system

Abstract

We provide the system. [Solution] Means for collecting information on past educational plans, test results, and learning outcomes, A method using an artificial intelligence model that generates educational plans using collected information, A means of distributing the generated educational plan to the educator's terminal, A means for selecting or generating the most suitable educational materials based on the educational content, A means of monitoring learning progress, analyzing results, and providing feedback, A means of adjusting plans by comparing educational outcomes with information from other educational institutions, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the educational field, there is a problem that teachers spend a lot of time on annual lesson plans, teaching materials, test creation, etc., making it difficult to provide detailed guidance to individual students. Also, in order to unify the quality and content of education, it is required to effectively utilize other schools and past achievements. Furthermore, with the deepening of teacher shortages and overwork, the efficiency of the entire educational field is needed. 【Means for Solving the Problems】 【0005】 This invention provides a system that collects information on past educational plans, test results, and learning outcomes, and generates an optimal educational plan using an artificial intelligence model based on this information. The generated plan is delivered to the educator's terminal, and the most suitable educational materials are selected or generated based on the educational content. Furthermore, it has a function to monitor learning progress, analyze the results, and provide feedback, thereby comparing educational outcomes with other educational institutions and correcting the plan. In this way, it enables improvement of the quality and efficiency of education. 【0006】 An "educational plan" refers to a specific schedule of lessons and activities conducted annually or semester-wise to achieve the educational goals set by a school or educational institution. 【0007】 An "artificial intelligence model" is a mathematical or computational model that analyzes data and identifies patterns to automatically generate and recommend the optimal solution for a specific problem. 【0008】 A "terminal" is a device used for inputting or displaying information, and usually refers to computers, tablets, smartphones, etc. 【0009】 "Educational materials" refer to teaching materials and resources used in classes and learning to achieve specific educational objectives, and include those provided in digital or paper format. 【0010】 "Learning progress" refers to an indicator or state that shows how far a learner has progressed in a particular educational task or program. 【0011】 "Feedback" is the process by which educators or systems evaluate learners' performance and understanding, and based on that, provide instructions and advice for improving learning. 【0012】 "Educational outcomes" refer to the results used to evaluate the improvement in learners' knowledge, skills, and attitudes that result from educational activities. 【0013】 "Comparison" is the act of examining multiple elements in relation to each other and clarifying their differences and similarities. 【0014】 "Correction" refers to the act of detecting errors or defects and correcting or improving them. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [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, which incorporates an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine. 【Mode for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. <M 【0017】 <M First, the terms used in the following description will be described. 【0018】 In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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. 【0019】 In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, a storage with a reference numeral 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 disk (e.g., hard disk), or magnetic tape, etc. 【0021】 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). 【0022】 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." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 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. 【0026】 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). 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 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. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 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. 【0033】 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. 【0034】 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. 【0035】 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". 【0036】 This invention is a system that supports the automatic generation and efficient management of annual educational plans in educational institutions. To achieve this, it performs the following operations. 【0037】 As one form of the invention, the server collects relevant data such as past educational plans, test results, and learning outcomes from a school or educational institution. This data includes not only digitally recorded information but also publicly available data on best practices from other schools. The server organizes this information and corrects missing values ​​and errors using data cleansing techniques. 【0038】 Next, the server runs an artificial intelligence model to generate educational plans based on the collected clean data. This model uses machine learning algorithms to analyze the data and proposes the optimal lesson plan for each subject and semester. 【0039】 The generated lesson plans are delivered directly from the server to the educators' and learners' devices. Educators receive these plans via tablets or computers and can check and modify them as needed. Learning materials, assignments, and test questions based on the plan are automatically provided to the learners' devices. 【0040】 Furthermore, the terminal has a function to monitor learners' progress in real time, and the progress data generated based on this is sent to the server. The server analyzes this data and provides individualized feedback to educators. This feedback serves as important reference information for educators when providing individualized instruction. 【0041】 Furthermore, the server generates information to optimize the school's own educational plan by comparing the accumulated learning outcomes with those of other educational institutions. Based on this comparative analysis, revised plans are proposed and used when formulating the educational plan for the following year. 【0042】 As a concrete example, in one school, an AI model proposed a new lesson plan based on past math lesson plans and test results. This plan included additional practice time to address students' weaknesses, resulting in improved overall grades. This freed teachers from the daily task of preparing materials, allowing them to focus on interacting with students and providing individualized instruction. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The server automatically collects data on past educational plans, test results, and learning outcomes. This includes access from the school's internal database and cloud storage. The server stores the collected data while maintaining consistency and avoiding duplication. 【0046】 Step 2: 【0047】 The server cleanses the collected data. During this process, the server detects missing values ​​and uses statistical methods to estimate and impute them. It also detects and corrects obvious data errors. 【0048】 Step 3: 【0049】 The server generates educational plans using an artificial intelligence model based on the cleansed data. This model utilizes machine learning algorithms to extract trends and patterns from the data and design an optimized plan. 【0050】 Step 4: 【0051】 The generated lesson plan is distributed from the server to the teacher's terminal. The terminal notifies the teacher that a new plan has arrived via a notification function. The teacher can review the plan and make modifications as needed. 【0052】 Step 5: 【0053】 The server selects or creates the most suitable educational materials and tests based on the educational plan. These materials are provided to learners' devices and are automatically updated as needed. 【0054】 Step 6: 【0055】 The device monitors the learner's learning progress. Progress data is collected based on the learner's activity logs and test responses, and this information is sent to the server. 