System

Abstract

An object of the system according to the embodiment is to allow a student to independently advance learning and to improve exploration of a learning process and self-adjustment learning.SOLUTION: A system includes a photograph analysis part, a hint provision part, a development problem setting part, a learning result visualization part, and a feedback part. The photograph analysis unit analyzes a photograph of a text question. The hint providing unit provides a hint to the question analyzed by the photo analyzing unit step by step. The development problem setting part sets a development problem according to the learning level after the student reaches the answer by the hint providing part. The learning result visualization unit visualizes a learning result of the problem set by the development problem setting unit. The feedback unit performs feedback based on the learning result visualized by the learning result visualization unit.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 technologies do not provide sufficient support for students to progress in their learning independently, and there are challenges in exploring the learning process and improving self-regulated learning.

[0005] The system according to the embodiment aims to encourage students to learn independently, to explore the learning process and to improve self-regulated learning. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, a hint provision unit, an advanced question setting unit, a learning result visualization unit, and a feedback unit. The photo analysis unit analyzes photos of text questions. The hint provision unit provides step-by-step hints for questions analyzed by the photo analysis unit. The advanced question setting unit sets advanced questions according to the student's level of proficiency after the hint provision unit has helped the student arrive at the answer. The learning result visualization unit visualizes the learning results of the questions set by the advanced question setting unit. The feedback unit provides feedback based on the learning results visualized by the learning result visualization unit. [Effects of the Invention]

[0007] The system according to the embodiment allows students to learn independently, improves learning process exploration and self-regulated learning. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) An educational support platform according to an embodiment of the present invention utilizes generative AI to assist students from elementary school to high school in completing homework across a wide range of subjects. This system allows students to take and upload photos of text questions, and provides hints without immediately providing the answer, helping them arrive at the answer themselves. Once the answer is found, the AI ​​automatically presents advanced questions tailored to the student's level to further deepen their proficiency and solidify what they have learned. Furthermore, learning results are visualized and feedback is provided, allowing students to adjust their future study plans and behaviors based on that feedback. This allows the educational support platform to foster independent study habits, explore learning processes, and improve self-regulated learning.

[0029] An educational support platform according to an embodiment includes a photo analysis unit, a hint providing unit, an advanced problem setting unit, a learning result visualization unit, and a feedback unit. The photo analysis unit analyzes photos of text problems. For example, the photo analysis unit analyzes handwritten or printed problems photographed with a smartphone or tablet and converts them into text data. The photo analysis unit recognizes characters using OCR technology and analyzes the content of the problem. The hint providing unit provides step-by-step hints for problems analyzed by the photo analysis unit. For example, for a math problem, the hint providing unit first shows the steps for solving the problem and then shows the specific calculation method. The hint providing unit supports students in arriving at the answer by thinking for themselves. After the student arrives at the answer using the hint providing unit, the advanced problem setting unit presents advanced problems according to the student's level of proficiency. For example, the advanced problem setting unit presents applied or more difficult problems after solving basic problems. The advanced problem setting unit analyzes the student's answer history and learning situation to select the most appropriate questions. The learning result visualization unit visualizes the learning results of the questions posed by the advanced question setting unit. For example, the learning result visualization unit displays the correct answer rate, answer time, answer trends, etc. in graphs or charts. The feedback unit provides feedback based on the learning results visualized by the learning result visualization unit. For example, the feedback unit provides specific advice to the student and supports them in making study plans for the next time and beyond. In this way, the education support platform according to the embodiment can help students develop independent learning habits, explore their learning process, and improve self-regulated learning.

[0030] The photo analysis unit accurately analyzes handwritten characters or figures, enabling a more detailed understanding of the content of the problem. For example, when recognizing handwritten characters, the photo analysis unit analyzes differences in character shape and writing pressure and converts them into accurate text data. For example, the recognition accuracy is improved by taking into account differences in different writing instruments and writing styles. Furthermore, when recognizing figures, the photo analysis unit analyzes geometric figures and freehand figures to understand the content of the problem in detail. For example, the shape and arrangement of the figure are analyzed to extract information necessary for solving the problem. This allows for accurate analysis of handwritten characters and figures, enabling a more detailed understanding of the content of the problem.

[0031] The photo analysis unit can automatically adjust background or lighting conditions to improve the accuracy of text recognition. For example, the photo analysis unit automatically removes the background of an uploaded photo and extracts only the text portion. For example, background noise and unnecessary parts are removed to improve the accuracy of text recognition. The photo analysis unit also automatically adjusts lighting conditions to optimize brightness and contrast. For example, lighting distortion is corrected to improve the accuracy of text recognition. In this way, the background and lighting conditions can be automatically adjusted to improve the accuracy of text recognition.

[0032] The photo analysis unit accepts audio or video input, and the generation AI analyzes it to recognize questions. For example, when a student reads a question aloud, the photo analysis unit allows the generation AI to analyze the audio data and convert it into text data. For example, it uses voice recognition technology to accurately recognize the content of the question. The photo analysis unit also accepts video input, and the generation AI analyzes the text and figures in the video. For example, it recognizes the text in the video using OCR technology and analyzes the figures using image recognition technology. This allows questions to be recognized by analyzing audio and video input.

