded according to learning progress. The generation AI also customizes the gamification elements based on the students' emotional data to increase their motivation to learn. For example, challenges and rewards are provided according to their emotional state. The generation AI also uses the emotion estimation function to introduce gamification elements in real time to increase students' motivation to learn. For example, level ups and rewards are provided according to learning progress. In this way, the introduction of gamification elements to increase students' motivation to learn improves learning effectiveness.

[0086] Teacher AI can provide free or low-cost educational programs tailored to economically disadvantaged families. For example, Teacher AI builds a system in which Generator AI provides free or low-cost educational programs tailored to economically disadvantaged families. For example, it provides free online teaching materials and lessons. Generator AI also designs low-cost educational programs so that students from economically disadvantaged families can receive a high-quality education. For example, it provides programs using sponsorships and donations. Generator AI also provides educational programs tailored to economically disadvantaged families in real time, thereby eliminating educational disparities. For example, it provides online courses that can be used at low cost. This eliminates educational disparities by providing free or low-cost educational programs tailored to economically disadvantaged families.

[0087] The teaching AI can analyze regional educational disparity data and provide special educational support to areas that need it most. For example, the generating AI analyzes regional educational disparity data and builds a system to provide special educational support to areas that need it most. For example, it provides programs specialized for areas that lack educational resources. The generating AI also provides special educational support based on regional educational disparity data to eliminate educational disparities. For example, it holds educational events and workshops in specific areas. The generating AI also analyzes regional educational disparity data in real time and provides special educational support to areas that need it most. For example, it optimizes the allocation of educational resources. This eliminates educational disparities by analyzing regional educational disparity data and providing special educational support to areas that need it most.

[0088] The teacher AI can use the emotion estimation function to analyze the impact of educational disparity on students' psychology and provide appropriate mental support. For example, the teacher AI can use the emotion estimation function to analyze the impact of educational disparity on students' psychology and build a system to provide appropriate mental support. For example, it can monitor students' stress levels and provide counseling. The generation AI can also analyze the impact of educational disparity based on students' emotional data and provide mental support. For example, it can provide positive feedback and encouraging messages. The generation AI can also use the emotion estimation function to analyze the impact of educational disparity on students' psychology in real time and provide appropriate mental support. For example, it can suggest relaxation activities to reduce stress. In this way, the impact of educational disparity on students' psychology can be analyzed and appropriate mental support can be provided, thereby reducing the psychological burden on students.

[0089] Teacher AI can automatically generate policy recommendations aimed at eliminating educational disparities. For example, Teacher AI will build a system in which Generator AI analyzes data on educational disparities and automatically generates policy recommendations based on the results. For example, it will propose the reallocation of educational resources or the introduction of new educational programs. Generator AI will also automatically create policy recommendations aimed at eliminating educational disparities and provide them to governments and educational institutions. For example, it will propose support measures for specific regions or schools. Generator AI will also analyze data on educational disparities in real time and automatically generate policy recommendations. For example, it will visualize the current state of educational disparities and propose specific solutions. In this way, by automatically generating policy recommendations aimed at eliminating educational disparities, it will propose effective policies.

[0090] Teacher AI can collaborate with companies and non-profit organizations to jointly develop educational support programs. For example, Teacher AI will build a system in which Generative AI collaborates with companies and non-profit organizations to jointly develop educational support programs. For example, it will provide educational programs by utilizing corporate resources. Generative AI will also collaborate with companies and non-profit organizations to design educational support programs and eliminate educational disparities. For example, it will provide programs that utilize corporate expertise and technology. Generative AI will also collaborate with companies and non-profit organizations to develop educational support programs in real time. For example, it will jointly hold educational events and workshops. In this way, by collaborating with companies and non-profit organizations and jointly developing educational support programs, it will eliminate educational disparities.

[0091] The teacher AI can use the emotion estimation function to launch a campaign to raise social awareness of educational inequality. For example, the teacher AI uses the emotion estimation function to build a system that launches a campaign to raise social awareness of educational inequality. For example, it creates effective messages based on emotional data. The generation AI also analyzes the emotional data and launches a campaign to raise social awareness of educational inequality. For example, it creates videos and posters that appeal to emotions. The generation AI also uses the emotion estimation function to launch a campaign to raise social awareness of educational inequality in real time. For example, it provides messages tailored to the target demographic based on the emotional data. In this way, by launching a campaign to raise social awareness of educational inequality, social movements toward eliminating educational inequality can be promoted.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The education support system can also include a health management unit that monitors students' health. For example, the health management unit can analyze students' sleep patterns and meal records and suggest healthy lifestyle habits. The health management unit can also detect lack of exercise and suggest appropriate exercise programs. Furthermore, the health management unit can monitor stress levels and suggest relaxation methods. This allows for comprehensive management of students' health and improves learning effectiveness.

[0094] The education support system can further include a creativity development module to foster students' creativity. For example, the creativity development module can provide a platform where students can freely present their ideas. The creativity development module can also suggest creative assignments and projects to bring out students' creativity. Furthermore, the creativity development module can evaluate students' work and provide feedback. This can foster students' creativity and broaden the scope of their learning.