【0056】 Step 7: 【0057】 The server analyzes progress data and generates personalized feedback. This feedback is provided to the teacher's terminal and used to determine the teaching strategy for the students. 【0058】 Step 8: 【0059】 The server compares the collected learning outcomes with data from other educational institutions. Through this process, the school identifies areas for improvement and strengths in its curriculum, and adjusts its plans for the following year as needed. 【0060】 (Example 1) 【0061】 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." 【0062】 Developing educational plans in educational institutions requires the proper organization and analysis of multiple data and information, which is time-consuming and labor-intensive. Furthermore, a lack of progress monitoring and feedback for individual learners can lead to missed opportunities for personalized instruction. Additionally, incomplete comparative analyses with other educational institutions can result in unoptimized plans. These challenges hinder the improvement of educational quality. 【0063】 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. 【0064】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes of educational institutions; means for using a data analysis model to generate educational plans using the collected information; and means for distributing the generated educational plans to the terminals of educators and learners. This enables the efficient generation of educational plans and the provision of progress management and feedback tailored to individual learners. 【0065】 An "educational plan" is a plan that systematically and comprehensively arranges the lessons, assignments, and other educational activities that an educational institution will provide over a specific period of time. 【0066】 "Test results" refer to information that shows the grades and evaluation scores of tests taken by learners. 【0067】 "Learning outcomes" refer to the results that demonstrate the knowledge, skills, and level of understanding that learners acquire through educational activities. 【0068】 A "data analysis model" refers to computational methods and algorithms used to obtain insights and predictions tailored to specific purposes, based on collected data. 【0069】 A "terminal" is an electronic device used by educators and learners to receive and transmit information. 【0070】 "Educational information" refers to all information provided based on the educational plan, including teaching materials, assignments, and test questions. 【0071】 "Progress monitoring" refers to the activity of continuously observing and recording learners' learning status and achievements. 【0072】 "Feedback" refers to information provided for evaluation and guidance based on the results of monitoring and analysis. 【0073】 "Data cleansing" is the process of detecting and correcting missing values ​​and errors in data. 【0074】 "Visual presentation" refers to the activity of making information easier to understand by using visual media such as graphs and charts. 【0075】 This system is designed to efficiently and automatically generate and manage annual educational plans for educational institutions. The specific implementation of this system is described below. 【0076】 The server collects data from educational institutions, including past teaching plans, test results, and learning outcomes. This data also includes publicly available information on best practices from other educational institutions. The collected data is cleansed using Python's Pandas library and SQL, with missing values ​​imputed and errors corrected. 【0077】 The server executes machine learning algorithms based on the cleansed data. Data analysis models built using software such as Sci-kit Learn and TENSORFLOW® generate optimal lesson plans for each subject and semester. This AI model has the ability to analyze data and propose educational plans for the following academic year. 【0078】 The generated lesson plans are delivered directly from the server to the educators' and learners' devices. Educators can review the plans using tablets or PCs and make necessary modifications using Microsoft® Excel® or Google® Sheets. Educational materials, assignments, and test questions are provided to the devices via the Learning Management System (LMS). 【0079】 The terminal monitors learners' progress in real time and sends the data to the server. Users (educators) receive feedback from a dashboard provided by the server, enabling them to plan individualized instruction and additional support. The server also performs comparative analysis with data from other educational institutions to help adjust future teaching plans. 【0080】 As a concrete example, in one school, an AI model proposed a new lesson plan based on past math lesson plans and test results. This plan included additional practice time to strengthen students' weaknesses, resulting in improved grades. An example of a prompt would be, "Please propose a lesson plan for the next academic year based on the math lesson plans and test results from the past three years." 【0081】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0082】 Step 1: 【0083】 The server collects data on educational institutions' past teaching plans, test results, and learning outcomes. Inputs include digitized historical education data and publicly available information on best practices from other schools. The server achieves centralized information management by collecting data using scraping techniques and APIs and storing it in a database. Output is a unified set of educational data. 【0084】 Step 2: 【0085】 The server applies data cleansing to the collected data. The input is training data containing missing values ​​and errors. The server uses the Python Pandas library to impute missing values ​​with mean or mode values ​​and automatically correct obvious errors. The output is a cleansed, accurate dataset. 【0086】 Step 3: 【0087】 The server runs a machine learning model based on cleansed data. The input is well-organized educational data. The server implements a data analysis model using Sci-kit Learn and TensorFlow, applies machine learning algorithms, and analyzes the data. The output is an optimized educational plan for the following year. 【0088】 Step 4: 【0089】 The server distributes the generated lesson plans to the educators' and learners' devices. The input is the lesson plan generated by a machine learning model. The server distributes the plan to the devices via the educational management platform, allowing educators to view and edit it. The output is the lesson plan data accessible on the devices. 【0090】 Step 5: 【0091】 The terminal monitors the learner's progress in real time. Inputs include the learner's assignment submission status and test results. The terminal collects progress data using a learning management system and automatically sends it to the server. Output is the progress data sent to the server. 【0092】 Step 6: 【0093】 The server analyzes progress data and provides feedback to educators. The input is learner progress data. The server analyzes the data and generates feedback based on performance on assignments and tests. Educators can use this feedback to create individualized instruction plans. The output is the feedback information provided to educators. 【0094】 Step 7: 【0095】 The server compares collected learning outcomes with data from other educational institutions. The inputs are the educational institution's learning outcomes data and publicly available benchmark data. The server uses BI tools to perform comparative analysis and generate revised educational plans. The output is the revised educational plan based on the analysis results. 【0096】 (Application Example 1) 【0097】 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." 【0098】 Developing annual educational plans in educational institutions requires extensive data analysis and planning, placing a significant burden on educators. Furthermore, customizing education to individual learners and adjusting plans based on performance comparisons with other institutions are cumbersome when done manually, highlighting the need for greater efficiency. Additionally, the lack of an environment that allows educators and learners to easily review and edit generated educational plans poses a problem for their implementation. 【0099】 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. 【0100】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes; means for using an artificial intelligence model to generate educational plans using the collected information; and means for providing a terminal application that presents the generated educational plans to the terminals of educators and learners, allowing them to review and modify them. This automates the formulation and management of educational plans, reduces the burden on educators, and improves the feasibility and effectiveness of the plans. 【0101】 "Past educational plans" refers to outlines of learning programs and schedules previously established at educational institutions. 【0102】 "Test results" refers to data that includes the grades and evaluations of tests taken by learners. 【0103】 "Learning outcomes" refer to information that demonstrates the knowledge and skills that learners have acquired through an educational program. 【0104】 "Means of information gathering" refers to the process or technology of collecting necessary education-related data through databases or the internet. 【0105】 An "artificial intelligence model" refers to an algorithm or system that analyzes large amounts of data to derive patterns and predictions. 【0106】 A "terminal application" is software that runs on a user's device and enables the provision of information and operations. 【0107】 "Educational materials" refer to learning materials such as textbooks, videos, audiobooks, and practice problems that learners use during their learning process. 【0108】 "Means of monitoring learning progress" refers to the process or technology of tracking learners' learning status in real time and collecting that data. 【0109】 "Means of providing feedback" refers to functions that provide learners and educators with advice and information based on learning progress and evaluation. 【0110】 "Using prompt statements" refers to a method of inputting instructions in natural language into an AI model to obtain a specific output. 【0111】 The system that realizes this invention efficiently generates educational plans by collecting data on past educational plans, test results, and learning outcomes on a server and analyzing it with an artificial intelligence model. Specifically, the server extracts the necessary data from information sources such as databases and cleans the data using the Python programming language. After the data is prepared, it trains an artificial intelligence model using an AI framework such as TensorFlow and generates educational plans. 【0112】 The generated lesson plans are delivered to educators and learners through a terminal application developed using React Native. This application allows educators to review and edit the plan, while learners can access necessary educational materials using QR codes (registered trademark) and an intuitive user interface. The terminal also monitors learning progress in real time and sends the data back to the server. The server analyzes this data and provides individualized feedback to educators. 【0113】 As a concrete example, in a middle school mathematics class, using this system allows educators to gain a detailed understanding of each student's learning progress. Based on this, the AI ​​can suggest optimal practice problems, thereby improving the learners' comprehension. 【0114】 An example of an input prompt for the generating AI model is expected to be the text, "Based on past educational plan data and test results, please generate the optimal lesson plans for mathematics, Japanese language, and English for the new school year." By using such prompts, the AI ​​will be able to optimize the educational plans. 【0115】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0116】 Step 1: 【0117】 The server collects past educational plans, test results, and learning outcomes from the educational institution's database and external sources. This data is retrieved via an API and imported into the server in JSON format. 【0118】 Step 2: 【0119】 The server cleanses the collected raw data using the Python programming language and the Pandas library. After performing processes such as imputing missing values ​​and ensuring data consistency, it generates a clean dataset. The input to this process is raw data in JSON format, and the output is cleansed structured data. 【0120】 Step 3: 【0121】 The server feeds a clean dataset into an AI model built using TensorFlow to generate educational plans. This model executes machine learning algorithms to output optimal class schedules for each subject and semester. The input is cleansed structured data, and the output is an optimized educational plan. 【0122】 Step 4: 【0123】 The server delivers the generated lesson plan to the terminal application. This application is developed in React Native and provides an interface accessible to both educators and learners. The input is the optimized lesson plan, and the output is the display of the lesson plan on the terminal. 【0124】 Step 5: 【0125】 The terminal monitors the learner's learning progress in real time and sends the data to the server. This monitoring is performed by collecting user interaction data and learning status logs. The input is user interaction data, and the output is the aggregation of progress data on the server. 【0126】 Step 6: 【0127】 The server analyzes progress data and provides individualized feedback to educators. This feedback is generated through data analysis using Python and sent to the educator's terminal in text format. The input is student-specific progress data, and the output is feedback information. 【0128】 Step 7: 【0129】 The user enters a prompt to review or edit the content of the educational plan. The server re-evaluates the AI ​​model based on this prompt and modifies the plan as needed. The input is the prompt, and the output is the updated educational plan. 【0130】 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. 【0131】 This invention provides a system that improves the quality of education by incorporating an emotion engine that recognizes the user's emotions and adjusts educational activities based on those emotions into a system that efficiently generates educational plans using past educational plans and test results. The operation of the processing in this system is described below. 【0132】 First, the server collects information from a database, such as past teaching plans, test results, and learning outcomes. This data is then analyzed by an artificial intelligence model. Based on the analysis, the server generates an optimized teaching plan. This plan is delivered to the educator's terminal, where they can review the information and make modifications as needed. 【0133】 Furthermore, the server selects learning materials appropriate to the learning content based on the collected data. This includes materials that match the learner's past performance and educational goals. The selected materials are delivered directly to the learner's device. 【0134】 Furthermore, a key aspect of this invention is the integration of an emotion engine into the system that recognizes the user's emotions. The terminal is equipped with a camera and microphone, which are used to capture the learner's facial expressions and voice tone in real time. The server uses the emotion engine to analyze this data and estimate the learner's current emotional state—for example, whether they understand, are stressed, or are bored. 【0135】 Based on the emotional state identified by the emotion engine, the server dynamically adjusts the educational content. Specifically, if the server determines that the learner's understanding is insufficient, it provides additional supplementary materials and hints. Similarly, if motivation is low, the server can present encouraging messages and interactive content through the device. 【0136】 For example, suppose a student shows signs of anxiety when taking a unit test. The emotion engine recognizes this anxiety, and the server displays relaxing audio guidance and simple warm-up exercises on the student's device. This allows the learner to take the test in a more relaxed state. 【0137】 This emotional recognition-based adjustment function enables educators to provide individualized education tailored to each learner, which is expected to improve overall learning outcomes. 【0138】 The following describes the processing flow. 【0139】 Step 1: 【0140】 The server collects past educational plans, test results, and learning outcomes from a database. This lays the foundation for analyzing educational patterns and effectiveness. 【0141】 Step 2: 【0142】 The server detects missing data and uses statistical algorithms to fill in the gaps. This process enhances data integrity. 【0143】 Step 3: 【0144】 The server runs an artificial intelligence model using the consistent data to generate an optimal educational plan. The AI ​​builds the plan based on past trends and future goals. 【0145】 Step 4: 【0146】 The generated lesson plans are distributed from the server to the terminals. The terminals then send notifications to the teachers, prompting them to review the plans and make any necessary revisions. 【0147】 Step 5: 【0148】 The device captures the user's facial expressions and voice through its camera and microphone. This data is transmitted to the server in real time. 【0149】 Step 6: 【0150】 The server's emotion engine analyzes the received visual and auditory data to estimate the user's emotional state. For example, it can determine whether the user is stressed or unable to concentrate. 【0151】 Step 7: 【0152】 The server dynamically adjusts educational content based on the results of the emotion engine. If it is estimated that there is a lack of understanding, it delivers additional support materials to the device. 【0153】 Step 8: 【0154】 The device displays the user with adjusted educational materials to help improve their understanding. It also presents interactive content to increase engagement. 【0155】 Step 9: 【0156】 Users progress through their learning using the provided content. Progress data and sentiment data continue to be sent from the device to the server and used to inform subsequent actions. 【0157】 (Example 2) 【0158】 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 will be referred to as the "terminal." 【0159】 Modern education demands individualized instruction tailored to each learner's level of understanding and psychological state, but traditional systems struggle to accurately grasp and adjust these factors in real time. Furthermore, there is a need to provide education that considers learners' emotional states while efficiently utilizing past educational plans and outcomes. 【0160】 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. 【0161】 In this invention, the server includes means for collecting information on past plans, test results, and outcomes; means for using a model to generate plans using the collected information; and means for collecting and analyzing emotional information. This makes it possible to precisely understand the current state of learners and provide effective education tailored to their individual psychological states. 【0162】 "Past plans" refer to past strategies and progress in education, and serve as foundational information for analyzing learner performance. 【0163】 "Test results" refer to the grades and evaluations of tests taken by learners, and are objective information that shows each individual's level of understanding and achievements. 【0164】 "Outcomes" are indicators that show the level of skills and knowledge acquired by learners through educational activities. 【0165】 "Means of collection" refers to methods and techniques for efficiently gathering necessary information. 【0166】 A "model" is an algorithm or system designed for a specific purpose, which processes data to derive the optimal solution. 【0167】 "Means of distribution" refers to the technologies and methods for transmitting generated information and educational materials to the appropriate target audience. 【0168】 "Optimal materials" refer to teaching materials and information that are most suitable for the learner's needs and progress. 【0169】 "Emotional information" refers to data that indicates the psychological state of learners and is a factor that influences the learning environment and outcomes. 【0170】 "Means of analysis" refer to methods and techniques for analyzing collected data in detail and understanding its meaning and trends. 【0171】 "Means of monitoring progress" refer to methods for continuously tracking and evaluating learners' activities and progress. 【0172】 "Means of providing feedback" refer to methods for presenting learners and educators with improvements and indicators based on the analysis results. 【0173】 This invention is an innovative system that efficiently generates educational plans and provides dynamic education based on user emotions. The server collects past plans, test results, and learning outcomes from a database and utilizes a generative AI model to generate educational plans based on this information. This model uses machine learning libraries such as TensorFlow and can highly analyze the collected data. The analyzed results are delivered to the educator's terminal as an optimized educational plan. 【0174】 Furthermore, the server collects and analyzes emotional information to understand the learner's psychological state. For this purpose, the camera and microphone installed on each learner's terminal are used to capture facial expressions and voice in real time. This data is sent to the server and analyzed by the emotion engine. Specifically, facial expression analysis is performed using libraries such as OpenCV to estimate the learner's psychological state. 【0175】 Based on the results of sentiment analysis, the server dynamically adjusts the educational content. For example, if a learner is having difficulty understanding, it can provide additional supplementary materials or hints. Furthermore, if it determines that motivation is low, it displays encouraging messages or interactive content on the learner's device. 【0176】 For example, suppose a user is feeling stressed before a math test. When the emotion engine detects this, the server sends a relaxing audio guide or warm-up questions to the user's device. This allows the user to approach the test in a calm state. 【0177】 An example of a prompt used in this system would be, "Please provide something to help you relax before the math test." This enables personalized education that takes into account the learner's psychological and emotional state. 【0178】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0179】 Step 1: 【0180】 The server collects data on past plans, test results, and learning outcomes from the database. Input is done using SQL queries, and output is a data frame summarizing the collected data. This data frame is used in the next analysis step. 【0181】 Step 2: 【0182】 The server inputs the collected data into a generating AI model for analysis. This AI model is built using the TensorFlow library. The input is the data frame obtained in the previous step, and the output is the trend analysis results used to optimize the educational plan. This analysis predicts what kind of plan will be effective. 【0183】 Step 3: 【0184】 The server generates an educational plan based on the analysis results of the AI ​​model. The generated plan is output in PDF format and delivered to the educator's terminal. It is then delivered as an email using the SMTP protocol, and the educator reviews its contents. At this point, the educator can revise the plan as needed. 【0185】 Step 4: 【0186】 The server selects learning materials that are appropriate to the learner's needs based on the analysis results. The input is the analysis results obtained in the previous stage, and the output is a list of learning materials delivered to the learner's device. Past performance and learning objectives are taken into consideration in this selection. The learning materials are provided as PDFs, videos, or interactive content. 【0187】 Step 5: 【0188】 The device uses a camera and microphone to capture the learner's emotional state in real time. Input consists of the learner's facial expressions and voice data, which the device sends to a server. Output is a dataset for emotion analysis. This data is processed using a facial expression analysis API. 【0189】 Step 6: 【0190】 The server analyzes the received emotional data and estimates the learner's psychological state. The input is the emotional data obtained in step 5, and the output is an assessment of the learner's current emotional state. Here, it determines whether the learner is confused, stressed, or bored. 【0191】 Step 7: 【0192】 The server dynamically adjusts educational content based on the sentiment analysis results. The input is an assessment of the emotional state, and the output is improved educational materials or motivational content. Specifically, if there is a lack of understanding, additional materials are provided, and if motivation is low, encouraging messages are displayed on the device. 【0193】 (Application Example 2) 【0194】 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". 【0195】 Traditional education systems have faced challenges in recognizing and effectively responding to learners' emotions and learning progress in real time, making it difficult to provide individually optimized education. In particular, in home environments, limited learning support makes it difficult to maintain learners' motivation and learning effectiveness. This results in a decline in the quality of education and makes it difficult to achieve effective learning outcomes. 【0196】 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. 【0197】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes; means for using an artificial intelligence model to generate educational plans using the collected information; means for distributing the generated educational plans; and means for recognizing learners' emotions and dynamically adjusting educational activities based on those emotions. This makes it possible to provide individually optimized education to learners in real time. 【0198】 "Past educational plans" refers to the teaching policies and learning program designs that were previously formulated in educational activities. 【0199】 "Test results" refer to the results of tests used to evaluate the achievements of learners in educational activities. 【0200】 "Learning outcomes" refer to the extent to which learners have acquired knowledge and skills through educational activities. 【0201】 An "artificial intelligence model" is an algorithm that uses large amounts of data to make predictions and decisions for performing specific tasks. 【0202】 A "terminal" is a type of computing device capable of displaying and inputting information, and is used for receiving and displaying educational content. 【0203】 "Educational materials" refer to teaching materials and information resources that learners use to study. 【0204】 "Monitoring" is the process of continuously observing and recording the state and trends of a specific subject, and taking action as needed. 【0205】 "Feedback" refers to evaluations and suggestions provided to learners to encourage improvement and growth. 【0206】 "Recognizing emotions" is the process of determining a user's emotional state at any given moment through their facial expressions, voice, and other actions. 【0207】 "Dynamic adjustment" means changing systems and processes in real time in response to changes in circumstances and needs. 【0208】 To realize this application, the system is implemented using a combination of specific hardware and software. The server collects information on past teaching plans, test results, and learning outcomes from a database, and uses an artificial intelligence model to generate an optimal teaching plan based on this data. The generated teaching plan is then sent to the educator's terminal and used as the basis for teaching activities. 【0209】 Furthermore, the server uses devices such as cameras and microphones to capture the learner's facial expressions and voice in order to recognize the user's emotions. This data is analyzed by an emotion recognition engine (for example, Microsoft Azure's Face API). Based on the emotional state obtained from the analysis, the server dynamically adjusts the educational content. For example, if the server estimates that the learner is feeling stressed, it will send a voice message to help them relax or simple warm-up exercises to the learner's device to support their learning experience. 【0210】 The goal of this system is to provide personalized education to individual learners and support efficient and effective learning. The generative AI model optimizes the teaching plan and materials based on prompts. For example, a prompt might be: "This child has lost interest in today's math assignment. What would you suggest to rekindle his interest?" 【0211】 These technologies enable the system to provide appropriate educational content tailored to the learner's needs, thereby improving learning outcomes. 【0212】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0213】 Step 1: 【0214】 The server collects information on past educational plans, test results, and learning outcomes from a database. The input data consists of educational data provided by educational institutions, and based on this, an artificial intelligence model begins generating educational plans. The output is a dataset in the format necessary for analysis. At this stage, the main operations are data collection and organization. 【0215】 Step 2: 【0216】 The server uses the collected data to feed into an artificial intelligence model to generate an optimal educational plan. The input is educational information retrieved from a database, and data processing includes cleaning and pre-processing. The output is a detailed educational plan sent to the educator's terminal. Here, the AI ​​model analyzes the data and constructs the optimal learning plan. 【0217】 Step 3: 【0218】 The device captures the learner's facial expressions and voice through its camera and microphone and sends them to the server. The input is real-time audio and visual data. The output is formatted data for analysis based on this data. In this step, data on the user's emotions is collected through the device. 【0219】 Step 4: 【0220】 The server analyzes the learner's emotions from the acquired data using an emotion recognition engine. The input is video and audio data transmitted from the terminal, and an emotion recognition algorithm is applied as data processing. The output is information indicating the learner's emotional state. Specifically, the server calls an emotion recognition API to analyze emotions. 【0221】 Step 5: 【0222】 The server adjusts the educational content based on emotion analysis. For example, if the input indicates that the learner is experiencing stress, the server provides additional educational materials to promote relaxation. The output is the adjusted educational plan and supplementary materials. In this step, the AI ​​model generates the responses to enhance support for the learner. 【0223】 Step 6: 【0224】 The terminal displays pre-configured educational content provided by the server to the learner, facilitating interaction. Input is the instructions from the server, and output is feedback information based on the user's interaction. The system then captures how the user perceived the task and provides feedback accordingly. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 [Second Embodiment] 【0229】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0230】 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. 【0231】 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). 【0232】 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. 【0233】 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. 【0234】 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). 【0235】 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. 【0236】 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. 【0237】 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. 【0238】 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. 【0239】 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. 【0240】 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". 【0241】 This invention is a system that supports the automatic generation and efficient management of annual educational plans in educational institutions. To achieve this, it performs the following operations. 【0242】 As one form of the invention, the server collects relevant data such as past educational plans, test results, and learning outcomes from a school or educational institution. This data includes not only digitally recorded information but also publicly available data on best practices from other schools. The server organizes this information and corrects missing values ​​and errors using data cleansing techniques. 【0243】 Next, the server runs an artificial intelligence model to generate educational plans based on the collected clean data. This model uses machine learning algorithms to analyze the data and proposes the optimal lesson plan for each subject and semester. 【0244】 The generated lesson plans are delivered directly from the server to the educators' and learners' devices. Educators receive these plans via tablets or computers and can check and modify them as needed. Learning materials, assignments, and test questions based on the plan are automatically provided to the learners' devices. 【0245】 Furthermore, the terminal has a function to monitor learners' progress in real time, and the progress data generated based on this is sent to the server. The server analyzes this data and provides individualized feedback to educators. This feedback serves as important reference information for educators when providing individualized instruction. 【0246】 Furthermore, the server generates information to optimize the school's own educational plan by comparing the accumulated learning outcomes with those of other educational institutions. Based on this comparative analysis, revised plans are proposed and used when formulating the educational plan for the following year. 【0247】 As a concrete example, in one school, an AI model proposed a new lesson plan based on past math lesson plans and test results. This plan included additional practice time to address students' weaknesses, resulting in improved overall grades. This freed teachers from the daily task of preparing materials, allowing them to focus on interacting with students and providing individualized instruction. 【0248】 The following describes the processing flow. 【0249】 Step 1: 【0250】 The server automatically collects data on past educational plans, test results, and learning outcomes. This includes access from the school's internal database and cloud storage. The server stores the collected data while maintaining consistency and avoiding duplication. 【0251】 Step 2: 【0252】 The server cleanses the collected data. During this process, the server detects missing values ​​and uses statistical methods to estimate and impute them. It also detects and corrects obvious data errors. 【0253】 Step 3: 【0254】 The server generates educational plans using an artificial intelligence model based on the cleansed data. This model utilizes machine learning algorithms to extract trends and patterns from the data and design an optimized plan. 【0255】 Step 4: 【0256】 The generated lesson plan is distributed from the server to the teacher's terminal. The terminal notifies the teacher that a new plan has arrived via a notification function. The teacher can review the plan and make modifications as needed. 【0257】 Step 5: 【0258】 The server selects or creates the most suitable educational materials and tests based on the educational plan. These materials are provided to learners' devices and are automatically updated as needed. 【0259】 Step 6: 【0260】 The device monitors the learner's learning progress. Progress data is collected based on the learner's activity logs and test responses, and this information is sent to the server. 【0261】 Step 7: 【0262】 The server analyzes progress data and generates personalized feedback. This feedback is provided to the teacher's terminal and used to determine the teaching strategy for the students. 【0263】 Step 8: 【0264】 The server compares the collected learning outcomes with data from other educational institutions. Through this process, the school identifies areas for improvement and strengths in its curriculum, and adjusts its plans for the following year as needed. 【0265】 (Example 1) 【0266】 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." 【0267】 Developing educational plans in educational institutions requires the proper organization and analysis of multiple data and information, which is time-consuming and labor-intensive. Furthermore, a lack of progress monitoring and feedback for individual learners can lead to missed opportunities for personalized instruction. Additionally, incomplete comparative analyses with other educational institutions can result in unoptimized plans. These challenges hinder the improvement of educational quality. 【0268】 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. 【0269】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes of educational institutions; means for using a data analysis model to generate educational plans using the collected information; and means for distributing the generated educational plans to the terminals of educators and learners. This enables the efficient generation of educational plans and the provision of progress management and feedback tailored to individual learners. 【0270】 An "educational plan" is a plan that systematically and comprehensively arranges the lessons, assignments, and other educational activities that an educational institution will provide over a specific period of time. 【0271】 "Test results" refer to information that shows the grades and evaluation scores of tests taken by learners. 【0272】 "Learning outcomes" refer to the results that demonstrate the knowledge, skills, and level of understanding that learners acquire through educational activities. 【0273】 A "data analysis model" refers to computational methods and algorithms used to obtain insights and predictions tailored to specific purposes, based on collected data. 【0274】 A "terminal" is an electronic device used by educators and learners to receive and transmit information. 【0275】 "Educational information" refers to all information provided based on the educational plan, including teaching materials, assignments, and test questions. 【0276】 "Progress monitoring" refers to the activity of continuously observing and recording learners' learning status and achievements. 【0277】 "Feedback" refers to information provided for evaluation and guidance based on the results of monitoring and analysis. 【0278】 "Data cleansing" is the process of detecting and correcting missing values ​​and errors in data. 【0279】 "Visual presentation" refers to the activity of making information easier to understand by using visual media such as graphs and charts. 【0280】 This system is designed to efficiently and automatically generate and manage annual educational plans for educational institutions. The specific implementation of this system is described below. 【0281】 The server collects data such as the past education plans, test results, and learning outcomes of educational institutions. This data also includes publicly available information on the best practices of other educational institutions. The collected data is subjected to data cleansing using Python's Pandas library or SQL, and missing values are complemented and errors are corrected. 【0282】 The server executes machine learning algorithms based on the cleansed data. Data analysis models built using software such as Sci-kit Learn or TensorFlow generate optimal teaching plans for each subject and semester. This AI model has the ability to analyze data and propose an education plan for the next academic year. 【0283】 The generated education plan is directly distributed from the server to the terminals of educators and learners. Educators can view the plan using a tablet or a personal computer and make necessary corrections using Microsoft Excel or Google Sheets. Educational materials, assignments, and test questions are provided to the terminals via a learning management system (LMS). 【0284】 The terminal monitors the progress of the learner in real time and sends that data to the server. The user (educator) receives feedback from the dashboard provided by the server, enabling individualized instruction and additional support planning. Also, the server conducts comparative analysis with the data of other educational institutions to assist in correcting future education plans. 【0285】 As a specific example, in a certain school, an AI model proposed a new teaching plan based on past math teaching plans and test results. This plan included additional practice time to strengthen the weaknesses of the students, resulting in improved grades. As an example of a prompt sentence, a format such as "Please propose a teaching plan for the next academic year based on the math teaching plans and test results for the past three years" can be considered. 【0286】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0287】 Step 1: 【0288】 The server collects data on the past education plans, test results, and learning outcomes of educational institutions. The input is digitized past education-related data and public information on the best practices of other schools. The server uses scraping technology and APIs to collect data and stores it in a database to achieve centralized management of information. The output is a set of integrated education data. 【0289】 Step 2: 【0290】 The server applies data cleansing to the collected data. The input is education data containing missing values and errors. The server uses the Pandas library in Python to complement missing values with mean values or mode values and automatically correct obvious errors. The output is a cleansed and accurate dataset. 【0291】 Step 3: 【0292】 The server executes a machine learning model based on the cleansed data. The input is the prepared education data. The server utilizes Sci-kit Learn and TensorFlow to implement a data analysis model, applies machine learning algorithms, and analyzes the data. The output is an optimized education plan for the next year. 【0293】 Step 4: 【0294】 The server distributes the generated education plan to the terminals of educators and learners. The input is the education plan generated by the machine learning model. The server distributes the plan to the terminals through an education management platform so that educators can view and edit it. The output is education plan data accessible on the terminals. 【0295】 Step 5: 【0296】 The terminal monitors the learner's progress in real time. Inputs include the learner's assignment submission status and test results. The terminal collects progress data using a learning management system and automatically sends it to the server. Output is the progress data sent to the server. 【0297】 Step 6: 【0298】 The server analyzes progress data and provides feedback to educators. The input is learner progress data. The server analyzes the data and generates feedback based on performance on assignments and tests. Educators can use this feedback to create individualized instruction plans. The output is the feedback information provided to educators. 【0299】 Step 7: 【0300】 The server compares collected learning outcomes with data from other educational institutions. The inputs are the educational institution's learning outcomes data and publicly available benchmark data. The server uses BI tools to perform comparative analysis and generate revised educational plans. The output is the revised educational plan based on the analysis results. 【0301】 (Application Example 1) 【0302】 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." 【0303】 Developing annual educational plans in educational institutions requires extensive data analysis and planning, placing a significant burden on educators. Furthermore, customizing education to individual learners and adjusting plans based on performance comparisons with other institutions are cumbersome when done manually, highlighting the need for greater efficiency. Additionally, the lack of an environment that allows educators and learners to easily review and edit generated educational plans poses a problem for their implementation. 【0304】 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. 【0305】 In this invention, the server includes means for collecting information on past education plans, test results, and learning achievements, means for using an artificial intelligence model that generates an education plan using the collected information, and means for providing a terminal application that presents the generated education plan to the terminals of educators and learners and enables confirmation and modification. As a result, the formulation and management of education plans are automated, reducing the burden on educators and improving the feasibility and effectiveness of the plans. 【0306】 The "past education plan" refers to the outline of the learning program and schedule in an educational institution set previously. 【0307】 The "test results" refer to data including the grades and evaluations of tests taken by learners. 【0308】 The "learning achievements" are information indicating the knowledge and skills acquired by learners through an educational program. 【0309】 The "means for collecting information" refers to the process or technology for collecting necessary education-related data through a database or the Internet. 【0310】 The "artificial intelligence model" refers to an algorithm or system that analyzes a large amount of data to derive patterns and predictions. 【0311】 The "terminal application" is software that operates on a device used by a user and enables the provision and operation of information. 【0312】 The "educational materials" refer to teaching materials such as texts, videos, audio, and exercise problems used by learners during the learning process. 【0313】 "Means of monitoring learning progress" refers to the process or technology of tracking learners' learning status in real time and collecting that data. 【0314】 "Means of providing feedback" refers to functions that provide learners and educators with advice and information based on learning progress and evaluation. 【0315】 "Using prompt statements" refers to a method of inputting instructions in natural language into an AI model to obtain a specific output. 【0316】 The system that realizes this invention efficiently generates educational plans by collecting data on past educational plans, test results, and learning outcomes on a server and analyzing it with an artificial intelligence model. Specifically, the server extracts the necessary data from information sources such as databases and cleans the data using the Python programming language. After the data is prepared, it trains an artificial intelligence model using an AI framework such as TensorFlow and generates educational plans. 【0317】 The generated lesson plans are delivered to educators and learners through a terminal application developed using React Native. This application allows educators to review and edit the plan, while learners can access necessary educational materials using QR codes and an intuitive user interface. The terminal also monitors learning progress in real time and sends this data back to the server. The server analyzes this data and provides individualized feedback to educators. 【0318】 As a concrete example, in a middle school mathematics class, using this system allows educators to gain a detailed understanding of each student's learning progress. Based on this, the AI ​​can suggest optimal practice problems, thereby improving the learners' comprehension. 【0319】 An example of an input prompt for the generating AI model is expected to be the text, "Based on past educational plan data and test results, please generate the optimal lesson plans for mathematics, Japanese language, and English for the new school year." By using such prompts, the AI ​​will be able to optimize the educational plans. 【0320】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0321】 Step 1: 【0322】 The server collects past educational plans, test results, and learning outcomes from the educational institution's database and external sources. This data is retrieved via an API and imported into the server in JSON format. 【0323】 Step 2: 【0324】 The server cleanses the collected raw data using the Python programming language and the Pandas library. After performing processes such as imputing missing values ​​and ensuring data consistency, it generates a clean dataset. The input to this process is raw data in JSON format, and the output is cleansed structured data. 【0325】 Step 3: 【0326】 The server feeds a clean dataset into an AI model built using TensorFlow to generate educational plans. This model executes machine learning algorithms to output optimal class schedules for each subject and semester. The input is cleansed structured data, and the output is an optimized educational plan. 【0327】 Step 4: 【0328】 The server delivers the generated lesson plan to the terminal application. This application is developed in React Native and provides an interface accessible to both educators and learners. The input is the optimized lesson plan, and the output is the display of the lesson plan on the terminal. 【0329】 Step 5: 【0330】 The terminal monitors the learner's learning progress in real time and sends the data to the server. This monitoring is performed by collecting user interaction data and learning status logs. The input is user interaction data, and the output is the aggregation of progress data on the server. 【0331】 Step 6: 【0332】 The server analyzes progress data and provides individualized feedback to educators. This feedback is generated through data analysis using Python and sent to the educator's terminal in text format. The input is student-specific progress data, and the output is feedback information. 【0333】 Step 7: 【0334】 The user enters a prompt to review or edit the content of the educational plan. The server re-evaluates the AI ​​model based on this prompt and modifies the plan as needed. The input is the prompt, and the output is the updated educational plan. 【0335】 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. 【0336】 This invention provides a system that improves the quality of education by incorporating an emotion engine that recognizes the user's emotions and adjusts educational activities based on those emotions into a system that efficiently generates educational plans using past educational plans and test results. The operation of the processing in this system is described below. 【0337】 First, the server collects information from a database, such as past teaching plans, test results, and learning outcomes. This data is then analyzed by an artificial intelligence model. Based on the analysis, the server generates an optimized teaching plan. This plan is delivered to the educator's terminal, where they can review the information and make modifications as needed. 【0338】 Furthermore, the server selects learning materials appropriate to the learning content based on the collected data. This includes materials that match the learner's past performance and educational goals. The selected materials are delivered directly to the learner's device. 【0339】 Furthermore, a key aspect of this invention is the integration of an emotion engine into the system that recognizes the user's emotions. The terminal is equipped with a camera and microphone, which are used to capture the learner's facial expressions and voice tone in real time. The server uses the emotion engine to analyze this data and estimate the learner's current emotional state—for example, whether they understand, are stressed, or are bored. 【0340】 Based on the emotional state identified by the emotion engine, the server dynamically adjusts the educational content. Specifically, if the server determines that the learner's understanding is insufficient, it provides additional supplementary materials and hints. Similarly, if motivation is low, the server can present encouraging messages and interactive content through the device. 【0341】 For example, suppose a student shows signs of anxiety when taking a unit test. The emotion engine recognizes this anxiety, and the server displays relaxing audio guidance and simple warm-up exercises on the student's device. This allows the learner to take the test in a more relaxed state. 【0342】 This emotional recognition-based adjustment function enables educators to provide individualized education tailored to each learner, which is expected to improve overall learning outcomes. 【0343】 The following describes the processing flow. 【0344】 Step 1: 【0345】 The server collects past educational plans, test results, and learning outcomes from a database. This lays the foundation for analyzing educational patterns and effectiveness. 【0346】 Step 2: 【0347】 The server detects missing data and uses statistical algorithms to fill in the gaps. This process enhances data integrity. 【0348】 Step 3: 【0349】 The server runs an artificial intelligence model using the consistent data to generate an optimal educational plan. The AI ​​builds the plan based on past trends and future goals. 【0350】 Step 4: 【0351】 The generated lesson plans are distributed from the server to the terminals. The terminals then send notifications to the teachers, prompting them to review the plans and make any necessary revisions. 【0352】 Step 5: 【0353】 The device captures the user's facial expressions and voice through its camera and microphone. This data is transmitted to the server in real time. 【0354】 Step 6: 【0355】 The server's emotion engine analyzes the received visual and auditory data to estimate the user's emotional state. For example, it can determine whether the user is stressed or unable to concentrate. 【0356】 Step 7: 【0357】 The server dynamically adjusts educational content based on the results of the emotion engine. If it is estimated that there is a lack of understanding, it delivers additional support materials to the device. 【0358】 Step 8: 【0359】 The device displays the user with adjusted educational materials to help improve their understanding. It also presents interactive content to increase engagement. 【0360】 Step 9: 【0361】 Users progress through their learning using the provided content. Progress data and sentiment data continue to be sent from the device to the server and used to inform subsequent actions. 【0362】 (Example 2) 【0363】 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". 【0364】 Modern education demands individualized instruction tailored to each learner's level of understanding and psychological state, but traditional systems struggle to accurately grasp and adjust these factors in real time. Furthermore, there is a need to provide education that considers learners' emotional states while efficiently utilizing past educational plans and outcomes. 【0365】 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. 【0366】 In this invention, the server includes means for collecting information on past plans, test results, and outcomes; means for using a model to generate plans using the collected information; and means for collecting and analyzing emotional information. This makes it possible to precisely understand the current state of learners and provide effective education tailored to their individual psychological states. 【0367】 "Past plans" refer to past strategies and progress in education, and serve as foundational information for analyzing learner performance. 【0368】 "Test results" refer to the grades and evaluations of tests taken by learners, and are objective information that shows each individual's level of understanding and achievements. 【0369】 "Outcomes" are indicators that show the level of skills and knowledge acquired by learners through educational activities. 【0370】 "Means of collection" refers to methods and techniques for efficiently gathering necessary information. 【0371】 A "model" is an algorithm or system designed for a specific purpose, which processes data to derive the optimal solution. 【0372】 "Means of distribution" refers to the technologies and methods for transmitting generated information and educational materials to the appropriate target audience. 【0373】 "Optimal materials" refer to teaching materials and information that are most suitable for the learner's needs and progress. 【0374】 "Emotional information" refers to data that indicates the psychological state of learners and is a factor that influences the learning environment and outcomes. 【0375】 "Means of analysis" refer to methods and techniques for analyzing collected data in detail and understanding its meaning and trends. 【0376】 "Means of monitoring progress" refer to methods for continuously tracking and evaluating learners' activities and progress. 【0377】 "Means of providing feedback" refer to methods for presenting learners and educators with improvements and indicators based on the analysis results. 【0378】 This invention is an innovative system that efficiently generates educational plans and provides dynamic education based on user emotions. The server collects past plans, test results, and learning outcomes from a database and utilizes a generative AI model to generate educational plans based on this information. This model uses machine learning libraries such as TensorFlow and can highly analyze the collected data. The analyzed results are delivered to the educator's terminal as an optimized educational plan. 【0379】 Furthermore, the server collects and analyzes emotional information to understand the learner's psychological state. For this purpose, the camera and microphone installed on each learner's terminal are used to capture facial expressions and voice in real time. This data is sent to the server and analyzed by the emotion engine. Specifically, facial expression analysis is performed using libraries such as OpenCV to estimate the learner's psychological state. 【0380】 Based on the results of sentiment analysis, the server dynamically adjusts the educational content. For example, if a learner is having difficulty understanding, it can provide additional supplementary materials or hints. