system

Abstract

We provide the system. [Solution] A data collection method for recording students' learning activities in real time, A data analysis method that analyzes recorded learning data to identify each student's learning style, strengths, and weaknesses, A plan creation means for generating individually optimized learning plans based on identified results, A feedback system that provides real-time feedback to students during their learning, A teacher support tool that generates detailed reports used to support teachers, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In a conventional educational system, it is difficult to provide an optimized learning experience for each student, and a great deal of labor and time are required for teachers to give guidance according to the characteristics of students. Also, it is difficult to grasp the progress of learning in real time and provide immediate feedback, resulting in problems such as a decline in learning efficiency and insufficient response to individual learning needs. 【Means for Solving the Problems】 【0005】 This invention provides a means to identify each student's learning style and areas of strength and weakness by analyzing the data obtained from an AI-powered educational support system that records students' learning activities in real time. Furthermore, it improves learning efficiency and results by generating individually optimized learning plans based on the analysis results and providing real-time feedback to students. In addition, it creates an environment where teachers can concentrate on instructing students by generating and providing detailed reports. This effectively solves the above-mentioned problems. 【0006】 "Data collection means" refers to a technical device or method for recording students' learning activities in real time and collecting relevant learning data. 【0007】 "Data analysis means" refers to a technical device or method for analyzing collected learning data to identify each student's learning style and areas of strength and weakness. 【0008】 "Plan creation means" refers to a technical device or method that generates a learning plan optimized for each individual student based on the analysis results. 【0009】 A "feedback mechanism" is a technical device or method that responds to students' learning activities in real time and provides immediate guidance or assistance. 【0010】 "Teacher support tools" are technical devices or methods that generate detailed learning progress reports and provide teachers with the information necessary to provide effective instruction to students. 【0011】 A "plan adjustment tool" is a technical device or method for analyzing students' learning weaknesses based on their answer history and incorporating tasks that correspond to their next learning plan. 【0012】 A "dashboard" is a visual interface designed to allow users to see their learning progress and performance at a glance. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like. 【0017】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0019】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0020】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0024】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0025】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0026】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0027】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0028】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0031】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0032】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0033】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0034】 The AI-powered educational support system of the present invention provides an environment that optimizes the learning experience through the collaborative operation of a server, terminals, and users. Embodiments of this system are described below. 【0035】 The system begins with the user (student) accessing the online platform using a device. The device functions as a data collection tool by recording the user's actions in real time and sending the collected data to the server. The server analyzes the received data to identify learning styles and each user's strengths and weaknesses. This process executes the data analysis mechanism. 【0036】 Next, the server generates an individually optimized learning plan based on the analysis results. This is achieved by dynamically incorporating learning materials and tests tailored to each user's needs. The plan creation mechanism functions throughout this process. 【0037】 As the user progresses through the learning process, the device communicates with the server each time a question is answered, instantly determining whether the answer is correct. The server generates and provides feedback to the user based on the correctness of the answer. This feedback is designed to enhance motivation and facilitate understanding. This feedback mechanism is achieved through such immediate responses. 【0038】 Furthermore, the server uses the accumulated data and feedback information to create a detailed report, which is then provided to the teacher. The teacher can use this report to understand the learning progress of individual students and provide effective instruction. In this way, a teacher support system is realized. 【0039】 For example, if a user has weaknesses in a particular math subject, this system will suggest a focused learning plan to reinforce those areas. Furthermore, if a user answers a problem incorrectly, the server immediately analyzes the answer and provides hints for further learning. As a result, users can continue to receive high-quality instruction at their own pace. 【0040】 Thus, the present invention aims to improve the quality of education by providing educational support that meets the individual needs of students. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The user logs into the learning platform using their device. After logging in, the user selects an appropriate course and learning materials to begin their studies. From this point on, the device begins recording the user's learning activity. 【0044】 Step 2: 【0045】 The device continuously records learning activities such as the user's viewing time of learning materials, the questions answered, and the scores obtained. This data is transmitted to the server in real time. 【0046】 Step 3: 【0047】 The server analyzes the received training data. Specifically, it analyzes the user's answer patterns and time allocation to identify their learning style and areas of strength and weakness. This result forms the basis for the next step. 【0048】 Step 4: 【0049】 The server generates an individually optimized learning plan based on the analysis results. Specifically, it selects learning materials to overcome the user's weaknesses and incorporates them into the next learning session. The generated plan is sent to the terminal and provided to the user. 【0050】 Step 5: 【0051】 As the user progresses through the learning process, the device sends each answer to the server in real time. The server immediately evaluates these answers and determines their correctness. The results are returned to the user as feedback and displayed on the screen. 【0052】 Step 6: 【0053】 The user receives feedback and has the opportunity to correct mistakes or relearn. The device continues to record the user's activity throughout this process, and this data is then used again by the server for analysis. 【0054】 Step 7: 【0055】 The server integrates all the collected data and generates detailed analytics reports for teachers. This allows teachers to gain an overview of users' learning progress and obtain information that can be used for individualized instruction. 【0056】 (Example 1) 【0057】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0058】 The current education system lacks the flexibility and ability to respond immediately to the individual learning needs of students. This makes it difficult to provide an optimized learning experience for each student, and there is a need to efficiently identify and address each student's strengths and weaknesses. Furthermore, the provision of specific and detailed reports to instructors is insufficient, making it difficult to effectively provide individualized instruction. 【0059】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0060】 In this invention, the server includes information gathering means for recording learners' educational activities in real time, information analysis means for analyzing the recorded educational data and identifying each learner's educational style, strengths, and weaknesses, and planning means for generating individually optimized educational plans based on the identified results. This makes it possible to provide an educational experience optimized for each learner. 【0061】 "Information gathering means" refers to a device or function for recording learners' educational activities in real time and collecting those activities as data. 【0062】 "Information analysis means" refers to a device or function for analyzing recorded educational data to identify each learner's learning style, areas of strength, and areas of weakness. 【0063】 "Plan creation means" refers to a device or function for generating an individually optimized educational plan for learners based on the results of information analysis. 【0064】 A "response mechanism" is a device or function for providing real-time feedback to learners during instruction. 【0065】 "Leadership support tools" refer to devices or functions for generating and providing detailed reports used to support leaders. 【0066】 "Generation means" refers to a device or function that uses an artificial intelligence model to analyze educational data and provide dynamically generated educational plans and feedback. 【0067】 A "plan adjustment tool" is a device or function that analyzes a learner's weaknesses in a specific area based on their answer history and incorporates tasks dealing with that area into the next educational plan. 【0068】 A "display device for instructors" is a device or function that visually displays the educational status and progress of each learner to the instructor. 【0069】 The present invention provides an environment that enables individualized instruction in educational settings. This system involves the collaborative operation of a server, terminals, and users to provide learners with an optimized educational experience. 【0070】 The server collects data related to educational activities transmitted from terminals and functions as an information gathering tool. This involves using terminals such as PCs and tablets used by users. The terminals record user actions and learning processes in real time and transfer the data via internet communication to the server. HTTP and WebSocket protocols are used for this transfer. 【0071】 The server stores the received data in a database and then performs analysis using data analysis tools. The database management systems used include common solutions such as MySQL® and MongoDB. For data analysis, AI models are employed, using machine learning libraries such as Python's TENSORFLOW® and Scikit-learn to identify each learner's learning style and areas of strength and weakness. 【0072】 Based on these analysis results, the server utilizes planning tools to generate individually optimized learning plans for each learner. The generated plan dynamically selects necessary materials and tests via the LMS API and delivers them to the user's terminal in real time. Specifically, if weaknesses are found in a particular subject, a learning plan is created to focus on strengthening those areas. Dynamic generation by AI models plays a crucial role in this process. 【0073】 As learners progress through educational activities, the server provides immediate feedback on user responses using a response mechanism. Specifically, when a user submits an answer, the server determines its correctness and, if necessary, suggests areas for improvement or hints for further learning. 【0074】 Furthermore, the server uses instructor support tools to analyze accumulated educational data and feedback information, generating detailed reports and providing them to instructors. Instructors can then utilize these reports to provide appropriate guidance based on the individual learners' situations. 【0075】 As a concrete example, here is an example of a prompt sentence to input into a generative AI model: "If a user is having difficulty memorizing English words, please suggest an appropriate learning plan for that user." 【0076】 Through this system, learners can progress at their own pace, flexibly and effectively, while instructors can provide more precise guidance. In this way, it contributes to improving the quality of education. 【0077】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0078】 Step 1: User Access and Login 【0079】 The user accesses the online platform using their device and begins learning. The device receives the user's login information as input and authenticates them through the authentication system. Upon successful authentication, the device starts a session and prepares to record the user's learning activity. During this process, the device communicates securely with the server using HTTPS. 【0080】 Step 2: Recording user actions and sending data 【0081】 The device continuously records user actions and learning progress in real time. Specifically, this includes data such as page navigation, problem answers, and study time. This data is transmitted sequentially from the device to the server. The transmitted data is stored in a database and used as input for data analysis on the server. 【0082】 Step 3: Data analysis and identification of learning style 【0083】 The server receives data sent from the terminal and stores it in a database. Next, the server analyzes this data using an AI model. Specifically, it analyzes each user's learning pattern and identifies their strengths and weaknesses. For example, by identifying topics in mathematics where the user frequently makes mistakes, it reveals the user's weak points. The results of this analysis become input for the next step. 【0084】 Step 4: Generating an Individually Optimized Learning Plan 【0085】 The server generates an individually optimized learning plan based on the results of data analysis. This plan is a process that takes the analysis results as input and dynamically selects learning materials and tests as output. For example, for a user with weaknesses in a specific area, additional learning materials to strengthen that area will be selected. The generated plan is sent to the user's terminal. 【0086】 Step 5: Providing feedback during learning 【0087】 Each time a user answers a question on their device, the device sends the answer data to the server. The server receives this answer data as input, uses an AI model to determine if it is correct or incorrect, and generates feedback as a response. This feedback is sent to the user's device in real time and provided to the user immediately. For example, it may include specific messages such as "That's correct!" or "Let's think about it again." 【0088】 Step 6: Create and provide a detailed report 【0089】 The server generates detailed reports based on accumulated educational data and feedback information. These reports include each user's learning progress and characteristics. Once generated, these reports are provided to the instructor's display device, allowing instructors to adjust their teaching plans accordingly. This reporting process makes it easier for instructors to visually understand the status of each learner. 【0090】 (Application Example 1) 【0091】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0092】 Providing flexible learning support tailored to each student's individual learning style and weaknesses is challenging in home learning. In particular, the lack of appropriate real-time feedback and insufficient interactive learning support using voice recognition are significant issues. 【0093】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0094】 In this invention, the server includes data collection means for recording students' learning activities in real time, data analysis means for analyzing the recorded learning data to identify each student's learning style, strengths, and weaknesses, and plan creation means for generating individually optimized learning plans based on the identified results. This enables real-time feedback and interactive learning support utilizing speech recognition and data communication. 【0095】 A "data collection method" is a component that has the function of observing students' learning activities in real time and recording data on their actions and progress. 【0096】 "Data analysis means" refers to components that perform information processing using recorded learning data to identify individual students' learning styles and areas of strength and weakness. 【0097】 "Plan creation means" refers to the components for dynamically generating customized learning plans for each student based on the results of data analysis. 【0098】 A "feedback mechanism" is a component that provides real-time evaluation and advice in response to students' answers and actions, thereby supporting their learning. 【0099】 "Teacher support tools" are components that generate detailed reports summarizing students' learning progress and provide teachers with assistance in instruction. 【0100】 A "control means" is a component that utilizes speech recognition and data communication to present learning tasks in response to user instructions and provide learning support. 【0101】 The system for implementing this invention is specifically designed to support home learning, and involves the collaborative operation of a server, terminals, and users. 【0102】 First, the server records students' learning activities in real time and analyzes the recorded data through data analysis tools. This identifies each student's learning style, strengths, and weaknesses. Based on the analysis results, a plan creation tool generates individually optimized learning plans. Throughout this entire system, the server plays a central role in managing data collection, analysis, and plan creation. The server and terminals are connected via the internet, and data communication is conducted using the HTTP protocol. 【0103】 Meanwhile, the device uses speech recognition to present learning tasks to students and records their actions and answers. The device is equipped with a speech recognition microphone and a touch interface, allowing it to receive user instructions. It utilizes Google® Speech-to-Text API for speech recognition, enabling real-time and effective interactive learning. Furthermore, the device communicates with a server and has the functionality to provide immediate feedback using feedback mechanisms. This allows students to instantly receive evaluations and hints for improvement. 【0104】 Students, as users, can manage their learning progress and receive efficient learning support by using this system via their devices. For example, if a student enters an incorrect answer while solving a math problem, the server immediately analyzes it and provides feedback via the device, such as "Let's try again." Because the feedback is generated using a generative AI model, more advanced support is possible. 【0105】 An example of a prompt message for a generative AI model might be, "If the user's solution is incorrect, please provide feedback to prompt retraining." 【0106】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0107】 Step 1: 【0108】 The server records students' learning activities in real time through their devices. It receives user operation data and responses as input and writes them to a database to accumulate a history of learning activities. 【0109】 Step 2: 【0110】 The server analyzes the recorded data using data analysis tools. The input here is the student learning data recorded in step 1. The server applies an analysis algorithm and generates output that identifies each student's learning style and areas of strength and weakness as the analysis result. 【0111】 Step 3: 【0112】 The server generates an individually optimized learning plan using a plan creation mechanism based on the analysis results. The input for this step is the analysis results from step 2, and the server programmatically assembles the customized learning plan and sends the generated learning plan to the terminal as output. 【0113】 Step 4: 【0114】 The device uses speech recognition to present learning tasks to students according to their learning plan. Based on the learning plan received as input, it uses a speech recognition API to present the questions in either voice or text format and notifies the user of this information as output. 【0115】 Step 5: 【0116】 The user answers the presented learning tasks. The user provides the answers to the terminal as input, and this operation is immediately recorded and proceeds to the next server processing step. 【0117】 Step 6: 【0118】 The server receives user responses and provides immediate evaluation using a feedback mechanism. The input is user response data, and the server uses a generative AI model to perform the evaluation. Based on the results, it generates immediate feedback, which is then output to the terminal. 【0119】 Step 7: 【0120】 The terminal provides the user with feedback sent from the server. The input is the feedback information output from the server in step 6, and the terminal communicates the feedback to the student through screen display or audio output device. 【0121】 Step 8: 【0122】 The server periodically generates detailed reports based on accumulated learning data and feedback, and reports these to teachers using teacher support tools. The input consists of all recorded data and feedback information, and the server uses a report generation algorithm to output a detailed report of the learning progress. 【0123】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0124】 The present invention enhances the individualized learning experience of students by incorporating an emotion engine into an AI-powered educational support system. In the implementation of this system, the server, terminals, and users interact with each other at a central level. 【0125】 First, the user participates in a learning session using a device. The device tracks the user's learning activity in real time and collects data. This data is continuously transmitted to the server. The server analyzes the received learning data to identify the user's learning style, strengths and weaknesses, and create an individually optimized learning plan based on this information. 【0126】 In addition, this invention incorporates an emotion engine in the terminal, which is responsible for recognizing the user's emotions through facial expressions, tone of voice, and other factors. The server analyzes this emotion information to understand the user's emotional state in real time. The information from the emotion engine is used to immediately adjust the learning plan and feedback content according to the user's stress level and learning attitude. 【0127】 Specifically, if the emotion engine determines that a user is facing difficulties and feeling frustrated during their learning, the server adjusts the learning plan and provides easier-to-understand supplementary materials and step-by-step guidance. The emotion engine also continuously sends the user's emotional state to the server, which then displays this data on a dashboard accessible to teachers. This allows teachers to quickly understand the user's situation and provide appropriate support. 【0128】 Thus, the present invention provides a function that supports students' learning performance and experiences from an emotional perspective, thereby offering an individually optimized learning process and providing information for teachers to intervene effectively. 【0129】 The following describes the processing flow. 【0130】 Step 1: 【0131】 The user accesses the learning session using their device. The device verifies the user's authentication information and grants access to the learning platform. The user selects learning materials appropriate to their learning progress and begins learning. 【0132】 Step 2: 【0133】 The device monitors the user's learning activity in real time and records data such as learning material viewing status, response time, and test scores. This data is sent to the server with the user's permission. 【0134】 Step 3: 【0135】 The device's built-in emotion engine analyzes the user's facial expressions and voice to recognize their emotional state in real time. This allows information such as whether the user is stressed or relaxed to be extracted. 【0136】 Step 4: 【0137】 The server integrates and analyzes the learning data and sentiment data sent from the terminal. Based on this, it evaluates the most effective learning plan based on the user's learning patterns and current emotional state, and redesigns the plan as needed. 【0138】 Step 5: 【0139】 The server adjusts the feedback based on information obtained from the emotion engine. For example, if the user is frustrated, it sends an encouraging message and provides additional resources to explain the problem more clearly. 【0140】 Step 6: 【0141】 The server displays the analysis results and the user's emotional state on a teacher dashboard. Teachers use this information to understand the difficulties and progress the user is facing and plan support, whether in person or online. 【0142】 Step 7: 【0143】 Users learn based on the feedback they receive and receive further feedback through the emotion engine. This allows users to learn at their own pace while receiving support as needed. 【0144】 (Example 2) 【0145】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0146】 While conventional educational support systems record learners' activities and performance, they fail to adequately grasp their emotions and psychological states during these periods and incorporate them into learning plans. This has resulted in the challenge of providing individually optimized learning support. Furthermore, there is a lack of information that allows teachers to quickly understand learners' situations and provide appropriate guidance. 【0147】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0148】 In this invention, the server includes data analysis means, emotion analysis means, and plan adjustment means. This enables the integrated analysis of learner activity data and emotional state, and the adjustment of the learning plan in real time. As a result, an optimized learning experience is provided for each individual learner, and teachers can immediately grasp the situation and provide appropriate guidance. 【0149】 "Data collection means" refers to a device or method for recording learners' learning activities in real time. 【0150】 "Data analysis means" refers to a device or method for analyzing recorded learning data to identify each learner's learning style, strengths, and weaknesses. 【0151】 "Plan creation means" refers to an apparatus or method for generating an individually optimized learning plan based on the analyzed results. 【0152】 "Emotional analysis means" refers to a device or method for recognizing and analyzing a learner's emotional state. 【0153】 A "plan adjustment means" is a device or method for adjusting a learning plan in real time based on emotional information. 【0154】 A "feedback device" is a device or method for providing real-time feedback to learners during their learning process. 【0155】 "Teacher support means" refers to a device or method for generating detailed reports used to support teachers. 【0156】 A "teacher dashboard" is an interface or system for visually displaying the learning status and progress of each student. 【0157】 A "generative AI model" is a model based on artificial intelligence technology used for data analysis and generating learning plans. 【0158】 A "prompt statement" is an input statement used to give instructions or make inquiries to a generative AI model. 【0159】 This invention is an AI-powered educational support system that includes an emotion analysis function to provide learners with an individually optimized learning experience. The method for implementing this system is described below. 【0160】 The server uses a generative AI model to analyze the learner's learning data and sentiment information. Learners participate in learning sessions using a dedicated terminal. The terminal monitors the user's learning activity in real time and collects data such as clicks, answers, and response times. This data is continuously transmitted to the server. 【0161】 The device is equipped with a camera and microphone to detect the user's facial expressions and voice tone. An emotion engine analyzes this data in real time to recognize the user's emotional state. Based on this emotional information, the server adjusts the learning plan and provides the learner with supplementary materials and step-by-step guides. 【0162】 For example, if analysis reveals that a user is experiencing frustration due to a difficult problem, the server uses a generative AI model to generate additional materials to help them understand the problem. In this way, the learner's comprehension can be improved, and the learning experience can be enhanced. 【0163】 The server provides teachers with learner progress and sentiment data through a dashboard, enabling teachers to support learners more effectively. Additionally, an example of a prompt message can be given to the generating AI model: "Please tell me how to change learning materials in real time based on the user's learning attitude." 【0164】 In this way, the present invention is designed to provide learners with a highly individualized learning environment, enabling teachers to provide appropriate support throughout the process. 【0165】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0166】 Step 1: 【0167】 The user starts a learning session using the device. The device records learning activity data in real time, such as the user's actions, inputs, and response times. Inputs are the user's clicks and keyboard inputs, and outputs are these data saved as logs. Specifically, the device sequentially stores the information received from the user interface into a data buffer. 【0168】 Step 2: 【0169】 The device sends the collected learning activity data to the server. The input is the learning data stored on the device, and the output is the data stored in a structured format in a database on the server. Specifically, the device periodically converts the data into packets via batch processing over the internet and sends them to the server's receiving API. 【0170】 Step 3: 【0171】 The server analyzes the received training data. The input is the training data stored in the server's database, and the output is the analysis results showing the user's learning style, strengths, and weaknesses. These results are generated by classifying the data using a generative AI model and applying a pattern recognition algorithm. Specifically, the dataset is processed using data mining techniques, and the generative AI model generates the analysis results. 【0172】 Step 4: 【0173】 The device senses the user's facial expressions and voice tone, collecting emotional data. Input is video and audio data obtained from the camera and microphone, while output is data indicating the user's emotional state. An emotion analysis algorithm analyzes this data to determine the user's emotional state. Specifically, the device periodically samples sensor data and inputs it into the emotion recognition model. 【0174】 Step 5: 【0175】 The server adjusts the learning plan based on sentiment analysis data. The input is the user's sentiment data obtained from sentiment analysis and the learning analysis results, and the output is the adjusted learning plan. The generative AI model considers the emotional state and decides how to improve or supplement the learning content. Specifically, the adaptive learning algorithm modifies the current plan and sends the generated content to the terminal. 【0176】 Step 6: 【0177】 The server updates a dashboard for teachers, making it easy for them to view learning progress and sentiment data. The input is learning and sentiment data collected on the server, and the output is a visualized interface. Specifically, the server converts the data into charts and graphs and updates the web dashboard displayed in the teacher's browser. 【0178】 (Application Example 2) 【0179】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0180】 Traditional individualized learning support systems could identify students' learning styles and strengths and weaknesses, and provide learning plans based on that information. However, they struggled to dynamically adjust plans to take into account students' emotions and stress levels. Furthermore, in the field of home-based learning support, there is a demand for interactive learning support using user-friendly robots, but current systems do not adequately meet this need. Therefore, there is a need for means to provide students with a better learning experience. 【0181】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0182】 In this invention, the server includes data collection means for recording students' learning activities in real time, emotion recognition means for recognizing emotions from the learner's facial expressions and voice and dynamically adjusting the learning content according to that state, and robot operation means for providing interactive educational content through a home learning support robot. This enables an emotion-based, individually optimized learning process and makes home learning support more effective. 【0183】 "Data collection means" refers to a device or process that records students' learning activities in real time and collects that data for post-processing. 【0184】 "Data analysis means" refers to a device or process for analyzing recorded learning data to identify students' learning styles, strengths, and weaknesses. 【0185】 A "plan creation method" is a device or process that generates an individually optimized learning plan based on the learning style and strengths and weaknesses of identified students. 【0186】 A "feedback mechanism" is a device or process that provides real-time feedback to students during their learning process. 【0187】 An "emotion recognition means" is a device or process that recognizes emotions from a learner's facial expressions and voice, and dynamically adjusts the learning content according to that state. 【0188】 "Robot operation means" refers to a device or process for providing interactive educational content through a learning support robot installed in a home. 【0189】 "Teacher support tools" refer to devices or processes that generate detailed reports used to support teachers. 【0190】 In one embodiment of this invention, a technology is implemented in an educational support system to optimize individual student learning experiences based on emotions. The core of the system consists of a server, terminals, and a learning support robot used in the home. 【0191】 The server uses a data collection device to record students' learning activities and accumulates data in real time. The terminals have the ability to save detected data locally or to cloud storage and send the data to the server as needed. This data is analyzed using Python and related analytical libraries. As a result, each student's learning style, strengths, and weaknesses are identified. 【0192】 Next, the server performs analysis using an emotion recognition engine. It utilizes the camera and microphone on the device to capture and analyze the learner's facial expressions and voice data in real time. This process uses the OpenCV and librosa libraries. 【0193】 The analysis results from the server are also used to control the Home robot. This robot provides user-friendly educational content within the home and dynamically changes the content as learning progresses. The robot uses a generative AI model to provide real-time feedback and more effectively support learning. 【0194】 Specifically, if a student is faced with a difficult math problem and shows a confused expression, the robot will use sensors to detect this emotion and use a prompt such as, "How would you give a simple hint to a 10-year-old who is struggling to figure out how to solve this equation?" to generate hints to help them understand. Based on the generated hints, the robot will provide advice in a friendly voice and play interactive explanatory videos as needed. 【0195】 This embodiment of the invention supports students' learning performance from an emotional perspective and provides teachers and parents with information to intervene effectively. In this way, students experience a more individualized learning process, and an improvement in overall learning effectiveness is expected. 【0196】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0197】 Step 1: 【0198】 The device records the user's learning activity in real time. Specifically, the device's camera and microphone monitor the learning situation, collecting data on screen operations and responses during the learning time. The input is the result of observations of the user's learning situation, and the output is generated in the form of observation data saved locally or in cloud storage. This data is used for subsequent analysis. 【0199】 Step 2: 【0200】 The server analyzes the data sent from the terminal. This specifically involves processing training data using Python's analysis library. The input is training data, which the server uses to identify each student's learning style and areas of strength and weakness. The output is the analysis results, which form the basis of an individually optimized learning plan. 【0201】 Step 3: 【0202】 The device captures students' facial expressions and voices using its camera and microphone, and performs emotion recognition processing. Specifically, it uses libraries such as OpenCV and librosa to process image and audio data and analyze students' emotions. The input is the captured facial and audio data, and the output is the analyzed emotional state information. 【0203】 Step 4: 【0204】 The server adjusts the learning content based on the emotional state. It creates prompts for the generative AI model and generates appropriate learning feedback and learning materials based on them. The input is the learning style information from step 2 and the emotional state information from step 3, and the output is appropriate learning materials and feedback information. 【0205】 Step 5: 【0206】 A terminal or a home-use learning support robot presents the generated learning materials to the user. Specifically, the robot explains to the user in a friendly voice and displays interactive content on the display as needed. The input is learning material information from the server, and the output is the visual and auditory information received by the user. 【0207】 Step 6: 【0208】 The server integrates all processing results and generates a detailed report for teachers. Specifically, it updates the teacher dashboard, organizing data to visually display student learning progress and emotional state. The input is the processing result of each step, and the output is a visualized detailed report. 【0209】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0210】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0211】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0212】 [Second Embodiment] 【0213】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0214】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0215】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0216】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0217】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0218】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0219】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0220】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0221】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0222】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0223】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0224】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0225】 The AI-powered educational support system of the present invention provides an environment that optimizes the learning experience through the collaborative operation of a server, terminals, and users. Embodiments of this system are described below. 【0226】 The system begins with the user (student) accessing the online platform using a device. The device functions as a data collection tool by recording the user's actions in real time and sending the collected data to the server. The server analyzes the received data to identify learning styles and each user's strengths and weaknesses. This process executes the data analysis mechanism. 【0227】 Next, the server generates an individually optimized learning plan based on the analysis results. This is achieved by dynamically incorporating learning materials and tests tailored to each user's needs. The plan creation mechanism functions throughout this process. 【0228】 As the user progresses through the learning process, the device communicates with the server each time a question is answered, instantly determining whether the answer is correct. The server generates and provides feedback to the user based on the correctness of the answer. This feedback is designed to enhance motivation and facilitate understanding. This feedback mechanism is achieved through such immediate responses. 【0229】 Furthermore, the server uses the accumulated data and feedback information to create a detailed report, which is then provided to the teacher. The teacher can use this report to understand the learning progress of individual students and provide effective instruction. In this way, a teacher support system is realized. 【0230】 For example, if a user has weaknesses in a particular math subject, this system will suggest a focused learning plan to reinforce those areas. Furthermore, if a user answers a problem incorrectly, the server immediately analyzes the answer and provides hints for further learning. As a result, users can continue to receive high-quality instruction at their own pace. 【0231】 Thus, the present invention aims to improve the quality of education by providing educational support that meets the individual needs of students. 【0232】 The following describes the processing flow. 【0233】 Step 1: 【0234】 The user logs into the learning platform using their device. After logging in, the user selects an appropriate course and learning materials to begin their studies. From this point on, the device begins recording the user's learning activity. 【0235】 Step 2: 【0236】 The device continuously records learning activities such as the user's viewing time of learning materials, the questions answered, and the scores obtained. This data is transmitted to the server in real time. 【0237】 Step 3: 【0238】 The server analyzes the received training data. Specifically, it analyzes the user's answer patterns and time allocation to identify their learning style and areas of strength and weakness. This result forms the basis for the next step. 【0239】 Step 4: 【0240】 The server generates an individually optimized learning plan based on the analysis results. Specifically, it selects learning materials to overcome the user's weaknesses and incorporates them into the next learning session. The generated plan is sent to the terminal and provided to the user. 【0241】 Step 5: 【0242】 As the user progresses through the learning process, the device sends each answer to the server in real time. The server immediately evaluates these answers and determines their correctness. The results are returned to the user as feedback and displayed on the screen. 【0243】 Step 6: 【0244】 The user receives feedback and has the opportunity to correct mistakes or relearn. The device continues to record the user's activity throughout this process, and this data is then used again by the server for analysis. 【0245】 Step 7: 【0246】 The server integrates all the collected data and generates detailed analytics reports for teachers. This allows teachers to gain an overview of users' learning progress and obtain information that can be used for individualized instruction. 【0247】 (Example 1) 【0248】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0249】 The current education system lacks the flexibility and ability to respond immediately to the individual learning needs of students. This makes it difficult to provide an optimized learning experience for each student, and there is a need to efficiently identify and address each student's strengths and weaknesses. Furthermore, the provision of specific and detailed reports to instructors is insufficient, making it difficult to effectively provide individualized instruction. 【0250】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0251】 In this invention, the server includes information gathering means for recording learners' educational activities in real time, information analysis means for analyzing the recorded educational data and identifying each learner's educational style, strengths, and weaknesses, and planning means for generating individually optimized educational plans based on the identified results. This makes it possible to provide an educational experience optimized for each learner. 【0252】 "Information gathering means" refers to a device or function for recording learners' educational activities in real time and collecting those activities as data. 【0253】 "Information analysis means" refers to a device or function for analyzing recorded educational data to identify each learner's learning style, strengths, and weaknesses. 【0254】 "Plan creation means" refers to a device or function for generating an individually optimized educational plan for learners based on the results of information analysis. 【0255】 A "response mechanism" is a device or function for providing real-time feedback to learners during instruction. 【0256】 "Leadership support tools" refer to devices or functions for generating and providing detailed reports used to support leaders. 【0257】 "Generation means" refers to a device or function that uses an artificial intelligence model to analyze educational data and provide dynamically generated educational plans and feedback. 【0258】 A "plan adjustment tool" is a device or function that analyzes a learner's weaknesses in a specific area based on their answer history and incorporates tasks dealing with that area into the next educational plan. 【0259】 A "display device for instructors" is a device or function that visually displays the educational status and progress of each learner to the instructor. 【0260】 The present invention provides an environment that enables individualized instruction in educational settings. This system involves the collaborative operation of a server, terminals, and users to provide learners with an optimized educational experience. 【0261】 The server collects data related to educational activities transmitted from terminals and functions as an information gathering tool. This involves using terminals such as PCs and tablets used by users. The terminals record user actions and learning processes in real time and transfer the data via internet communication to the server. HTTP and WebSocket protocols are used for this transfer. 【0262】 The server stores the received data in a database and then performs analysis using data analysis tools. The database management systems used include common solutions such as MySQL and MongoDB. For data analysis, AI models are employed, using machine learning libraries such as Python's TensorFlow and Scikit-learn to identify each learner's learning style and areas of strength and weakness. 【0263】 Based on these analysis results, the server utilizes planning tools to generate individually optimized learning plans for each learner. The generated plan dynamically selects necessary materials and tests via the LMS API and delivers them to the user's terminal in real time. Specifically, if weaknesses are found in a particular subject, a learning plan is created to focus on strengthening those areas. Dynamic generation by AI models plays a crucial role in this process. 【0264】 As learners progress through educational activities, the server provides immediate feedback on user responses using a response mechanism. Specifically, when a user submits an answer, the server determines its correctness and, if necessary, suggests areas for improvement or hints for further learning. 【0265】 Furthermore, the server uses instructor support tools to analyze accumulated educational data and feedback information, generating detailed reports and providing them to instructors. Instructors can then utilize these reports to provide appropriate guidance based on the individual learners' situations. 【0266】 As a concrete example, here is an example of a prompt sentence to input into a generative AI model: "If a user is having difficulty memorizing English words, please suggest an appropriate learning plan for that user." 【0267】 Through this system, learners can progress at their own pace, flexibly and effectively, while instructors can provide more precise guidance. In this way, it contributes to improving the quality of education. 【0268】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0269】 Step 1: User Access and Login 【0270】 The user accesses the online platform using their device and begins learning. The device receives the user's login information as input and authenticates them through the authentication system. Upon successful authentication, the device starts a session and prepares to record the user's learning activity. During this process, the device communicates securely with the server using HTTPS. 【0271】 Step 2: Recording user actions and sending data 【0272】 The device continuously records user actions and learning progress in real time. Specifically, this includes data such as page navigation, problem answers, and study time. This data is transmitted sequentially from the device to the server. The transmitted data is stored in a database and used as input for data analysis on the server. 【0273】 Step 3: Data analysis and identification of learning style 【0274】 The server receives data sent from the terminal and stores it in a database. Next, the server analyzes this data using an AI model. Specifically, it analyzes each user's learning pattern and identifies their strengths and weaknesses. For example, by identifying topics in mathematics where the user frequently makes mistakes, it reveals the user's weak points. The results of this analysis become input for the next step. 【0275】 Step 4: Generating an Individually Optimized Learning Plan 【0276】 The server generates an individually optimized learning plan based on the results of data analysis. This plan is a process that takes the analysis results as input and dynamically selects learning materials and tests as output. For example, for a user with weaknesses in a specific area, additional learning materials to strengthen that area will be selected. The generated plan is sent to the user's terminal. 【0277】 Step 5: Providing feedback during learning 【0278】 Each time a user answers a question on their device, the device sends the answer data to the server. The server receives this answer data as input, uses an AI model to determine if it is correct or incorrect, and generates feedback as a response. This feedback is sent to the user's device in real time and provided to the user immediately. For example, it may include specific messages such as "That's correct!" or "Let's think about it again." 【0279】 Step 6: Create and provide a detailed report 【0280】 The server generates detailed reports based on accumulated educational data and feedback information. These reports include each user's learning progress and characteristics. Once generated, these reports are provided to the instructor's display device, allowing instructors to adjust their teaching plans accordingly. This reporting process makes it easier for instructors to visually understand the status of each learner. 【0281】 (Application Example 1) 【0282】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0283】 Providing flexible learning support tailored to each student's individual learning style and weaknesses is challenging in home learning. In particular, the lack of appropriate real-time feedback and insufficient interactive learning support using speech recognition are significant issues. 【0284】 The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means. 【0285】 In this invention, the server includes: a data collection means for recording the learning activities of students in real time; a data analysis means for analyzing the recorded learning data to identify the learning styles, strong fields, and weak fields of each student; and a plan creation means for generating an individually optimized learning plan based on the identified results. This enables real-time feedback and interactive learning support that utilizes voice recognition and data communication. 【0286】 The "data collection means" is a component having a function of observing the learning activities of students in real time and recording data on their operations and progress. 【0287】 The "data analysis means" is a component that performs information processing for identifying the learning styles and strong / weak fields of individual students using the recorded learning data. 【0288】 The "plan creation means" is a component for dynamically generating a customized learning plan for each student based on the results of data analysis. 【0289】 The "feedback means" is a component that provides evaluations and advice in real time according to students' answers and operations to support learning. 【0290】 The "teacher support means" is a component for generating a detailed report summarizing the learning status of students and assisting teachers in their guidance. 【0291】 The "control means" is a component that presents learning tasks according to user instructions by utilizing voice recognition and data communication and provides learning support. 【0292】 The system for implementing this invention is specifically designed to support home learning, and involves the collaborative operation of a server, terminals, and users. 【0293】 First, the server records students' learning activities in real time and analyzes the recorded data through data analysis tools. This identifies each student's learning style, strengths, and weaknesses. Based on the analysis results, a plan creation tool generates individually optimized learning plans. Throughout this entire system, the server plays a central role in managing data collection, analysis, and plan creation. The server and terminals are connected via the internet, and data communication is conducted using the HTTP protocol. 【0294】 Meanwhile, the device uses voice recognition to present learning tasks to students and records their actions and answers. The device is equipped with a voice recognition microphone and a touch interface, allowing it to receive user instructions. It utilizes the Google Speech-to-Text API for voice recognition, enabling real-time and effective interactive learning. Furthermore, the device communicates with a server and has the functionality to provide immediate feedback using feedback mechanisms. This allows students to instantly receive evaluations and hints for improvement. 【0295】 Students, as users, can manage their learning progress and receive efficient learning support by using this system via their devices. For example, if a student enters an incorrect answer while solving a math problem, the server immediately analyzes it and provides feedback via the device, such as "Let's try again." Because the feedback is generated using a generative AI model, more advanced support is possible. 【0296】 An example of a prompt message for a generative AI model might be, "If the user's solution is incorrect, please provide feedback to prompt retraining." 【0297】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0298】 Step 1: 【0299】 The server records in real time the learning activities performed by students through terminals. As input, it receives the user's operation data and answer content, and accumulates the history of learning activities by writing them into the database. 【0300】 Step 2: 【0301】 The server analyzes the data recorded using data analysis means. The input here is the learning data of the students recorded in Step 1. The server applies an analysis algorithm and generates an output that identifies the learning styles and strong / weak areas of each student as the analysis result. 【0302】 Step 3: 【0303】 The server generates an individually optimized learning plan by the plan creation means based on the analysis result. The input for this step is the analysis result of Step 2. The server assembles a customized learning plan programmatically and transmits the generated learning plan to the terminal as output. 【0304】 Step 4: 【0305】 The terminal presents learning tasks to the student using the voice recognition function according to the learning plan. Based on the received learning plan as input, it uses the voice recognition API to present the questions in voice or text and notifies the user of this as output. 【0306】 Step 5: 【0307】 The user answers the presented learning tasks. The user provides an answer to the terminal as input, and that operation is immediately recorded and proceeds to the next server processing. 【0308】 Step 6: 【0309】 The server receives user responses and provides immediate evaluation using a feedback mechanism. The input is user response data, and the server uses a generative AI model to perform the evaluation. Based on the results, it generates immediate feedback, which is then output to the terminal. 【0310】 Step 7: 【0311】 The terminal provides the user with feedback sent from the server. The input is the feedback information output from the server in step 6, and the terminal communicates the feedback to the student through screen display or audio output device. 【0312】 Step 8: 【0313】 The server periodically generates detailed reports based on accumulated learning data and feedback, and reports these to teachers using teacher support tools. The input consists of all recorded data and feedback information, and the server uses a report generation algorithm to output a detailed report of the learning progress. 【0314】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0315】 The present invention enhances the individualized learning experience of students by incorporating an emotion engine into an AI-powered educational support system. In the implementation of this system, the server, terminals, and users interact with each other at a central level. 【0316】 First, the user participates in a learning session using a device. The device tracks the user's learning activity in real time and collects data. This data is continuously transmitted to the server. The server analyzes the received learning data to identify the user's learning style, strengths and weaknesses, and create an individually optimized learning plan based on this information. 【0317】 In addition, this invention incorporates an emotion engine in the terminal, which is responsible for recognizing the user's emotions through facial expressions, tone of voice, and other factors. The server analyzes this emotion information to understand the user's emotional state in real time. The information from the emotion engine is used to immediately adjust the learning plan and feedback content according to the user's stress level and learning attitude. 【0318】 Specifically, if the emotion engine determines that a user is facing difficulties and feeling frustrated during their learning, the server adjusts the learning plan and provides easier-to-understand supplementary materials and step-by-step guidance. The emotion engine also continuously sends the user's emotional state to the server, which then displays this data on a dashboard accessible to teachers. This allows teachers to quickly understand the user's situation and provide appropriate support. 【0319】 Thus, the present invention provides a function that supports students' learning performance and experiences from an emotional perspective, thereby offering an individually optimized learning process and providing information for teachers to intervene effectively. 【0320】 The following describes the processing flow. 【0321】 Step 1: 【0322】 The user accesses the learning session using their device. The device verifies the user's authentication information and grants access to the learning platform. The user selects learning materials appropriate to their learning progress and begins learning. 【0323】 Step 2: 【0324】 The device monitors the user's learning activity in real time and records data such as learning material viewing status, response time, and test scores. This data is sent to the server with the user's permission. 【0325】 Step 3: 【0326】 The device's built-in emotion engine analyzes the user's facial expressions and voice to recognize their emotional state in real time. This allows information such as whether the user is stressed or relaxed to be extracted. 【0327】 Step 4: 【0328】 The server integrates and analyzes the learning data and sentiment data sent from the terminal. Based on this, it evaluates the most effective learning plan based on the user's learning patterns and current emotional state, and redesigns the plan as needed. 【0329】 Step 5: 【0330】 The server adjusts the feedback based on information obtained from the emotion engine. For example, if the user is frustrated, it sends an encouraging message and provides additional resources to explain the problem more clearly. 【0331】 Step 6: 【0332】 The server displays the analysis results and the user's emotional state on a teacher dashboard. Teachers use this information to understand the difficulties and progress the user is facing and plan support, whether in person or online. 【0333】 Step 7: 【0334】 Users learn based on the feedback they receive and receive further feedback through the emotion engine. This allows users to learn at their own pace while receiving support as needed. 【0335】 (Example 2) 【0336】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0337】 While conventional educational support systems record learners' activities and performance, they fail to adequately grasp their emotions and psychological states during these periods and incorporate them into learning plans. This has resulted in the challenge of providing individually optimized learning support. Furthermore, there is a lack of information that allows teachers to quickly understand learners' situations and provide appropriate guidance. 【0338】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0339】 In this invention, the server includes data analysis means, emotion analysis means, and plan adjustment means. This enables the integrated analysis of learner activity data and emotional state, and the adjustment of the learning plan in real time. As a result, an optimized learning experience is provided for each individual learner, and teachers can immediately grasp the situation and provide appropriate guidance. 【0340】 "Data collection means" refers to a device or method for recording learners' learning activities in real time. 【0341】 "Data analysis means" refers to a device or method for analyzing recorded learning data to identify each learner's learning style, strengths, and weaknesses. 【0342】 "Plan creation means" refers to an apparatus or method for generating an individually optimized learning plan based on the analyzed results. 【0343】 "Emotional analysis means" refers to a device or method for recognizing and analyzing a learner's emotional state. 【0344】 A "plan adjustment means" is a device or method for adjusting a learning plan in real time based on emotional information. 【0345】 A "feedback device" is a device or method for providing real-time feedback to learners during their learning process. 【0346】 "Teacher support means" refers to a device or method for generating detailed reports used to support teachers. 【0347】 A "teacher dashboard" is an interface or system for visually displaying the learning status and progress of each student. 【0348】 A "generative AI model" is a model based on artificial intelligence technology used for data analysis and generating learning plans. 【0349】 A "prompt statement" is an input statement used to give instructions or make inquiries to a generative AI model. 【0350】 This invention is an AI-powered educational support system that includes an emotion analysis function to provide learners with an individually optimized learning experience. The method for implementing this system is described below. 【0351】 The server uses a generative AI model to analyze the learner's learning data and sentiment information. Learners participate in learning sessions using a dedicated terminal. The terminal monitors the user's learning activity in real time and collects data such as clicks, answers, and response times. This data is continuously transmitted to the server. 【0352】 The device is equipped with a camera and microphone to detect the user's facial expressions and voice tone. An emotion engine analyzes this data in real time to recognize the user's emotional state. Based on this emotional information, the server adjusts the learning plan and provides the learner with supplementary materials and step-by-step guides. 【0353】 For example, if analysis reveals that a user is experiencing frustration due to a difficult problem, the server uses a generative AI model to generate additional materials to help them understand the problem. In this way, the learner's comprehension can be improved, and the learning experience can be enhanced. 【0354】 The server provides teachers with learner progress and sentiment data through a dashboard, enabling teachers to support learners more effectively. Additionally, an example of a prompt message can be given to the generating AI model: "Please tell me how to change learning materials in real time based on the user's learning attitude." 【0355】 In this way, the present invention is designed to provide learners with a highly individualized learning environment, enabling teachers to provide appropriate support throughout the process. 【0356】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0357】 Step 1: 【0358】 The user starts a learning session using the device. The device records learning activity data in real time, such as the user's actions, inputs, and response times. Inputs are the user's clicks and keyboard inputs, and outputs are these data saved as logs. Specifically, the device sequentially stores the information received from the user interface into a data buffer. 【0359】 Step 2: 【0360】 The device sends the collected learning activity data to the server. The input is the learning data stored on the device, and the output is the data stored in a structured format in a database on the server. Specifically, the device periodically converts the data into packets via batch processing over the internet and sends them to the server's receiving API. 【0361】 Step 3: 【0362】 The server analyzes the received training data. The input is the training data stored in the server's database, and the output is the analysis results showing the user's learning style, strengths, and weaknesses. These results are generated by classifying the data using a generative AI model and applying a pattern recognition algorithm. Specifically, the dataset is processed using data mining techniques, and the generative AI model generates the analysis results. 【0363】 Step 4: 【0364】 The device senses the user's facial expressions and voice tone, collecting emotional data. Input is video and audio data obtained from the camera and microphone, while output is data indicating the user's emotional state. An emotion analysis algorithm analyzes this data to determine the user's emotional state. Specifically, the device periodically samples sensor data and inputs it into the emotion recognition model. 【0365】 Step 5: 【0366】 The server adjusts the learning plan based on sentiment analysis data. The input is the user's sentiment data obtained from sentiment analysis and the learning analysis results, and the output is the adjusted learning plan. The generative AI model considers the emotional state and decides how to improve or supplement the learning content. Specifically, the adaptive learning algorithm modifies the current plan and sends the generated content to the terminal. 【0367】 Step 6: 【0368】 The server updates a dashboard for teachers, making it easy for them to view learning progress and sentiment data. The input is learning and sentiment data collected on the server, and the output is a visualized interface. Specifically, the server converts the data into charts and graphs and updates the web dashboard displayed in the teacher's browser. 【0369】 (Application Example 2) 【0370】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0371】 Traditional individualized learning support systems could identify students' learning styles and strengths and weaknesses, and provide learning plans based on that information. However, they struggled to dynamically adjust plans to take into account students' emotions and stress levels. Furthermore, in the field of home-based learning support, there is a demand for interactive learning support using user-friendly robots, but current systems do not adequately meet this need. Therefore, there is a need for means to provide students with a better learning experience. 【0372】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0373】 In this invention, the server includes data collection means for recording students' learning activities in real time, emotion recognition means for recognizing emotions from the learner's facial expressions and voice and dynamically adjusting the learning content according to that state, and robot operation means for providing interactive educational content through a home learning support robot. This enables an emotion-based, individually optimized learning process and makes home learning support more effective. 【0374】 "Data collection means" refers to a device or process that records students' learning activities in real time and collects that data for post-processing. 【0375】 "Data analysis means" refers to a device or process for analyzing recorded learning data to identify students' learning styles, strengths, and weaknesses. 【0376】 A "plan creation method" is a device or process that generates an individually optimized learning plan based on the learning style and strengths and weaknesses of identified students. 【0377】 A "feedback mechanism" is a device or process that provides real-time feedback to students during their learning process. 【0378】 An "emotion recognition means" is a device or process that recognizes emotions from a learner's facial expressions and voice, and dynamically adjusts the learning content according to that state. 【0379】 "Robot operation means" refers to a device or process for providing interactive educational content through a learning support robot installed in a home. 【0380】 "Teacher support tools" refer to devices or processes that generate detailed reports used to support teachers. 【0381】 In one embodiment of this invention, a technology is implemented in an educational support system to optimize individual student learning experiences based on emotions. The core of the system consists of a server, terminals, and a learning support robot used in the home. 【0382】 The server uses a data collection device to record students' learning activities and accumulates data in real time. The terminals have the ability to save detected data locally or to cloud storage and send the data to the server as needed. This data is analyzed using Python and related analytical libraries. As a result, each student's learning style, strengths, and weaknesses are identified. 【0383】 Next, the server performs analysis using an emotion recognition engine. It utilizes the camera and microphone on the device to capture and analyze the learner's facial expressions and voice data in real time. This process uses the OpenCV and librosa libraries. 【0384】 The analysis results from the server are also used to control the Home robot. This robot provides user-friendly educational content within the home and dynamically changes the content as learning progresses. The robot uses a generative AI model to provide real-time feedback and more effectively support learning. 【0385】 Specifically, if a student is faced with a difficult math problem and shows a confused expression, the robot will use sensors to detect this emotion and use a prompt such as, "How would you give a simple hint to a 10-year-old who is struggling to figure out how to solve this equation?" to generate hints to help them understand. Based on the generated hints, the robot will provide advice in a friendly voice and play interactive explanatory videos as needed. 【0386】 This embodiment of the invention supports students' learning performance from an emotional perspective and provides teachers and parents with information to intervene effectively. In this way, students experience a more individualized learning process, and an improvement in overall learning effectiveness is expected. 【0387】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0388】 Step 1: 【0389】 The device records the user's learning activity in real time. Specifically, the device's camera and microphone monitor the learning situation, collecting data on screen operations and responses during the learning time. The input is the result of observations of the user's learning situation, and the output is generated in the form of observation data saved locally or in cloud storage. This data is used for subsequent analysis. 【0390】 Step 2: 【0391】 The server analyzes the data sent from the terminal. This specifically involves processing training data using Python's analysis library. The input is training data, which the server uses to identify each student's learning style and areas of strength and weakness. The output is the analysis results, which form the basis of an individually optimized learning plan. 【0392】 Step 3: 【0393】 The device captures students' facial expressions and voices using its camera and microphone, and performs emotion recognition processing. Specifically, it uses libraries such as OpenCV and librosa to process image and audio data and analyze students' emotions. The input is the captured facial and audio data, and the output is the analyzed emotional state information. 【0394】 Step 4: 【0395】 The server adjusts the learning content based on the emotional state. It creates prompts for the generative AI model and generates appropriate learning feedback and learning materials based on them. The input is the learning style information from step 2 and the emotional state information from step 3, and the output is appropriate learning materials and feedback information. 【0396】 Step 5: 【0397】 A terminal or a home-use learning support robot presents the generated learning materials to the user. Specifically, the robot explains to the user in a friendly voice and displays interactive content on the display as needed. The input is learning material information from the server, and the output is the visual and auditory information received by the user. 【0398】 Step 6: 【0399】 The server integrates all processing results and generates a detailed report for teachers. Specifically, it updates the teacher dashboard, organizing data to visually display student learning progress and emotional state. The input is the processing result of each step, and the output is a visualized detailed report. 【0400】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0401】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0402】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0403】 [Third Embodiment] 【0404】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0405】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0406】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0407】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0408】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0409】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0410】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0411】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0412】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0413】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0414】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0415】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0416】 The AI-powered educational support system of the present invention provides an environment that optimizes the learning experience through the collaborative operation of a server, terminals, and users. Embodiments of this system are described below. 【0417】 The system begins with the user (student) accessing the online platform using a device. The device functions as a data collection tool by recording the user's actions in real time and sending the collected data to the server. The server analyzes the received data to identify learning styles and each user's strengths and weaknesses. This process executes the data analysis mechanism. 【0418】 Next, the server generates an individually optimized learning plan based on the analysis results. This is achieved by dynamically incorporating learning materials and tests tailored to each user's needs. The plan creation mechanism functions throughout this process. 【0419】 As the user progresses through the learning process, the device communicates with the server each time a question is answered, instantly determining whether the answer is correct. The server generates and provides feedback to the user based on the correctness of the answer. This feedback is designed to enhance motivation and facilitate understanding. This feedback mechanism is achieved through such immediate responses. 【0420】 Furthermore, the server uses the accumulated data and feedback information to create a detailed report, which is then provided to the teacher. The teacher can use this report to understand the learning progress of individual students and provide effective instruction. In this way, a teacher support system is realized. 【0421】 For example, if a user has weaknesses in a particular math subject, this system will suggest a focused learning plan to reinforce those areas. Furthermore, if a user answers a problem incorrectly, the server immediately analyzes the answer and provides hints for further learning. As a result, users can continue to receive high-quality instruction at their own pace. 【0422】 Thus, the present invention aims to improve the quality of education by providing educational support that meets the individual needs of students. 【0423】 The following describes the processing flow. 【0424】 Step 1: 【0425】 The user logs into the learning platform using their device. After logging in, the user selects an appropriate course and learning materials to begin their studies. From this point on, the device begins recording the user's learning activity. 【0426】 Step 2: 【0427】 The device continuously records learning activities such as the user's viewing time of learning materials, the questions answered, and the scores obtained. This data is transmitted to the server in real time. 【0428】 Step 3: 【0429】 The server analyzes the received training data. Specifically, it analyzes the user's answer patterns and time allocation to identify their learning style and areas of strength and weakness. This result forms the basis for the next step. 【0430】 Step 4: 【0431】 The server generates an individually optimized learning plan based on the analysis results. Specifically, it selects learning materials to overcome the user's weaknesses and incorporates them into the next learning session. The generated plan is sent to the terminal and provided to the user. 【0432】 Step 5: 【0433】 As the user progresses through the learning process, the device sends each answer to the server in real time. The server immediately evaluates these answers and determines their correctness. The results are returned to the user as feedback and displayed on the screen. 【0434】 Step 6: 【0435】 The user receives feedback and has the opportunity to correct mistakes or relearn. The device continues to record the user's activity throughout this process, and this data is then used again by the server for analysis. 【0436】 Step 7: 【0437】 The server integrates all the collected data and generates detailed analytics reports for teachers. This allows teachers to gain an overview of users' learning progress and obtain information that can be used for individualized instruction. 【0438】 (Example 1) 【0439】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0440】 The current education system lacks the flexibility and ability to respond immediately to the individual learning needs of students. This makes it difficult to provide an optimized learning experience for each student, and there is a need to efficiently identify and address each student's strengths and weaknesses. Furthermore, the provision of specific and detailed reports to instructors is insufficient, making it difficult to effectively provide individualized instruction. 【0441】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0442】 In this invention, the server includes information gathering means for recording learners' educational activities in real time, information analysis means for analyzing the recorded educational data and identifying each learner's educational style, strengths, and weaknesses, and planning means for generating individually optimized educational plans based on the identified results. This makes it possible to provide an educational experience optimized for each learner. 【0443】 "Information gathering means" refers to a device or function for recording learners' educational activities in real time and collecting those activities as data. 【0444】 "Information analysis means" refers to a device or function for analyzing recorded educational data to identify each learner's learning style, strengths, and weaknesses. 【0445】 "Plan creation means" refers to a device or function for generating an individually optimized educational plan for learners based on the results of information analysis. 【0446】 A "response mechanism" is a device or function for providing real-time feedback to learners during instruction. 【0447】 "Leadership support tools" refer to devices or functions for generating and providing detailed reports used to support leaders. 【0448】 "Generation means" refers to a device or function that uses an artificial intelligence model to analyze educational data and provide dynamically generated educational plans and feedback. 【0449】 A "plan adjustment tool" is a device or function that analyzes a learner's weaknesses in a specific area based on their answer history and incorporates tasks dealing with that area into the next educational plan. 【0450】 A "display device for instructors" is a device or function that visually displays the educational status and progress of each learner to the instructor. 【0451】 The present invention provides an environment that enables individualized instruction in educational settings. This system involves the collaborative operation of a server, terminals, and users to provide learners with an optimized educational experience. 【0452】 The server collects data related to educational activities transmitted from terminals and functions as an information gathering tool. This involves using terminals such as PCs and tablets used by users. The terminals record user actions and learning processes in real time and transfer the data via internet communication to the server. HTTP and WebSocket protocols are used for this transfer. 【0453】 The server stores the received data in a database and then performs analysis using data analysis tools. The database management systems used include common solutions such as MySQL and MongoDB. For data analysis, AI models are employed, using machine learning libraries such as Python's TensorFlow and Scikit-learn to identify each learner's learning style and areas of strength and weakness. 【0454】 Based on these analysis results, the server utilizes planning tools to generate individually optimized learning plans for each learner. The generated plan dynamically selects necessary materials and tests via the LMS API and delivers them to the user's terminal in real time. Specifically, if weaknesses are found in a particular subject, a learning plan is created to focus on strengthening those areas. Dynamic generation by AI models plays a crucial role in this process. 【0455】 As learners progress through educational activities, the server provides immediate feedback on user responses using a response mechanism. Specifically, when a user submits an answer, the server determines its correctness and, if necessary, suggests areas for improvement or hints for further learning. 【0456】 Furthermore, the server uses instructor support tools to analyze accumulated educational data and feedback information, generating detailed reports and providing them to instructors. Instructors can then utilize these reports to provide appropriate guidance based on the individual learners' situations. 【0457】 As a concrete example, here is an example of a prompt sentence to input into a generative AI model: "If a user is having difficulty memorizing English words, please suggest an appropriate learning plan for that user." 【0458】 Through this system, learners can progress at their own pace, flexibly and effectively, while instructors can provide more precise guidance. In this way, it contributes to improving the quality of education. 【0459】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0460】 Step 1: User Access and Login 【0461】 The user accesses the online platform using their device and begins learning. The device receives the user's login information as input and authenticates them through the authentication system. Upon successful authentication, the device starts a session and prepares to record the user's learning activity. During this process, the device communicates securely with the server using HTTPS. 【0462】 Step 2: Recording user actions and sending data 【0463】 The device continuously records user actions and learning progress in real time. Specifically, this includes data such as page navigation, problem answers, and study time. This data is transmitted sequentially from the device to the server. The transmitted data is stored in a database and used as input for data analysis on the server. 【0464】 Step 3: Data analysis and identification of learning style 【0465】 The server receives data sent from the terminal and stores it in a database. Next, the server analyzes this data using an AI model. Specifically, it analyzes each user's learning pattern and identifies their strengths and weaknesses. For example, by identifying topics in mathematics where the user frequently makes mistakes, it reveals the user's weak points. The results of this analysis become input for the next step. 【0466】 Step 4: Generating an Individually Optimized Learning Plan 【0467】 The server generates an individually optimized learning plan based on the results of data analysis. This plan is a process that takes the analysis results as input and dynamically selects learning materials and tests as output. For example, for a user with weaknesses in a specific area, additional learning materials to strengthen that area will be selected. The generated plan is sent to the user's terminal. 【0468】 Step 5: Providing feedback during learning 【0469】 Each time a user answers a question on their device, the device sends the answer data to the server. The server receives this answer data as input, uses an AI model to determine if it is correct or incorrect, and generates feedback as a response. This feedback is sent to the user's device in real time and provided to the user immediately. For example, it may include specific messages such as "That's correct!" or "Let's think about it again." 【0470】 Step 6: Create and provide a detailed report 【0471】 The server generates detailed reports based on accumulated educational data and feedback information. These reports include each user's learning progress and characteristics. Once generated, these reports are provided to the instructor's display device, allowing instructors to adjust their teaching plans accordingly. This reporting process makes it easier for instructors to visually understand the status of each learner. 【0472】 (Application Example 1) 【0473】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0474】 Providing flexible learning support tailored to each student's individual learning style and weaknesses is challenging in home learning. In particular, the lack of appropriate real-time feedback and insufficient interactive learning support using speech recognition are significant issues. 【0475】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0476】 In this invention, the server includes data collection means for recording students' learning activities in real time, data analysis means for analyzing the recorded learning data to identify each student's learning style, strengths, and weaknesses, and plan creation means for generating individually optimized learning plans based on the identified results. This enables real-time feedback and interactive learning support utilizing speech recognition and data communication. 【0477】 A "data collection method" is a component that has the function of observing students' learning activities in real time and recording data on their actions and progress. 【0478】 "Data analysis means" refers to components that perform information processing using recorded learning data to identify individual students' learning styles and areas of strength and weakness. 【0479】 "Plan creation means" refers to the components for dynamically generating customized learning plans for each student based on the results of data analysis. 【0480】 A "feedback mechanism" is a component that provides real-time evaluation and advice in response to students' answers and actions, thereby supporting their learning. 【0481】 "Teacher support tools" are components that generate detailed reports summarizing students' learning progress and provide teachers with assistance in instruction. 【0482】 A "control means" is a component that utilizes speech recognition and data communication to present learning tasks in response to user instructions and provide learning support. 【0483】 The system for implementing this invention is specifically designed to support home learning, and involves the collaborative operation of a server, terminals, and users. 【0484】 First, the server records students' learning activities in real time and analyzes the recorded data through data analysis tools. This identifies each student's learning style, strengths, and weaknesses. Based on the analysis results, a plan creation tool generates individually optimized learning plans. Throughout this entire system, the server plays a central role in managing data collection, analysis, and plan creation. The server and terminals are connected via the internet, and data communication is conducted using the HTTP protocol. 【0485】 Meanwhile, the device uses voice recognition to present learning tasks to students and records their actions and answers. The device is equipped with a voice recognition microphone and a touch interface, allowing it to receive user instructions. It utilizes the Google Speech-to-Text API for voice recognition, enabling real-time and effective interactive learning. Furthermore, the device communicates with a server and has the functionality to provide immediate feedback using feedback mechanisms. This allows students to instantly receive evaluations and hints for improvement. 【0486】 Students, as users, can manage their learning progress and receive efficient learning support by using this system via their devices. For example, if a student enters an incorrect answer while solving a math problem, the server immediately analyzes it and provides feedback via the device, such as "Let's try again." Because the feedback is generated using a generative AI model, more advanced support is possible. 【0487】 An example of a prompt message for a generative AI model might be, "If the user's solution is incorrect, please provide feedback to prompt retraining." 【0488】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0489】 Step 1: 【0490】 The server records students' learning activities in real time through their devices. It receives user operation data and responses as input and writes them to a database to accumulate a history of learning activities. 【0491】 Step 2: 【0492】 The server analyzes the recorded data using data analysis tools. The input here is the student learning data recorded in step 1. The server applies an analysis algorithm and generates output that identifies each student's learning style and areas of strength and weakness as the analysis result. 【0493】 Step 3: 【0494】 The server generates an individually optimized learning plan using a plan creation mechanism based on the analysis results. The input for this step is the analysis results from step 2, and the server programmatically assembles the customized learning plan and sends the generated learning plan to the terminal as output. 【0495】 Step 4: 【0496】 The device uses speech recognition to present learning tasks to students according to their learning plan. Based on the learning plan received as input, it uses a speech recognition API to present the questions in either voice or text format and notifies the user of this information as output. 【0497】 Step 5: 【0498】 The user answers the presented learning tasks. The user provides the answers to the terminal as input, and this operation is immediately recorded and proceeds to the next server processing step. 【0499】 Step 6: 【0500】 The server receives user responses and provides immediate evaluation using a feedback mechanism. The input is user response data, and the server uses a generative AI model to perform the evaluation. Based on the results, it generates immediate feedback, which is then output to the terminal. 【0501】 Step 7: 【0502】 The terminal provides the user with feedback sent from the server. The input is the feedback information output from the server in step 6, and the terminal communicates the feedback to the student through screen display and audio output devices. 【0503】 Step 8: 【0504】 The server periodically generates detailed reports based on accumulated learning data and feedback, and reports these to teachers using teacher support tools. The input consists of all recorded data and feedback information, and the server uses a report generation algorithm to output a detailed report of the learning progress. 【0505】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0506】 The present invention enhances the individualized learning experience of students by incorporating an emotion engine into an AI-powered educational support system. In the implementation of this system, the server, terminals, and users interact with each other at a central level. 【0507】 First, the user participates in a learning session using a device. The device tracks the user's learning activity in real time and collects data. This data is continuously transmitted to the server. The server analyzes the received learning data to identify the user's learning style, strengths and weaknesses, and create an individually optimized learning plan based on this information. 【0508】 In addition, this invention incorporates an emotion engine in the terminal, which is responsible for recognizing the user's emotions through facial expressions, tone of voice, and other factors. The server analyzes this emotion information to understand the user's emotional state in real time. The information from the emotion engine is used to immediately adjust the learning plan and feedback content according to the user's stress level and learning attitude. 【0509】 Specifically, if the emotion engine determines that a user is facing difficulties and feeling frustrated during their learning, the server adjusts the learning plan and provides easier-to-understand supplementary materials and step-by-step guidance. The emotion engine also continuously sends the user's emotional state to the server, which then displays this data on a dashboard accessible to teachers. This allows teachers to quickly understand the user's situation and provide appropriate support. 【0510】 Thus, the present invention provides a function that supports students' learning performance and experiences from an emotional perspective, thereby offering an individually optimized learning process and providing information for teachers to intervene effectively. 【0511】 The following describes the processing flow. 【0512】 Step 1: 【0513】 The user accesses the learning session using their device. The device verifies the user's authentication information and grants access to the learning platform. The user selects learning materials appropriate to their learning progress and begins learning. 【0514】 Step 2: 【0515】 The device monitors the user's learning activity in real time and records data such as material viewing status, response time, and test scores. This data is sent to the server with the user's permission. 【0516】 Step 3: 【0517】 The device's built-in emotion engine analyzes the user's facial expressions and voice to recognize their emotional state in real time. This allows information such as whether the user is stressed or relaxed to be extracted. 【0518】 Step 4: 【0519】 The server integrates and analyzes the learning data and sentiment data sent from the terminal. Based on this, it evaluates the most effective learning plan based on the user's learning patterns and current emotional state, and redesigns the plan as needed. 【0520】 Step 5: 【0521】 The server adjusts the feedback based on information obtained from the emotion engine. For example, if the user is frustrated, it sends an encouraging message and provides additional resources to explain the problem more clearly. 【0522】 Step 6: 【0523】 The server displays the analysis results and the user's emotional state on a teacher dashboard. Teachers use this information to understand the difficulties and progress the user is facing and plan support, whether in person or online. 【0524】 Step 7: 【0525】 Users learn based on the feedback they receive and receive further feedback through the emotion engine. This allows users to learn at their own pace while receiving support as needed. 【0526】 (Example 2) 【0527】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0528】 While conventional educational support systems record learners' activities and performance, they fail to adequately grasp their emotions and psychological states during these periods and incorporate them into learning plans. This has resulted in the challenge of providing individually optimized learning support. Furthermore, there is a lack of information that allows teachers to quickly understand learners' situations and provide appropriate guidance. 【0529】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0530】 In this invention, the server includes data analysis means, emotion analysis means, and plan adjustment means. This enables the integrated analysis of learner activity data and emotional state, and the adjustment of the learning plan in real time. As a result, an optimized learning experience is provided for each individual learner, and teachers can immediately grasp the situation and provide appropriate guidance. 【0531】 "Data collection means" refers to a device or method for recording learners' learning activities in real time. 【0532】 "Data analysis means" refers to a device or method for analyzing recorded learning data to identify each learner's learning style, strengths, and weaknesses. 【0533】 "Plan creation means" refers to an apparatus or method for generating an individually optimized learning plan based on the analyzed results. 【0534】 "Emotional analysis means" refers to a device or method for recognizing and analyzing a learner's emotional state. 【0535】 A "plan adjustment means" is a device or method for adjusting a learning plan in real time based on emotional information. 【0536】 A "feedback device" is a device or method for providing real-time feedback to learners during their learning process. 【0537】 "Teacher support means" refers to a device or method for generating detailed reports used to support teachers. 【0538】 A "teacher dashboard" is an interface or system for visually displaying the learning status and progress of each student. 【0539】 A "generative AI model" is a model based on artificial intelligence technology used for data analysis and generating learning plans. 【0540】 A "prompt statement" is an input statement used to give instructions or make inquiries to a generative AI model. 【0541】 This invention is an AI-powered educational support system that includes an emotion analysis function to provide learners with an individually optimized learning experience. The method for implementing this system is described below. 【0542】 The server uses a generative AI model to analyze the learner's learning data and sentiment information. Learners participate in learning sessions using a dedicated terminal. The terminal monitors the user's learning activity in real time and collects data such as clicks, answers, and response times. This data is continuously transmitted to the server. 【0543】 The device is equipped with a camera and microphone to detect the user's facial expressions and voice tone. An emotion engine analyzes this data in real time to recognize the user's emotional state. Based on this emotional information, the server adjusts the learning plan and provides the learner with supplementary materials and step-by-step guides. 【0544】 For example, if analysis reveals that a user is experiencing frustration due to a difficult problem, the server uses a generative AI model to generate additional materials to help them understand the problem. In this way, the learner's comprehension can be improved, and the learning experience can be enhanced. 【0545】 The server provides teachers with learner progress and sentiment data through a dashboard, enabling teachers to support learners more effectively. Additionally, an example of a prompt message can be given to the generating AI model: "Please tell me how to change learning materials in real time based on the user's learning attitude." 【0546】 In this way, the present invention is designed to provide learners with a highly individualized learning environment, enabling teachers to provide appropriate support throughout the process. 【0547】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0548】 Step 1: 【0549】 The user starts a learning session using the device. The device records learning activity data in real time, such as the user's actions, inputs, and response times. Inputs are the user's clicks and keyboard inputs, and outputs are these data saved as logs. Specifically, the device sequentially stores the information received from the user interface into a data buffer. 【0550】 Step 2: 【0551】 The device sends the collected learning activity data to the server. The input is the learning data stored on the device, and the output is the data stored in a structured format in a database on the server. Specifically, the device periodically converts the data into packets via batch processing over the internet and sends them to the server's receiving API. 【0552】 Step 3: 【0553】 The server analyzes the received training data. The input is the training data stored in the server's database, and the output is the analysis results showing the user's learning style, strengths, and weaknesses. These results are generated by classifying the data using a generative AI model and applying a pattern recognition algorithm. Specifically, the dataset is processed using data mining techniques, and the generative AI model generates the analysis results. 【0554】 Step 4: 【0555】 The device senses the user's facial expressions and voice tone, collecting emotional data. Input is video and audio data obtained from the camera and microphone, while output is data indicating the user's emotional state. An emotion analysis algorithm analyzes this data to determine the user's emotional state. Specifically, the device periodically samples sensor data and inputs it into the emotion recognition model. 【0556】 Step 5: 【0557】 The server adjusts the learning plan based on sentiment analysis data. The input is the user's sentiment data obtained from sentiment analysis and the learning analysis results, and the output is the adjusted learning plan. The generative AI model considers the emotional state and decides how to improve or supplement the learning content. Specifically, the adaptive learning algorithm modifies the current plan and sends the generated content to the terminal. 【0558】 Step 6: 【0559】 The server updates a dashboard for teachers, making it easy for them to view learning progress and sentiment data. The input is learning and sentiment data collected on the server, and the output is a visualized interface. Specifically, the server converts the data into charts and graphs and updates the web dashboard displayed in the teacher's browser. 【0560】 (Application Example 2) 【0561】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0562】 Traditional individualized learning support systems could identify students' learning styles and strengths and weaknesses, and provide learning plans based on that information. However, they struggled to dynamically adjust plans to take into account students' emotions and stress levels. Furthermore, in the field of home-based learning support, there is a demand for interactive learning support using user-friendly robots, but current systems do not adequately meet this need. Therefore, there is a need for means to provide students with a better learning experience. 【0563】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0564】 In this invention, the server includes data collection means for recording students' learning activities in real time, emotion recognition means for recognizing emotions from the learner's facial expressions and voice and dynamically adjusting the learning content according to that state, and robot operation means for providing interactive educational content through a home learning support robot. This enables an emotion-based, individually optimized learning process and makes home learning support more effective. 【0565】 "Data collection means" refers to a device or process that records students' learning activities in real time and collects that data for post-processing. 【0566】 "Data analysis means" refers to a device or process for analyzing recorded learning data to identify students' learning styles, strengths, and weaknesses. 【0567】 A "plan creation method" is a device or process that generates an individually optimized learning plan based on the learning style and strengths and weaknesses of identified students. 【0568】 A "feedback mechanism" is a device or process that provides real-time feedback to students during their learning process. 【0569】 An "emotion recognition means" is a device or process that recognizes emotions from a learner's facial expressions and voice, and dynamically adjusts the learning content according to that state. 【0570】 "Robot operation means" refers to a device or process for providing interactive educational content through a learning support robot installed in a home. 【0571】 "Teacher support tools" refer to devices or processes that generate detailed reports used to support teachers. 【0572】 In one embodiment of this invention, a technology is implemented in an educational support system to optimize individual student learning experiences based on emotions. The core of the system consists of a server, terminals, and a learning support robot used in the home. 【0573】 The server uses a data collection device to record students' learning activities and accumulates data in real time. The terminals have the ability to save detected data locally or to cloud storage and send the data to the server as needed. This data is analyzed using Python and related analytical libraries. As a result, each student's learning style, strengths, and weaknesses are identified. 【0574】 Next, the server performs analysis using an emotion recognition engine. It utilizes the camera and microphone on the device to capture and analyze the learner's facial expressions and voice data in real time. This process uses the OpenCV and librosa libraries. 【0575】 The analysis results from the server are also used to control the Home robot. This robot provides user-friendly educational content within the home and dynamically changes the content as learning progresses. The robot uses a generative AI model to provide real-time feedback and more effectively support learning. 【0576】 Specifically, if a student is faced with a difficult math problem and shows a confused expression, the robot will use sensors to detect this emotion and use a prompt such as, "How would you give a simple hint to a 10-year-old who is struggling to figure out how to solve this equation?" to generate hints to help them understand. Based on the generated hints, the robot will provide advice in a friendly voice and play interactive explanatory videos as needed. 【0577】 This embodiment of the invention supports students' learning performance from an emotional perspective and provides teachers and parents with information to intervene effectively. In this way, students experience a more individualized learning process, and an improvement in overall learning effectiveness is expected. 【0578】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0579】 Step 1: 【0580】 The device records the user's learning activity in real time. Specifically, the device's camera and microphone monitor the learning situation, collecting data on screen operations and responses during the learning time. The input is the result of observations of the user's learning situation, and the output is generated in the form of observation data saved locally or in cloud storage. This data is used for subsequent analysis. 【0581】 Step 2: 【0582】 The server analyzes the data sent from the terminal. This specifically involves processing training data using Python's analysis library. The input is training data, which the server uses to identify each student's learning style and areas of strength and weakness. The output is the analysis results, which form the basis of an individually optimized learning plan. 【0583】 Step 3: 【0584】 The device captures students' facial expressions and voices using its camera and microphone, and performs emotion recognition processing. Specifically, it uses libraries such as OpenCV and librosa to process image and audio data and analyze students' emotions. The input is the captured facial and audio data, and the output is the analyzed emotional state information. 【0585】 Step 4: 【0586】 The server adjusts the learning content based on the emotional state. It creates prompts for the generative AI model and generates appropriate learning feedback and learning materials based on them. The input is the learning style information from step 2 and the emotional state information from step 3, and the output is appropriate learning materials and feedback information. 【0587】 Step 5: 【0588】 A terminal or a home-use learning support robot presents the generated learning materials to the user. Specifically, the robot explains to the user in a friendly voice and displays interactive content on the display as needed. The input is learning material information from the server, and the output is the visual and auditory information received by the user. 【0589】 Step 6: 【0590】 The server integrates all processing results and generates a detailed report for teachers. Specifically, it updates the teacher dashboard, organizing data to visually display student learning progress and emotional state. The input is the processing result of each step, and the output is a visualized detailed report. 【0591】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0592】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0593】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0594】 [Fourth Embodiment] 【0595】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0596】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0597】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0598】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0599】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0600】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0601】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0602】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0603】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0604】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0605】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0606】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0607】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0608】 The AI-powered educational support system of the present invention provides an environment that optimizes the learning experience through the collaborative operation of a server, terminals, and users. Embodiments of this system are described below. 【0609】 The system begins with the user (student) accessing the online platform using a device. The device functions as a data collection tool by recording the user's actions in real time and sending the collected data to the server. The server analyzes the received data to identify learning styles and each user's strengths and weaknesses. This process executes the data analysis mechanism. 【0610】 Next, the server generates an individually optimized learning plan based on the analysis results. This is achieved by dynamically incorporating learning materials and tests tailored to each user's needs. The plan creation mechanism functions throughout this process. 【0611】 As the user progresses through the learning process, the device communicates with the server each time a question is answered, instantly determining whether the answer is correct. The server generates and provides feedback to the user based on the correctness of the answer. This feedback is designed to enhance motivation and facilitate understanding. This feedback mechanism is achieved through such immediate responses. 【0612】 Furthermore, the server uses the accumulated data and feedback information to create a detailed report, which is then provided to the teacher. The teacher can use this report to understand the learning progress of individual students and provide effective instruction. In this way, a teacher support system is realized. 【0613】 For example, if a user has weaknesses in a particular math subject, this system will suggest a focused learning plan to reinforce those areas. Furthermore, if a user answers a problem incorrectly, the server immediately analyzes the answer and provides hints for further learning. As a result, users can continue to receive high-quality instruction at their own pace. 【0614】 Thus, the present invention aims to improve the quality of education by providing educational support that meets the individual needs of students. 【0615】 The following describes the processing flow. 【0616】 Step 1: 【0617】 The user logs into the learning platform using their device. After logging in, the user selects an appropriate course and learning materials to begin their studies. From this point on, the device begins recording the user's learning activity. 【0618】 Step 2: 【0619】 The device continuously records learning activities such as the user's viewing time of learning materials, the questions answered, and the scores obtained. This data is transmitted to the server in real time. 【0620】 Step 3: 【0621】 The server analyzes the received training data. Specifically, it analyzes the user's answer patterns and time allocation to identify their learning style and areas of strength and weakness. This result forms the basis for the next step. 【0622】 Step 4: 【0623】 The server generates an individually optimized learning plan based on the analysis results. Specifically, it selects learning materials to overcome the user's weaknesses and incorporates them into the next learning session. The generated plan is sent to the terminal and provided to the user. 【0624】 Step 5: 【0625】 As the user progresses through the learning process, the device sends each answer to the server in real time. The server immediately evaluates these answers and determines their correctness. The results are returned to the user as feedback and displayed on the screen. 【0626】 Step 6: 【0627】 The user receives feedback and has the opportunity to correct mistakes or relearn. The device continues to record the user's activity throughout this process, and this data is then used again by the server for analysis. 【0628】 Step 7: 【0629】 The server integrates all the collected data and generates detailed analytics reports for teachers. This allows teachers to gain an overview of users' learning progress and obtain information that can be used for individualized instruction. 【0630】 (Example 1) 【0631】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0632】 The current education system lacks the flexibility and ability to respond immediately to the individual learning needs of students. This makes it difficult to provide an optimized learning experience for each student, and there is a need to efficiently identify and address each student's strengths and weaknesses. Furthermore, the provision of specific and detailed reports to instructors is insufficient, making it difficult to effectively provide individualized instruction. 【0633】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0634】 In this invention, the server includes information gathering means for recording learners' educational activities in real time, information analysis means for analyzing the recorded educational data and identifying each learner's educational style, strengths, and weaknesses, and planning means for generating individually optimized educational plans based on the identified results. This makes it possible to provide an educational experience optimized for each learner. 【0635】 "Information gathering means" refers to a device or function for recording learners' educational activities in real time and collecting those activities as data. 【0636】 "Information analysis means" refers to a device or function for analyzing recorded educational data to identify each learner's learning style, strengths, and weaknesses. 【0637】 "Plan creation means" refers to a device or function for generating an individually optimized educational plan for learners based on the results of information analysis. 【0638】 A "response mechanism" is a device or function for providing real-time feedback to learners during instruction. 【0639】 "Leadership support tools" refer to devices or functions for generating and providing detailed reports used to support leaders. 【0640】 "Generation means" refers to a device or function that uses an artificial intelligence model to analyze educational data and provide dynamically generated educational plans and feedback. 【0641】 A "plan adjustment tool" is a device or function that analyzes a learner's weaknesses in a specific area based on their answer history and incorporates tasks dealing with that area into the next educational plan. 【0642】 A "display device for instructors" is a device or function that visually displays the educational status and progress of each learner to the instructor. 【0643】 The present invention provides an environment that enables individualized instruction in educational settings. This system involves the collaborative operation of a server, terminals, and users to provide learners with an optimized educational experience. 【0644】 The server collects data related to educational activities transmitted from terminals and functions as an information gathering tool. This involves using terminals such as PCs and tablets used by users. The terminals record user actions and learning processes in real time and transfer the data via internet communication to the server. HTTP and WebSocket protocols are used for this transfer. 【0645】 The server stores the received data in a database and then performs analysis using data analysis tools. The database management systems used include common solutions such as MySQL and MongoDB. For data analysis, AI models are employed, using machine learning libraries such as Python's TensorFlow and Scikit-learn to identify each learner's learning style and areas of strength and weakness. 【0646】 Based on these analysis results, the server utilizes planning tools to generate individually optimized learning plans for each learner. The generated plan dynamically selects necessary materials and tests via the LMS API and delivers them to the user's terminal in real time. Specifically, if weaknesses are found in a particular subject, a learning plan is created to focus on strengthening those areas. Dynamic generation by AI models plays a crucial role in this process. 【0647】 As learners progress through educational activities, the server provides immediate feedback on user responses using a response mechanism. Specifically, when a user submits an answer, the server determines its correctness and, if necessary, suggests areas for improvement or hints for further learning. 【0648】 Furthermore, the server uses instructor support tools to analyze accumulated educational data and feedback information, generating detailed reports and providing them to instructors. Instructors can then utilize these reports to provide appropriate guidance based on the individual learners' situations. 【0649】 As a concrete example, here is an example of a prompt sentence to input into a generative AI model: "If a user is having difficulty memorizing English words, please suggest an appropriate learning plan for that user." 【0650】 Through this system, learners can progress at their own pace, flexibly and effectively, while instructors can provide more precise guidance. In this way, it contributes to improving the quality of education. 【0651】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0652】 Step 1: User Access and Login 【0653】 The user accesses the online platform using their device and begins learning. The device receives the user's login information as input and authenticates them through the authentication system. Upon successful authentication, the device starts a session and prepares to record the user's learning activity. During this process, the device communicates securely with the server using HTTPS. 【0654】 Step 2: Recording user actions and sending data 【0655】 The device continuously records user actions and learning progress in real time. Specifically, this includes data such as page navigation, problem answers, and study time. This data is transmitted sequentially from the device to the server. The transmitted data is stored in a database and used as input for data analysis on the server. 【0656】 Step 3: Data analysis and identification of learning style 【0657】 The server receives data sent from the terminal and stores it in a database. Next, the server analyzes this data using an AI model. Specifically, it analyzes each user's learning pattern and identifies their strengths and weaknesses. For example, by identifying topics in mathematics where the user frequently makes mistakes, it reveals the user's weak points. The results of this analysis become input for the next step. 【0658】 Step 4: Generating an Individually Optimized Learning Plan 【0659】 The server generates an individually optimized learning plan based on the results of data analysis. This plan is a process that takes the analysis results as input and dynamically selects learning materials and tests as output. For example, for a user with weaknesses in a specific area, additional learning materials to strengthen that area will be selected. The generated plan is sent to the user's terminal. 【0660】 Step 5: Providing feedback during learning 【0661】 Each time a user answers a question on their device, the device sends the answer data to the server. The server receives this answer data as input, uses an AI model to determine if it is correct or incorrect, and generates feedback as a response. This feedback is sent to the user's device in real time and provided to the user immediately. For example, it may include specific messages such as "That's correct!" or "Let's think about it again." 【0662】 Step 6: Create and provide a detailed report 【0663】 The server generates detailed reports based on accumulated educational data and feedback information. These reports include each user's learning progress and characteristics. Once generated, these reports are provided to the instructor's display device, allowing instructors to adjust their teaching plans accordingly. This reporting process makes it easier for instructors to visually understand the status of each learner. 【0664】 (Application Example 1) 【0665】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0666】 Providing flexible learning support tailored to each student's individual learning style and weaknesses is challenging in home learning. In particular, the lack of appropriate real-time feedback and insufficient interactive learning support using speech recognition are significant issues. 【0667】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0668】 In this invention, the server includes data collection means for recording students' learning activities in real time, data analysis means for analyzing the recorded learning data to identify each student's learning style, strengths, and weaknesses, and plan creation means for generating individually optimized learning plans based on the identified results. This enables real-time feedback and interactive learning support utilizing speech recognition and data communication. 【0669】 A "data collection method" is a component that has the function of observing students' learning activities in real time and recording data on their actions and progress. 【0670】 "Data analysis means" refers to components that perform information processing using recorded learning data to identify individual students' learning styles and areas of strength and weakness. 【0671】 "Plan creation means" refers to the components for dynamically generating customized learning plans for each student based on the results of data analysis. 【0672】 A "feedback mechanism" is a component that provides real-time evaluation and advice in response to students' answers and actions, thereby supporting their learning. 【0673】 "Teacher support tools" are components that generate detailed reports summarizing students' learning progress and provide teachers with assistance in instruction. 【0674】 A "control means" is a component that utilizes speech recognition and data communication to present learning tasks in response to user instructions and provide learning support. 【0675】 The system for implementing this invention is specifically designed to support home learning, and involves the collaborative operation of a server, terminals, and users. 【0676】 First, the server records students' learning activities in real time and analyzes the recorded data through data analysis tools. This identifies each student's learning style, strengths, and weaknesses. Based on the analysis results, a plan creation tool generates individually optimized learning plans. Throughout this entire system, the server plays a central role in managing data collection, analysis, and plan creation. The server and terminals are connected via the internet, and data communication is conducted using the HTTP protocol. 【0677】 Meanwhile, the device uses voice recognition to present learning tasks to students and records their actions and answers. The device is equipped with a voice recognition microphone and a touch interface, allowing it to receive user instructions. It utilizes the Google Speech-to-Text API for voice recognition, enabling real-time and effective interactive learning. Furthermore, the device communicates with a server and has the functionality to provide immediate feedback using feedback mechanisms. This allows students to instantly receive evaluations and hints for improvement. 【0678】 Students, as users, can manage their learning progress and receive efficient learning support by using this system via their devices. For example, if a student enters an incorrect answer while solving a math problem, the server immediately analyzes it and provides feedback via the device, such as "Let's try again." Because the feedback is generated using a generative AI model, more advanced support is possible. 【0679】 An example of a prompt message for a generative AI model might be, "If the user's solution is incorrect, please provide feedback to prompt retraining." 【0680】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0681】 Step 1: 【0682】 The server records students' learning activities in real time through their devices. It receives user operation data and responses as input and writes them to a database to accumulate a history of learning activities. 【0683】 Step 2: 【0684】 The server analyzes the recorded data using data analysis tools. The input here is the student learning data recorded in step 1. The server applies an analysis algorithm and generates output that identifies each student's learning style and areas of strength and weakness as the analysis result. 【0685】 Step 3: 【0686】 The server generates an individually optimized learning plan using a plan creation mechanism based on the analysis results. The input for this step is the analysis results from step 2, and the server programmatically assembles the customized learning plan and sends the generated learning plan to the terminal as output. 【0687】 Step 4: 【0688】 The device uses speech recognition to present learning tasks to students according to their learning plan. Based on the learning plan received as input, it uses a speech recognition API to present the questions in either voice or text format and notifies the user of this information as output. 【0689】 Step 5: 【0690】 The user answers the presented learning tasks. The user provides the answers to the terminal as input, and this operation is immediately recorded and proceeds to the next server processing step. 【0691】 Step 6: 【0692】 The server receives user responses and provides immediate evaluation using a feedback mechanism. The input is user response data, and the server uses a generative AI model to perform the evaluation. Based on the results, it generates immediate feedback, which is then output to the terminal. 【0693】 Step 7: 【0694】 The terminal provides the user with feedback sent from the server. The input is the feedback information output from the server in step 6, and the terminal communicates the feedback to the student through screen display and audio output devices. 【0695】 Step 8: 【0696】 The server periodically generates detailed reports based on accumulated learning data and feedback, and reports these to teachers using teacher support tools. The input consists of all recorded data and feedback information, and the server uses a report generation algorithm to output a detailed report of the learning progress. 【0697】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0698】 The present invention enhances the individualized learning experience of students by incorporating an emotion engine into an AI-powered educational support system. In the implementation of this system, the server, terminals, and users interact with each other at a central level. 【0699】 First, the user participates in a learning session using a device. The device tracks the user's learning activity in real time and collects data. This data is continuously transmitted to the server. The server analyzes the received learning data to identify the user's learning style, strengths and weaknesses, and create an individually optimized learning plan based on this information. 【0700】 In addition, this invention incorporates an emotion engine in the terminal, which is responsible for recognizing the user's emotions through facial expressions, tone of voice, and other factors. The server analyzes this emotion information to understand the user's emotional state in real time. The information from the emotion engine is used to immediately adjust the learning plan and feedback content according to the user's stress level and learning attitude. 【0701】 Specifically, if the emotion engine determines that a user is facing difficulties and feeling frustrated during their learning, the server adjusts the learning plan and provides easier-to-understand supplementary materials and step-by-step guidance. The emotion engine also continuously sends the user's emotional state to the server, which then displays this data on a dashboard accessible to teachers. This allows teachers to quickly understand the user's situation and provide appropriate support. 【0702】 Thus, the present invention provides a function that supports students' learning performance and experiences from an emotional perspective, thereby offering an individually optimized learning process and providing information for teachers to intervene effectively. 【0703】 The following describes the processing flow. 【0704】 Step 1: 【0705】 The user accesses the learning session using their device. The device verifies the user's authentication information and grants access to the learning platform. The user selects learning materials appropriate to their learning progress and begins learning. 【0706】 Step 2: 【0707】 The device monitors the user's learning activity in real time and records data such as material viewing status, response time, and test scores. This data is sent to the server with the user's permission. 【0708】 Step 3: 【0709】 The device's built-in emotion engine analyzes the user's facial expressions and voice to recognize their emotional state in real time. This allows information such as whether the user is stressed or relaxed to be extracted. 【0710】 Step 4: 【0711】 The server integrates and analyzes the learning data and sentiment data sent from the terminal. Based on this, it evaluates the most effective learning plan based on the user's learning patterns and current emotional state, and redesigns the plan as needed. 【0712】 Step 5: 【0713】 The server adjusts the feedback based on information obtained from the emotion engine. For example, if the user is frustrated, it sends an encouraging message and provides additional resources to explain the problem more clearly. 【0714】 Step 6: 【0715】 The server displays the analysis results and the user's emotional state on a teacher dashboard. Teachers use this information to understand the difficulties and progress the user is facing and plan support, whether in person or online. 【0716】 Step 7: 【0717】 Users learn based on the feedback they receive and receive further feedback through the emotion engine. This allows users to learn at their own pace while receiving support as needed. 【0718】 (Example 2) 【0719】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0720】 While conventional educational support systems record learners' activities and performance, they fail to adequately grasp their emotions and psychological states during these periods and incorporate them into learning plans. This has resulted in the challenge of providing individually optimized learning support. Furthermore, there is a lack of information that allows teachers to quickly understand learners' situations and provide appropriate guidance. 【0721】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0722】 In this invention, the server includes data analysis means, emotion analysis means, and plan adjustment means. This enables the integrated analysis of learner activity data and emotional state, and the adjustment of the learning plan in real time. As a result, an optimized learning experience is provided for each individual learner, and teachers can immediately grasp the situation and provide appropriate guidance. 【0723】 "Data collection means" refers to a device or method for recording learners' learning activities in real time. 【0724】 "Data analysis means" refers to a device or method for analyzing recorded learning data to identify each learner's learning style, strengths, and weaknesses. 【0725】 "Plan creation means" refers to an apparatus or method for generating an individually optimized learning plan based on the analyzed results. 【0726】 "Emotional analysis means" refers to a device or method for recognizing and analyzing a learner's emotional state. 【0727】 A "plan adjustment means" is a device or method for adjusting a learning plan in real time based on emotional information. 【0728】 A "feedback device" is a device or method for providing real-time feedback to learners during their learning process. 【0729】 "Teacher support means" refers to a device or method for generating detailed reports used to support teachers. 【0730】 A "teacher dashboard" is an interface or system for visually displaying the learning status and progress of each student. 【0731】 A "generative AI model" is a model based on artificial intelligence technology used for data analysis and generating learning plans. 【0732】 A "prompt statement" is an input statement used to give instructions or make inquiries to a generative AI model. 【0733】 This invention is an AI-powered educational support system that includes an emotion analysis function to provide learners with an individually optimized learning experience. The method for implementing this system is described below. 【0734】 The server uses a generative AI model to analyze the learner's learning data and sentiment information. Learners participate in learning sessions using a dedicated terminal. The terminal monitors the user's learning activity in real time and collects data such as clicks, answers, and response times. This data is continuously transmitted to the server. 【0735】 The device is equipped with a camera and microphone to detect the user's facial expressions and voice tone. An emotion engine analyzes this data in real time to recognize the user's emotional state. Based on this emotional information, the server adjusts the learning plan and provides the learner with supplementary materials and step-by-step guides. 【0736】 For example, if analysis reveals that a user is experiencing frustration due to a difficult problem, the server uses a generative AI model to generate additional materials to help them understand the problem. In this way, the learner's comprehension can be improved, and the learning experience can be enhanced. 【0737】 The server provides teachers with learner progress and sentiment data through a dashboard, enabling teachers to support learners more effectively. Additionally, an example of a prompt message can be given to the generating AI model: "Please tell me how to change learning materials in real time based on the user's learning attitude." 【0738】 In this way, the present invention is designed to provide learners with a highly individualized learning environment, enabling teachers to provide appropriate support throughout the process. 【0739】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0740】 Step 1: 【0741】 The user starts a learning session using the device. The device records learning activity data in real time, such as the user's actions, inputs, and response times. Inputs are the user's clicks and keyboard inputs, and outputs are these data saved as logs. Specifically, the device sequentially stores the information received from the user interface into a data buffer. 【0742】 Step 2: 【0743】 The device sends the collected learning activity data to the server. The input is the learning data stored on the device, and the output is the data stored in a structured format in a database on the server. Specifically, the device periodically converts the data into packets via batch processing over the internet and sends them to the server's receiving API. 【0744】 Step 3: 【0745】 The server analyzes the received training data. The input is the training data stored in the server's database, and the output is the analysis results showing the user's learning style, strengths, and weaknesses. These results are generated by classifying the data using a generative AI model and applying a pattern recognition algorithm. Specifically, the dataset is processed using data mining techniques, and the generative AI model generates the analysis results. 【0746】 Step 4: 【0747】 The device senses the user's facial expressions and voice tone, collecting emotional data. Input is video and audio data obtained from the camera and microphone, while output is data indicating the user's emotional state. An emotion analysis algorithm analyzes this data to determine the user's emotional state. Specifically, the device periodically samples sensor data and inputs it into the emotion recognition model. 【0748】 Step 5: 【0749】 The server adjusts the learning plan based on sentiment analysis data. The input is the user's sentiment data obtained from sentiment analysis and the learning analysis results, and the output is the adjusted learning plan. The generative AI model considers the emotional state and decides how to improve or supplement the learning content. Specifically, the adaptive learning algorithm modifies the current plan and sends the generated content to the terminal. 【0750】 Step 6: 【0751】 The server updates a dashboard for teachers, making it easy for them to view learning progress and sentiment data. The input is learning and sentiment data collected on the server, and the output is a visualized interface. Specifically, the server converts the data into charts and graphs and updates the web dashboard displayed in the teacher's browser. 【0752】 (Application Example 2) 【0753】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0754】 Traditional individualized learning support systems could identify students' learning styles and strengths and weaknesses, and provide learning plans based on that information. However, they struggled to dynamically adjust plans to take into account students' emotions and stress levels. Furthermore, in the field of home-based learning support, there is a demand for interactive learning support using user-friendly robots, but current systems do not adequately meet this need. Therefore, there is a need for means to provide students with a better learning experience. 【0755】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0756】 In this invention, the server includes data collection means for recording students' learning activities in real time, emotion recognition means for recognizing emotions from the learner's facial expressions and voice and dynamically adjusting the learning content according to that state, and robot operation means for providing interactive educational content through a home learning support robot. This enables an emotion-based, individually optimized learning process and makes home learning support more effective. 【0757】 "Data collection means" refers to a device or process that records students' learning activities in real time and collects that data for post-processing. 【0758】 "Data analysis means" refers to a device or process for analyzing recorded learning data to identify students' learning styles, strengths, and weaknesses. 【0759】 A "plan creation method" is a device or process that generates an individually optimized learning plan based on the learning style and strengths and weaknesses of identified students. 【0760】 A "feedback mechanism" is a device or process that provides real-time feedback to students during their learning process. 【0761】 An "emotion recognition means" is a device or process that recognizes emotions from a learner's facial expressions and voice, and dynamically adjusts the learning content according to that state. 【0762】 "Robot operation means" refers to a device or process for providing interactive educational content through a learning support robot installed in a home. 【0763】 "Teacher support tools" refer to devices or processes that generate detailed reports used to support teachers. 【0764】 In one embodiment of this invention, a technology is implemented in an educational support system to optimize individual student learning experiences based on emotions. The core of the system consists of a server, terminals, and a learning support robot used in the home. 【0765】 The server uses a data collection device to record students' learning activities and accumulates data in real time. The terminals have the ability to save detected data locally or to cloud storage and send the data to the server as needed. This data is analyzed using Python and related analytical libraries. As a result, each student's learning style, strengths, and weaknesses are identified. 【0766】 Next, the server performs analysis using an emotion recognition engine. It utilizes the camera and microphone on the device to capture and analyze the learner's facial expressions and voice data in real time. This process uses the OpenCV and librosa libraries. 【0767】 The analysis results from the server are also used to control the Home robot. This robot provides user-friendly educational content within the home and dynamically changes the content as learning progresses. The robot uses a generative AI model to provide real-time feedback and more effectively support learning. 【0768】 Specifically, if a student is faced with a difficult math problem and shows a confused expression, the robot will use sensors to detect this emotion and use a prompt such as, "How would you give a simple hint to a 10-year-old who is struggling to figure out how to solve this equation?" to generate hints to help them understand. Based on the generated hints, the robot will provide advice in a friendly voice and play interactive explanatory videos as needed. 【0769】 This embodiment of the invention supports students' learning performance from an emotional perspective and provides teachers and parents with information to intervene effectively. In this way, students experience a more individualized learning process, and an improvement in overall learning effectiveness is expected. 【0770】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0771】 Step 1: 【0772】 The device records the user's learning activity in real time. Specifically, the device's camera and microphone monitor the learning situation, collecting data on screen operations and responses during the learning time. The input is the result of observations of the user's learning situation, and the output is generated in the form of observation data saved locally or in cloud storage. This data is used for subsequent analysis. 【0773】 Step 2: 【0774】 The server analyzes the data sent from the terminal. This specifically involves processing training data using Python's analysis library. The input is training data, which the server uses to identify each student's learning style and areas of strength and weakness. The output is the analysis results, which form the basis of an individually optimized learning plan. 【0775】 Step 3: 【0776】 The device captures students' facial expressions and voices using its camera and microphone, and performs emotion recognition processing. Specifically, it uses libraries such as OpenCV and librosa to process image and audio data and analyze students' emotions. The input is the captured facial and audio data, and the output is the analyzed emotional state information. 【0777】 Step 4: 【0778】 The server adjusts the learning content based on the emotional state. It creates prompts for the generative AI model and generates appropriate learning feedback and learning materials based on them. The input is the learning style information from step 2 and the emotional state information from step 3, and the output is appropriate learning materials and feedback information. 【0779】 Step 5: 【0780】 A terminal or a home-use learning support robot presents the generated learning materials to the user. Specifically, the robot explains to the user in a friendly voice and displays interactive content on the display as needed. The input is learning material information from the server, and the output is the visual and auditory information received by the user. 【0781】 Step 6: 【0782】 The server integrates all processing results and generates a detailed report for teachers. Specifically, it updates the teacher dashboard, organizing data to visually display student learning progress and emotional state. The input is the processing result of each step, and the output is a visualized detailed report. 【0783】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0784】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0785】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0786】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0787】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0788】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0789】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0790】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0791】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0792】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0793】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0794】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0795】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0796】 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. 【0797】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0798】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0799】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0800】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0801】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0802】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0803】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0804】 The following is further disclosed regarding the embodiments described above. 【0805】 (Claim 1) 【0806】 A data collection method for recording students' learning activities in real time, 【0807】 A data analysis method that analyzes recorded learning data to identify each student's learning style, strengths, and weaknesses, 【0808】 A plan creation means for generating individually optimized learning plans based on identified results, 【0809】 A feedback system that provides real-time feedback to students during their learning, 【0810】 A teacher support tool that generates detailed reports used to support teachers, 【0811】 A system that includes this. 【0812】 (Claim 2) 【0813】 The system according to claim 1, further comprising a plan adjustment means for analyzing a student's weaknesses in a specific area based on their answer history and incorporating tasks dealing with that area into the next lesson plan. 【0814】 (Claim 3) 【0815】 The system according to claim 1, further comprising means for updating a teacher dashboard to visually display the learning status and progress of each student. 【0816】 "Example 1" 【0817】 (Claim 1) 【0818】 A means of collecting information to record learners' educational activities in real time, 【0819】 An information analysis means for analyzing recorded educational data to identify each learner's teaching style, strengths, and weaknesses, 【0820】 A planning means for generating individually optimized educational plans based on identified results, 【0821】 A response mechanism that provides real-time feedback to learners during instruction, 【0822】 A leader support tool that generates detailed reports to be used to support leaders, 【0823】 A generation means that uses an artificial intelligence model to analyze educational data and provides dynamically generated educational plans and feedback, 【0824】 A system that includes this. 【0825】 (Claim 2) 【0826】 The system according to claim 1, further comprising a planning adjustment means for analyzing weaknesses in a specific area based on the learner's answer history and incorporating tasks dealing with that area into the next educational plan. 【0827】 (Claim 3) 【0828】 The system according to claim 1, further comprising means for updating the instructor's display device to visually display the educational status and progress of each learner. 【0829】 "Application Example 1" 【0830】 (Claim 1) 【0831】 A data collection method for recording students' learning activities in real time, 【0832】 A data analysis method that analyzes recorded learning data to identify each student's learning style, strengths, and weaknesses, 【0833】 A plan creation means for generating individually optimized learning plans based on identified results, 【0834】 A feedback system that provides real-time feedback to students during their learning, 【0835】 A teacher support tool that generates detailed reports used to support teachers, 【0836】 A control means that uses speech recognition and data communication to present learning tasks in response to user instructions and provide learning support, 【0837】 A system that includes this. 【0838】 (Claim 2) 【0839】 The system according to claim 1, further comprising a plan adjustment means for analyzing a student's weaknesses in a specific area based on their answer history and incorporating tasks dealing with that area into the next lesson plan. 【0840】 (Claim 3) 【0841】 The system according to claim 1, further comprising means for updating a teacher dashboard to visually display the learning status and progress of each student. 【0842】 "Example 2 of combining an emotion engine" 【0843】 (Claim 1) 【0844】 A data collection method for recording students' learning activities in real time, 【0845】 A data analysis method that analyzes recorded learning data to identify each student's learning style, strengths, and weaknesses, 【0846】 A plan creation means for generating individually optimized learning plans based on identified results, 【0847】 A means of emotional analysis that recognizes and analyzes the emotional state of students, 【0848】 A plan adjustment method that adjusts the learning plan in real time based on emotional information, 【0849】 A feedback system that provides real-time feedback to students during their learning, 【0850】 A teacher support tool that generates detailed reports used to support teachers, 【0851】 The teacher dashboard has been updated to provide a means of visually displaying each student's learning status and progress, 【0852】 A system that includes this. 【0853】 (Claim 2) 【0854】 The system according to claim 1, further comprising a plan adjustment means for analyzing a student's weaknesses in a specific area based on their answer history and emotional state, and incorporating tasks dealing with that area into the next lesson plan. 【0855】 (Claim 3) 【0856】 The system according to claim 1, further comprising means for visually displaying each student's learning status and progress, including emotion analysis data, and for generating prompts for teachers to implement effective support using a generated AI model. 【0857】 "Application example 2 when combining with an emotional engine" 【0858】 (Claim 1) 【0859】 A data collection method for recording students' learning activities in real time, 【0860】 A data analysis method that analyzes recorded learning data to identify each student's learning style, strengths, and weaknesses, 【0861】 A plan creation means for generating individually optimized learning plans based on identified results, 【0862】 A feedback system that provides real-time feedback to students during their learning, 【0863】 An emotion recognition means that recognizes emotions from the learner's facial expressions and voice, and dynamically adjusts the learning content according to that state, 【0864】 A robot operation method that provides interactive educational content through a learning support robot in the home, 【0865】 A teacher support tool that generates detailed reports used to support teachers, 【0866】 A system that includes this. 【0867】 (Claim 2) 【0868】 The system according to claim 1, further comprising a plan adjustment means for analyzing a student's weaknesses in a specific area based on their answer history and incorporating tasks dealing with that area into the next lesson plan in conjunction with their emotional state. 【0869】 (Claim 3) 【0870】 The system according to claim 1, further comprising means for updating a teacher dashboard to visually display each student's learning status and emotional state. [Explanation of Symbols] 【0871】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A data collection method for recording students' learning activities in real time, A data analysis method that analyzes recorded learning data to identify each student's learning style, strengths, and weaknesses, A plan creation means for generating individually optimized learning plans based on identified results, A feedback method that provides real-time feedback to students during their learning, A teacher support tool that generates detailed reports used to support teachers, A system that includes this. [Claim 2] The system according to claim 1, further comprising a plan adjustment means for analyzing a student's weaknesses in a specific area based on their answer history and incorporating tasks dealing with that area into the next lesson plan. [Claim 3] The system according to claim 1, further comprising means for updating a teacher dashboard to visually display the learning status and progress of each student.

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