[0095] The education support system may further include a social skills development section for cultivating students' social skills. For example, the social skills development section may propose group work in which students work together to tackle a task. The social skills development section may also provide workshops for improving communication skills. Furthermore, the social skills development section may provide programs for cultivating students' leadership skills. This will help develop students' social skills and put them to use in their future social lives.

[0096] The education support system can further include a career support section that supports students' career development. For example, the career support section analyzes students' interests and aptitudes and suggests the most suitable occupation. The career support section can also provide work experience programs to allow students to experience actual workplaces. Furthermore, the career support section can support job hunting activities and provide practice in creating resumes and interviews. This can support students' career development and help them choose their future occupations.

[0097] The education support system can further include an environmental education section to raise students' environmental awareness. For example, the environmental education section can provide students with knowledge about environmental issues. The environmental education section can also provide opportunities for students to participate in environmental protection activities and encourage students to actually take action. Furthermore, the environmental education section can provide students with advice on how to develop environmentally conscious lifestyles. This can raise students' environmental awareness and contribute to the realization of a sustainable society.

[0098] The education support system may further include an emotion analysis unit that estimates a student's emotions and provides learning content based on those emotions. For example, the emotion analysis unit may analyze a student's facial expressions and voice to estimate their current emotional state. The emotion analysis unit may also suggest learning content that is likely to interest the student based on the estimated emotions. Furthermore, the emotion analysis unit may also suggest activities to help the student relax, depending on the student's emotional state. This may provide a learning environment that takes students' emotions into consideration, thereby improving learning effectiveness.

[0099] The education support system may further include an emotion feedback unit that estimates the student's emotions and provides feedback based on the emotions. For example, the emotion feedback unit may analyze the student's emotional state and provide an encouraging message at an appropriate time. The emotion feedback unit may also suggest relaxation methods if the student is feeling stressed. Furthermore, the emotion feedback unit may provide feedback according to the student's learning progress based on the student's emotion data. This allows the system to provide feedback that takes into consideration the student's emotions and increase their motivation to learn.

[0100] The education support system may further include an emotional learning planning unit that estimates the student's emotions and proposes a learning plan based on the emotions. For example, the emotional learning planning unit may analyze the student's emotional state and propose an optimal learning schedule. The emotional learning planning unit may also suggest appropriate break times to help the student maintain concentration. Furthermore, the emotional learning planning unit may adjust the learning plan according to the student's learning progress based on the student's emotional data. This allows for the provision of a learning plan that takes the student's emotions into consideration, thereby improving learning effectiveness.

[0101] The education support system can further include an emotional partner suggestion unit that estimates a student's emotions and suggests study partners based on the emotions. For example, the emotional partner suggestion unit analyzes the student's emotional state and suggests compatible study partners. The emotional partner suggestion unit can also match students with partners who have a positive influence on each other based on the emotional data between the students. Furthermore, the emotional partner suggestion unit can adjust the combination of study partners according to the student's emotional state. This makes it possible to suggest study partners that take the student's emotions into consideration and improve learning effectiveness.

[0102] The education support system may further include an emotional environment providing unit that estimates the student's emotions and provides a learning environment based on the student's emotions. For example, the emotional environment providing unit may analyze the student's emotional state and suggest an optimal learning environment. The emotional environment providing unit may also suggest appropriate music and lighting to help the student relax. Furthermore, the emotional environment providing unit may adjust the learning environment in real time based on the student's emotional data. This allows the system to provide a learning environment that takes the student's emotions into consideration, thereby improving learning effectiveness.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The educational content analysis unit analyzes educational content. For example, it analyzes the contents of textbooks and reference books, extracts important points, and provides explanations. It can also analyze students' learning histories and present questions tailored to each student's level of understanding. It can also automatically incorporate the latest research papers and educational theories to constantly update teaching methods. Step 2: The individualized instruction provider provides individualized instruction based on the educational content analyzed by the educational content analyzer. For example, it can monitor the student's learning progress in real time and provide feedback based on their level of understanding. It can also analyze the student's learning history, predict their future learning progress, and propose optimal learning plans. Furthermore, it can use its emotion estimation function to generate personalized messages to maintain the student's motivation. Step 3: The online platform provides the individualized instruction provided by the individualized instruction provider through the online platform. For example, to enable learning in areas with unstable internet connections, it can automatically generate educational content that can be used in offline environments. It can also analyze students' schedules and suggest optimal study times. Furthermore, it can add a function that uses emotion estimation to suggest breaks when students' concentration wanes.

[0105] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a 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.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. Equipped with teacher AI, The teacher AI includes an educational content analysis unit that analyzes educational content; an individualized instruction providing unit that provides individualized instruction based on the educational content analyzed by the educational content analysis unit; an online platform that provides the individualized instruction provided by the individualized instruction providing unit through an online platform; A system characterized by:

2. The educational content analysis unit Analyze the contents of textbooks or reference books, extract important points, and explain them 2. The system of claim 1.

3. The individual instruction providing unit Analyze students' learning history and provide questions based on their level of understanding 2. The system of claim 1.

4. The individual instruction providing unit Monitor students' learning progress in real time and provide feedback based on their level of understanding 2. The system of claim 1.

5. The online platform: Monitor students' motivation or stress levels and offer encouragement or breaks when appropriate 2. The system of claim 1.

Images