[0033] The photo analysis unit supports uploading from different devices and can realize collaboration between devices. The photo analysis unit supports uploading of photos from different devices, such as smartphones, tablets, and PCs. For example, it provides applications and web interfaces compatible with each device. The photo analysis unit also uses cloud storage to realize collaboration between devices. For example, it stores uploaded photos in the cloud and makes them accessible from different devices. This makes it possible to support uploading from different devices and realize seamless collaboration between devices.

[0034] The hint providing unit can be customized according to the student's level of understanding and provide individually optimized support. The hint providing unit, for example, analyzes the student's past learning history and answer patterns and provides hints according to the student's level of understanding. For example, it provides basic hints to students with low levels of understanding and applied hints to students with high levels of understanding. The hint providing unit also customizes hints based on the real-time answering status. For example, it shows specific steps if the student is not making progress in answering, and provides supplementary information if the student is making progress in answering. This makes it possible to customize according to the student's level of understanding and provide individually optimized support.

[0035] The hint providing unit can provide the contents of the hint not only in text but also visually using video or interactive animation. For example, the hint providing unit provides the contents of the hint in video format to enable students to understand visually. For example, the steps of the solution are explained in video. The hint providing unit also provides hints using interactive animation. For example, the changes in shapes are shown in animation to enable students to visually understand how to solve the problem. Furthermore, the hint providing unit provides hints by combining text with video or animation. For example, explanations are given in text while supplemented with video or animation. In this way, the contents of the hint are provided visually, thereby deepening students' understanding.

[0036] The hint providing unit can incorporate feedback from other students and teachers and provide hints on a community basis. For example, the hint providing unit collects feedback from other students and teachers and provides hints based on that. For example, it refers to sample answers from students who have solved the same problem or advice from teachers. In addition, the hint providing unit shares message boards and sample answers in real time to provide hints on a community basis. For example, students can share sample answers with each other on the platform and give each other advice. This allows hints to be provided on a community basis by incorporating feedback from other students and teachers.

[0037] The advanced question setting unit can analyze a student's learning history and answer patterns and select the most appropriate advanced question. The advanced question setting unit, for example, analyzes a student's past learning history and selects advanced questions according to their level of understanding and proficiency. For example, it sets the most appropriate question based on the correct answer rate and answer time of questions previously solved. The advanced question setting unit also analyzes a student's answer patterns and identifies weak points and areas of strength. For example, if the correct answer rate in a particular area is low, it sets questions related to that area. In this way, it is possible to analyze a student's past learning history and answer patterns and select the most appropriate advanced question.

[0038] The advanced question setting unit can adjust the difficulty of advanced questions in real time and dynamically change it according to the student's level of understanding. The advanced question setting unit, for example, monitors the student's answering status in real time and dynamically adjusts the difficulty of advanced questions. For example, it increases the difficulty level when the student is progressing smoothly with the answers and decreases the difficulty level when the student is stuck on the answer. The advanced question setting unit also adjusts the order of questions to provide questions according to the student's level of understanding. For example, it starts with basic questions and gradually increases the difficulty level. In this way, the difficulty of advanced questions can be adjusted in real time and dynamically changed according to the student's level of understanding.

[0039] The advanced question setting unit can set advanced questions in a cross-curriculum format that integrates knowledge from different departments or fields. For example, the advanced question setting unit sets advanced questions that integrate knowledge from different departments. For example, it sets questions that combine knowledge of mathematics and physics. The advanced question setting unit also sets questions that relate to real life. For example, it sets questions that combine knowledge of science and social studies. Furthermore, the advanced question setting unit sets questions that integrate knowledge of history and geography. For example, it sets questions that combine historical events and geographical elements. This makes it possible to set advanced questions in a cross-curriculum format that integrates knowledge from different departments or fields.

[0040] The advanced question setting unit can set advanced questions in a group learning or competitive format to promote cooperation or competition between students. The advanced question setting unit, for example, sets advanced questions in a group learning format to promote cooperation between students. For example, it provides questions to be answered by teams. The advanced question setting unit can also set advanced questions in a competitive format to promote competition between students. For example, it provides questions in the form of a time trial or quiz. Furthermore, the advanced question setting unit can provide questions for team competition to promote cooperation and competition between students at the same time. This allows the setting of advanced questions to be set in a group learning or competitive format to promote cooperation and competition between students.

[0041] The learning result visualization unit can provide an interactive dashboard, allowing students to intuitively understand their own progress. The learning result visualization unit, for example, visualizes learning results in an interactive dashboard, allowing students to intuitively understand their own progress. For example, it displays learning results using graphs and charts. The learning result visualization unit also provides a filtering function, allowing students to check learning results by narrowing down to a specific period or item. For example, it displays learning results for a specific subject or period. Furthermore, the learning result visualization unit provides an analysis function, allowing students to analyze their learning results in detail. For example, it analyzes trends in the rate of correct answers and answer times. In this way, it is possible to provide an interactive dashboard, allowing students to intuitively understand their own progress.

[0042] The feedback unit can customize the feedback content according to the student's learning style and goals, and provide individually optimized advice. For example, the feedback unit customizes the content of the feedback according to the student's learning style and goals. For example, feedback using graphs and diagrams is provided to a student with a visual learning style. The feedback unit also provides advice based on the student's learning history and real-time progress. For example, if there is a lack of understanding in a particular area, the feedback unit suggests a study plan that focuses on that area. Furthermore, the feedback unit provides a customized study plan that matches the student's learning pace and learning style. This makes it possible to provide advice that is customized according to the student's learning style and goals, and is individually optimized.

[0043] The learning result visualization unit can share the visualization of learning results with parents and teachers, thereby strengthening support at home and at school. The learning result visualization unit can, for example, share learning results with parents and teachers, thereby strengthening support at home and at school. For example, learning results can be shared by email or a dedicated app. The learning result visualization unit also provides an online dashboard, allowing parents and teachers to check learning results in real time. For example, it can suggest learning plans and additional learning support. This allows the visualization of learning results to be shared with parents and teachers, thereby strengthening support at home and at school.

[0044] The feedback unit can incorporate game elements into the content of the feedback to enable students to learn while having fun. The feedback unit can, for example, incorporate game elements into the content of the feedback to enable students to learn while having fun. For example, a system can be introduced that allows students to earn points or badges according to their learning results. The feedback unit can also provide feedback in the form of a quiz to enable students to learn while having fun. For example, rewards can be provided according to the progress of learning. Furthermore, the feedback unit can visualize the level of learning achievement through the growth of a character. In this way, game elements can be incorporated into the content of the feedback to enable students to learn while having fun.

[0045] The learning plan adjustment unit can adjust the student's learning plan and learning behavior from the next time onwards based on the feedback. The learning plan adjustment unit, for example, adjusts the student's learning plan and learning behavior from the next time onwards based on the feedback. For example, if there is a lack of understanding in a particular area, the learning plan adjustment unit proposes a learning plan that focuses on that area. The learning plan adjustment unit also provides a customized learning plan that matches the student's learning pace and learning style. For example, it provides more assignments to a student who learns at a fast pace, and assignments that progress gradually to a student who learns at a slow pace. In this way, the student's learning plan and learning behavior from the next time onwards can be adjusted based on the feedback.

[0046] The learning plan adjustment unit can customize the learning plan to suit the learning pace and learning style of the student. The learning plan adjustment unit customizes the learning plan to suit, for example, the learning pace and learning style of the student. For example, it provides a learning plan that makes extensive use of diagrams and graphs to a student with a visual learning style, and a learning plan that makes extensive use of audio commentary to a student with an auditory learning style. The learning plan adjustment unit also adjusts the amount and difficulty of assignments according to the student's learning pace. For example, it provides more assignments to a student with a fast learning pace, and assignments that progress gradually to a student with a slow learning pace. In this way, the learning plan can be customized to suit the learning pace and learning style of the student.

[0047] The study plan adjustment unit can propose a study plan that focuses on a specific area when a student's understanding is lacking in that area. For example, when a student's understanding is lacking in a specific area, the study plan adjustment unit proposes a study plan that focuses on that area. For example, when a student's understanding of mathematics is lacking, a study plan that focuses on mathematics is provided. The study plan adjustment unit also analyzes the student's study history and answer patterns to identify weak points. For example, when a student's correct answer rate is low in a specific area, many assignments related to that area are provided. In this way, when a student's understanding is lacking in a specific area, a study plan that focuses on that area can be proposed.

[0048] The study plan adjustment unit can optimize the study plan based on the student's study history and answer patterns. The study plan adjustment unit, for example, analyzes the student's study history and answer patterns to optimize the study plan. For example, it provides a study plan according to the student's level of understanding and proficiency based on past test results and study time. The study plan adjustment unit also identifies the student's weak points and areas of strength, and adjusts the study plan based on that. For example, if the student's rate of correct answers in a particular area is low, it provides more tasks related to that area. This makes it possible to optimize the study plan based on the student's study history and answer patterns.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The educational support platform can further include an audio feedback unit. The audio feedback unit provides audio feedback when a student enters an answer. For example, if the answer is correct, an encouraging message such as "Good job!" is provided, and if the answer is incorrect, audio advice such as "Please think about it again" is provided. The audio feedback unit can also provide additional audio advice on specific areas based on the student's answer history. This allows students to receive feedback not only visually but also aurally, thereby improving the effectiveness of their learning.

[0051] The educational support platform can further include a gamification section. The gamification section makes learning fun by incorporating game elements when students input answers. For example, students can earn points for each problem they solve and use those points to develop their characters. A system can also be introduced that allows students to earn badges or trophies when they achieve certain goals. Furthermore, a ranking function that allows students to compete with other students can be provided, increasing motivation for learning. This allows students to learn while having fun.

[0052] The educational support platform can further include a reminder section. The reminder section provides study reminders based on the study schedule set by the student. For example, it can send a notification such as "Start studying math now" at a specific time. The reminder section can also suggest the next task to be tackled based on the student's study progress. Furthermore, the reminder section can send notifications urging the student to take a break between studies. This allows the student to proceed with their studies in a planned manner.

[0053] The educational support platform can also be equipped with a virtual assistant section. When a student inputs a question, the virtual assistant section uses generative AI to provide an instant answer. For example, it can provide detailed explanations for questions about how to solve a math problem or a historical event. The virtual assistant section can also suggest what the student should learn next based on their learning history. Furthermore, the virtual assistant section can monitor the student's learning progress and provide additional support as needed. This allows students to get answers to their questions at any time, improving their learning efficiency.

[0054] The educational support platform can further include a multilingual support section for learning content. The multilingual support section automatically translates learning content according to the language selected by the student. For example, if a student selects English, all questions, hints, and feedback will be displayed in English. The multilingual support section can also provide a function for comparing learning progress across different languages. This allows students learning different languages ​​to progress on the same platform.

[0055] The educational support platform can further include a learning content customization unit. The customization unit customizes learning content according to the student's interests. For example, a student who is interested in science can be provided with many science-related questions and hints. A student who is interested in a particular topic can also be provided with learning content related to that topic. Furthermore, the customization unit can suggest what the student should learn next based on the student's learning history. This makes it possible to provide learning content that matches the student's interests and increase their motivation to learn.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The photo analysis unit analyzes the photo of the text question. For example, it analyzes handwritten questions or printed questions photographed with a smartphone or tablet and converts them into text data. The photo analysis unit uses OCR technology to recognize the characters and analyze the content of the question. Step 2: The hint provider provides step-by-step hints for the problem analyzed by the photo analyzer. For example, for a math problem, the hint provider first shows the steps for solving the problem, and then shows the specific calculation method. The hint provider helps students think for themselves to arrive at the answer. Step 3: After the student arrives at the answer using the hint provider, the advanced problem generator presents advanced problems according to the student's level of proficiency. For example, after solving a basic problem, the generator presents applied or more difficult problems. The advanced problem generator analyzes the student's answer history and learning situation to select the most appropriate questions. Step 4: The learning result visualization unit visualizes the learning results of the questions posed by the advanced question setting unit. For example, it displays the correct answer rate, answer time, answer trends, etc. in graphs and charts. Step 5: The feedback unit provides feedback based on the learning results visualized by the learning result visualization unit. For example, it provides specific advice to the student and helps them create a learning plan for the next time.

[0058] (Example 2) An educational support platform according to an embodiment of the present invention utilizes generative AI to assist students from elementary school to high school in completing homework across a wide range of subjects. This system allows students to take and upload photos of text questions, and provides hints without immediately providing the answer, helping them arrive at the answer themselves. Once the answer is found, the AI ​​automatically presents advanced questions tailored to the student's level to further deepen their proficiency and solidify what they have learned. Furthermore, learning results are visualized and feedback is provided, allowing students to adjust their future study plans and behaviors based on that feedback. This allows the educational support platform to foster independent study habits, explore learning processes, and improve self-regulated learning.

[0059] An educational support platform according to an embodiment includes a photo analysis unit, a hint providing unit, an advanced problem setting unit, a learning result visualization unit, and a feedback unit. The photo analysis unit analyzes photos of text problems. For example, the photo analysis unit analyzes handwritten or printed problems photographed with a smartphone or tablet and converts them into text data. The photo analysis unit recognizes characters using OCR technology and analyzes the content of the problem. The hint providing unit provides step-by-step hints for problems analyzed by the photo analysis unit. For example, for a math problem, the hint providing unit first shows the steps for solving the problem and then shows the specific calculation method. The hint providing unit supports students in arriving at the answer by thinking for themselves. After the student arrives at the answer using the hint providing unit, the advanced problem setting unit presents advanced problems according to the student's level of proficiency. For example, the advanced problem setting unit presents applied or more difficult problems after solving basic problems. The advanced problem setting unit analyzes the student's answer history and learning situation to select the most appropriate questions. The learning result visualization unit visualizes the learning results of the questions posed by the advanced question setting unit. For example, the learning result visualization unit displays the correct answer rate, answer time, answer trends, etc. in graphs or charts. The feedback unit provides feedback based on the learning results visualized by the learning result visualization unit. For example, the feedback unit provides specific advice to the student and supports them in making study plans for the next time and beyond. In this way, the education support platform according to the embodiment can help students develop independent learning habits, explore their learning process, and improve self-regulated learning.

[0060] The photo analysis unit accurately analyzes handwritten characters or figures, enabling a more detailed understanding of the content of the problem. For example, when recognizing handwritten characters, the photo analysis unit analyzes differences in character shape and writing pressure and converts them into accurate text data. For example, the recognition accuracy is improved by taking into account differences in different writing instruments and writing styles. Furthermore, when recognizing figures, the photo analysis unit analyzes geometric figures and freehand figures to understand the content of the problem in detail. For example, the shape and arrangement of the figure are analyzed to extract information necessary for solving the problem. This allows for accurate analysis of handwritten characters and figures, enabling a more detailed understanding of the content of the problem.

[0061] The photo analysis unit can automatically adjust background or lighting conditions to improve the accuracy of text recognition. For example, the photo analysis unit automatically removes the background of an uploaded photo and extracts only the text portion. For example, background noise and unnecessary parts are removed to improve the accuracy of text recognition. The photo analysis unit also automatically adjusts lighting conditions to optimize brightness and contrast. For example, lighting distortion is corrected to improve the accuracy of text recognition. In this way, the background and lighting conditions can be automatically adjusted to improve the accuracy of text recognition.

[0062] The photo analysis unit can use an emotion estimation function to analyze the student's emotions and provide advice to reduce stress or anxiety. The photo analysis unit, for example, analyzes the student's facial expression when taking a photo of the question to estimate the emotion. For example, a camera can be used to analyze the student's facial expression in real time to detect stress or anxiety. The photo analysis unit can also analyze the student's voice when taking a photo of the question to estimate the emotion. For example, the tone and speed of voice can be analyzed to calculate an emotion score. The photo analysis unit can also analyze the student's emotions and provide advice to reduce stress and anxiety. For example, the photo analysis unit can suggest deep breathing, recommend taking a break, or provide positive feedback. This can provide advice to reduce the student's stress and anxiety.

[0063] The photo analysis unit accepts audio or video input, and the generation AI analyzes it to recognize questions. For example, when a student reads a question aloud, the photo analysis unit allows the generation AI to analyze the audio data and convert it into text data. For example, it uses voice recognition technology to accurately recognize the content of the question. The photo analysis unit also accepts video input, and the generation AI analyzes the text and figures in the video. For example, it recognizes the text in the video using OCR technology and analyzes the figures using image recognition technology. This allows questions to be recognized by analyzing audio and video input.

[0064] The photo analysis unit supports uploading from different devices and can realize collaboration between devices. The photo analysis unit supports uploading of photos from different devices, such as smartphones, tablets, and PCs. For example, it provides applications and web interfaces compatible with each device. The photo analysis unit also uses cloud storage to realize collaboration between devices. For example, it stores uploaded photos in the cloud and makes them accessible from different devices. This makes it possible to support uploading from different devices and realize seamless collaboration between devices.

[0065] The photo analysis unit uses an emotion estimation function to monitor students' emotions in real time and provide positive feedback. The photo analysis unit monitors emotions in real time, for example, by analyzing facial expressions and voices when students upload questions. For example, it detects the student's emotional state using a camera or microphone. The photo analysis unit also analyzes students' emotions and provides positive feedback. For example, it presents encouraging messages or success stories. This makes it possible to monitor students' emotions in real time when they upload questions and provide positive feedback.

[0066] The hint providing unit can be customized according to the student's level of understanding and provide individually optimized support. The hint providing unit, for example, analyzes the student's past learning history and answer patterns and provides hints according to the student's level of understanding. For example, it provides basic hints to students with low levels of understanding and applied hints to students with high levels of understanding. The hint providing unit also customizes hints based on the real-time answering status. For example, it shows specific steps if the student is not making progress in answering, and provides supplementary information if the student is making progress in answering. This makes it possible to customize according to the student's level of understanding and provide individually optimized support.

[0067] The hint providing unit can provide the contents of the hint not only in text but also visually using video or interactive animation. For example, the hint providing unit provides the contents of the hint in video format to enable students to understand visually. For example, the steps of the solution are explained in video. The hint providing unit also provides hints using interactive animation. For example, the changes in shapes are shown in animation to enable students to visually understand how to solve the problem. Furthermore, the hint providing unit provides hints by combining text with video or animation. For example, explanations are given in text while supplemented with video or animation. In this way, the contents of the hint are provided visually, thereby deepening students' understanding.

[0068] The hint providing unit can analyze the student's emotions using the emotion estimation function and provide additional hints to increase motivation. The hint providing unit can, for example, analyze the student's facial expression or voice when receiving a hint to estimate the student's emotion. For example, the hint providing unit can detect the student's emotional state in real time using a camera or microphone. The hint providing unit can also analyze the student's emotions and provide additional hints to increase motivation. For example, the hint providing unit can provide positive feedback or encouraging messages. This makes it possible to analyze the student's emotions and provide additional hints to increase motivation.

[0069] The hint providing unit can incorporate feedback from other students and teachers and provide hints on a community basis. For example, the hint providing unit collects feedback from other students and teachers and provides hints based on that. For example, it refers to sample answers from students who have solved the same problem or advice from teachers. In addition, the hint providing unit shares message boards and sample answers in real time to provide hints on a community basis. For example, students can share sample answers with each other on the platform and give each other advice. This allows hints to be provided on a community basis by incorporating feedback from other students and teachers.

[0070] The hint providing unit can monitor the student's emotions in real time using the emotion estimation function and provide additional support to elicit positive emotions. The hint providing unit, for example, analyzes the student's facial expressions and voice when receiving a hint and monitors emotions in real time. For example, it detects the student's emotional state using a camera or microphone. The hint providing unit also analyzes the student's emotions and provides additional support to elicit positive emotions. For example, it provides positive feedback or encouraging messages. This makes it possible to monitor the student's emotions in real time and provide additional support to elicit positive emotions.

[0071] The advanced question setting unit can analyze a student's learning history and answer patterns and select the most appropriate advanced question. The advanced question setting unit, for example, analyzes a student's past learning history and selects advanced questions according to their level of understanding and proficiency. For example, it sets the most appropriate question based on the correct answer rate and answer time of questions previously solved. The advanced question setting unit also analyzes a student's answer patterns and identifies weak points and areas of strength. For example, if the correct answer rate in a particular area is low, it sets questions related to that area. In this way, it is possible to analyze a student's past learning history and answer patterns and select the most appropriate advanced question.

[0072] The advanced question setting unit can adjust the difficulty of advanced questions in real time and dynamically change it according to the student's level of understanding. The advanced question setting unit, for example, monitors the student's answering status in real time and dynamically adjusts the difficulty of advanced questions. For example, it increases the difficulty level when the student is progressing smoothly with the answers and decreases the difficulty level when the student is stuck on the answer. The advanced question setting unit also adjusts the order of questions to provide questions according to the student's level of understanding. For example, it starts with basic questions and gradually increases the difficulty level. In this way, the difficulty of advanced questions can be adjusted in real time and dynamically changed according to the student's level of understanding.

[0073] The advanced question setting unit can analyze the student's emotions using the emotion estimation function and provide additional questions to maintain motivation. The advanced question setting unit, for example, analyzes the student's facial expressions and voice when working on advanced questions to estimate emotions. For example, it detects the student's emotional state in real time using a camera or microphone. The advanced question setting unit also analyzes the student's emotions and provides additional questions to maintain motivation. For example, it provides positive feedback or encouraging messages. This makes it possible to analyze the student's emotions and provide additional questions to maintain motivation.

[0074] The advanced question setting unit can set advanced questions in a cross-curriculum format that integrates knowledge from different departments or fields. For example, the advanced question setting unit sets advanced questions that integrate knowledge from different departments. For example, it sets questions that combine knowledge of mathematics and physics. The advanced question setting unit also sets questions that relate to real life. For example, it sets questions that combine knowledge of science and social studies. Furthermore, the advanced question setting unit sets questions that integrate knowledge of history and geography. For example, it sets questions that combine historical events and geographical elements. This makes it possible to set advanced questions in a cross-curriculum format that integrates knowledge from different departments or fields.

[0075] The advanced question setting unit can set advanced questions in a group learning or competitive format to promote cooperation or competition between students. The advanced question setting unit, for example, sets advanced questions in a group learning format to promote cooperation between students. For example, it provides questions to be answered by teams. The advanced question setting unit can also set advanced questions in a competitive format to promote competition between students. For example, it provides questions in the form of a time trial or quiz. Furthermore, the advanced question setting unit can provide questions for team competition to promote cooperation and competition between students at the same time. This allows the setting of advanced questions to be set in a group learning or competitive format to promote cooperation and competition between students.

[0076] The advanced question setting unit can monitor students' emotions in real time using an emotion estimation function and provide additional support to elicit positive emotions. The advanced question setting unit, for example, analyzes facial expressions and voices of students as they work on advanced questions to monitor emotions in real time. For example, it detects the student's emotional state using a camera or microphone. The advanced question setting unit also analyzes students' emotions and provides additional support to elicit positive emotions. For example, it provides positive feedback or encouraging messages. This makes it possible to monitor students' emotions in real time and provide additional support to elicit positive emotions.

[0077] The learning result visualization unit can provide an interactive dashboard, allowing students to intuitively understand their own progress. The learning result visualization unit, for example, visualizes learning results in an interactive dashboard, allowing students to intuitively understand their own progress. For example, it displays learning results using graphs and charts. The learning result visualization unit also provides a filtering function, allowing students to check learning results by narrowing down to a specific period or item. For example, it displays learning results for a specific subject or period. Furthermore, the learning result visualization unit provides an analysis function, allowing students to analyze their learning results in detail. For example, it analyzes trends in the rate of correct answers and answer times. In this way, it is possible to provide an interactive dashboard, allowing students to intuitively understand their own progress.

[0078] The feedback unit can customize the feedback content according to the student's learning style and goals, and provide individually optimized advice. For example, the feedback unit customizes the content of the feedback according to the student's learning style and goals. For example, feedback using graphs and diagrams is provided to a student with a visual learning style. The feedback unit also provides advice based on the student's learning history and real-time progress. For example, if there is a lack of understanding in a particular area, the feedback unit suggests a study plan that focuses on that area. Furthermore, the feedback unit provides a customized study plan that matches the student's learning pace and learning style. This makes it possible to provide advice that is customized according to the student's learning style and goals, and is individually optimized.

[0079] The feedback unit can use the emotion estimation function to analyze the student's emotions and provide additional advice to increase motivation. The feedback unit, for example, analyzes the student's facial expression and voice when receiving feedback to estimate the student's emotions. For example, the feedback unit detects the student's emotional state in real time using a camera or microphone. The feedback unit also analyzes the student's emotions and provides additional advice to increase motivation. For example, it provides positive feedback or encouraging messages. This makes it possible to analyze the student's emotions and provide additional advice to increase motivation.

[0080] The learning result visualization unit can share the visualization of learning results with parents and teachers, thereby strengthening support at home and at school. The learning result visualization unit can, for example, share learning results with parents and teachers, thereby strengthening support at home and at school. For example, learning results can be shared by email or a dedicated app. The learning result visualization unit also provides an online dashboard, allowing parents and teachers to check learning results in real time. For example, it can suggest learning plans and additional learning support. This allows the visualization of learning results to be shared with parents and teachers, thereby strengthening support at home and at school.

[0081] The feedback unit can incorporate game elements into the content of the feedback to enable students to learn while having fun. The feedback unit can, for example, incorporate game elements into the content of the feedback to enable students to learn while having fun. For example, a system can be introduced that allows students to earn points or badges according to their learning results. The feedback unit can also provide feedback in the form of a quiz to enable students to learn while having fun. For example, rewards can be provided according to the progress of learning. Furthermore, the feedback unit can visualize the level of learning achievement through the growth of a character. In this way, game elements can be incorporated into the content of the feedback to enable students to learn while having fun.

[0082] The learning plan adjustment unit can adjust the student's learning plan and learning behavior from the next time onwards based on the feedback. The learning plan adjustment unit, for example, adjusts the student's learning plan and learning behavior from the next time onwards based on the feedback. For example, if there is a lack of understanding in a particular area, the learning plan adjustment unit proposes a learning plan that focuses on that area. The learning plan adjustment unit also provides a customized learning plan that matches the student's learning pace and learning style. For example, it provides more assignments to a student who learns at a fast pace, and assignments that progress gradually to a student who learns at a slow pace. In this way, the student's learning plan and learning behavior from the next time onwards can be adjusted based on the feedback.

[0083] The study plan adjustment unit can analyze the student's emotions using the emotion estimation function and adjust the study plan. The study plan adjustment unit, for example, analyzes the student's emotions and adjusts the study plan. For example, if the student is feeling stressed, it proposes a plan to reduce the study load. The study plan adjustment unit also monitors the student's emotions in real time and dynamically adjusts the study plan. For example, if positive emotions are increasing, it provides more difficult tasks. In this way, the student's emotions can be analyzed and the study plan can be adjusted.

[0084] The learning plan adjustment unit can customize the learning plan to suit the learning pace and learning style of the student. The learning plan adjustment unit customizes the learning plan to suit, for example, the learning pace and learning style of the student. For example, it provides a learning plan that makes extensive use of diagrams and graphs to a student with a visual learning style, and a learning plan that makes extensive use of audio commentary to a student with an auditory learning style. The learning plan adjustment unit also adjusts the amount and difficulty of assignments according to the student's learning pace. For example, it provides more assignments to a student with a fast learning pace, and assignments that progress gradually to a student with a slow learning pace. In this way, the learning plan can be customized to suit the learning pace and learning style of the student.

[0085] The study plan adjustment unit can propose a study plan that focuses on a specific area when a student's understanding is lacking in that area. For example, when a student's understanding is lacking in a specific area, the study plan adjustment unit proposes a study plan that focuses on that area. For example, when a student's understanding of mathematics is lacking, a study plan that focuses on mathematics is provided. The study plan adjustment unit also analyzes the student's study history and answer patterns to identify weak points. For example, when a student's correct answer rate is low in a specific area, many assignments related to that area are provided. In this way, when a student's understanding is lacking in a specific area, a study plan that focuses on that area can be proposed.

[0086] The study plan adjustment unit can use the emotion estimation function to monitor the student's emotions in real time and adjust the study plan. The study plan adjustment unit, for example, monitors the student's emotions in real time and adjusts the study plan. For example, if the student is feeling stressed, it proposes a plan to reduce the study load. The study plan adjustment unit also analyzes the student's emotions and provides more difficult tasks when positive emotions are increasing. In this way, it is possible to monitor the student's emotions in real time and adjust the study plan.

[0087] The study plan adjustment unit can optimize the study plan based on the student's study history and answer patterns. The study plan adjustment unit, for example, analyzes the student's study history and answer patterns to optimize the study plan. For example, it provides a study plan according to the student's level of understanding and proficiency based on past test results and study time. The study plan adjustment unit also identifies the student's weak points and areas of strength, and adjusts the study plan based on that. For example, if the student's rate of correct answers in a particular area is low, it provides more tasks related to that area. This makes it possible to optimize the study plan based on the student's study history and answer patterns.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The educational support platform can further include an audio feedback unit. The audio feedback unit provides audio feedback when a student enters an answer. For example, if the answer is correct, an encouraging message such as "Good job!" is provided, and if the answer is incorrect, audio advice such as "Please think about it again" is provided. The audio feedback unit can also provide additional audio advice on specific areas based on the student's answer history. This allows students to receive feedback not only visually but also aurally, thereby improving the effectiveness of their learning.

[0090] The educational support platform can further include a gamification section. The gamification section makes learning fun by incorporating game elements when students input answers. For example, students can earn points for each problem they solve and use those points to develop their characters. A system can also be introduced that allows students to earn badges or trophies when they achieve certain goals. Furthermore, a ranking function that allows students to compete with other students can be provided, increasing motivation for learning. This allows students to learn while having fun.

[0091] The educational support platform can further include a reminder section. The reminder section provides study reminders based on the study schedule set by the student. For example, it can send a notification such as "Start studying math now" at a specific time. The reminder section can also suggest the next task to be tackled based on the student's study progress. Furthermore, the reminder section can send notifications urging the student to take a break between studies. This allows the student to proceed with their studies in a planned manner.

[0092] The educational support platform can also use the emotion estimation function to analyze students' emotions and provide relaxation content to reduce stress and anxiety. For example, if a student feels stressed while solving a problem, it can provide relaxation music or deep breathing guidance. The emotion estimation function can also be used to provide positive feedback when a student is relaxed. This can reduce students' stress and anxiety and provide an environment where they can concentrate on their studies.

[0093] The educational support platform can also be equipped with a virtual assistant section. When a student inputs a question, the virtual assistant section uses generative AI to provide an instant answer. For example, it can provide detailed explanations for questions about how to solve a math problem or a historical event. The virtual assistant section can also suggest what the student should learn next based on their learning history. Furthermore, the virtual assistant section can monitor the student's learning progress and provide additional support as needed. This allows students to get answers to their questions at any time, improving their learning efficiency.

[0094] The educational support platform can also use emotion estimation to analyze students' emotions and provide customized messages to motivate them to study. For example, if a student shows positive emotions while solving a problem, it can provide an encouraging message such as "Great! Keep it up!". On the other hand, if a student shows negative emotions, it can provide advice such as "Take a short break and refresh yourself." This allows it to provide support tailored to students' emotions and maintain their motivation to study.

[0095] The educational support platform can further include a multilingual support section for learning content. The multilingual support section automatically translates learning content according to the language selected by the student. For example, if a student selects English, all questions, hints, and feedback will be displayed in English. The multilingual support section can also provide a function for comparing learning progress across different languages. This allows students learning different languages ​​to progress on the same platform.

[0096] The educational support platform can also use emotion estimation to monitor students' emotions in real time and provide positive feedback according to their learning progress. For example, if a student is concentrating while solving a problem, it can provide a message such as "Your concentration is amazing!". If the answer is correct, it can also provide a congratulatory message such as "Congratulations!". This makes it possible to monitor students' emotions in real time and provide positive feedback according to their learning progress.

[0097] The educational support platform can further include a learning content customization unit. The customization unit customizes learning content according to the student's interests. For example, a student who is interested in science can be provided with many science-related questions and hints. A student who is interested in a particular topic can also be provided with learning content related to that topic. Furthermore, the customization unit can suggest what the student should learn next based on the student's learning history. This makes it possible to provide learning content that matches the student's interests and increase their motivation to learn.

[0098] The educational support platform can also use emotion estimation to analyze students' emotions and provide additional support to improve their motivation according to their learning progress. For example, if a student feels tired while solving a problem, it can provide advice such as "Take a short break and refresh yourself." If the student shows positive emotions, it can also provide encouraging messages such as "Keep up the good work!" This allows it to provide support according to students' emotions and maintain their motivation to study.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The photo analysis unit analyzes the photo of the text question. For example, it analyzes handwritten questions or printed questions photographed with a smartphone or tablet and converts them into text data. The photo analysis unit uses OCR technology to recognize the characters and analyze the content of the question. Step 2: The hint provider provides step-by-step hints for the problem analyzed by the photo analyzer. For example, for a math problem, the hint provider first shows the steps for solving the problem, and then shows the specific calculation method. The hint provider helps students think for themselves to arrive at the answer. Step 3: After the student arrives at the answer using the hint provider, the advanced problem generator presents advanced problems according to the student's level of proficiency. For example, after solving a basic problem, the generator presents applied or more difficult problems. The advanced problem generator analyzes the student's answer history and learning situation to select the most appropriate questions. Step 4: The learning result visualization unit visualizes the learning results of the questions posed by the advanced question setting unit. For example, it displays the correct answer rate, answer time, answer trends, etc. in graphs and charts. Step 5: The feedback unit provides feedback based on the learning results visualized by the learning result visualization unit. For example, it provides specific advice to the student and helps them create a learning plan for the next time.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0103] 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.

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

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0117] 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.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. A photo analysis section that analyzes the photos in the text questions; a hint providing unit that provides step-by-step hints to the problem analyzed by the photo analyzing unit; an advanced question setting unit that sets advanced questions according to the student's proficiency level after the student has arrived at the answer using the hint providing unit; a learning result visualization unit that visualizes the learning results of the questions set by the advanced question setting unit; a feedback unit that provides feedback based on the learning result visualized by the learning result visualization unit. A system characterized by:

2. The photo analysis unit In addition to photos, it also accepts voice or video input, and the generation AI analyzes them to recognize the problem.

2. The system of claim 1.

3. The hint providing unit Customize and provide individually optimized support according to the student's level of understanding 2. The system of claim 1.

4. The advanced question setting unit Analyze the student's learning history and answer patterns to select the most suitable advanced questions.

2. The system of claim 1.

5. The learning result visualization unit Provide an interactive dashboard that allows students to intuitively understand their progress 2. The system of claim 1.

6. The Learning Plan Coordination Department Based on this feedback, the student's future learning plans and learning behaviors are adjusted.

2. The system of claim 1.

7. The photo analysis unit Analyze the student's feelings about the shooting problem and offer advice to reduce stress or anxiety 2. The system of claim 1.

8. The hint providing unit Analyze the student's feelings when receiving the hint and provide additional hints to increase motivation 2. The system of claim 1.

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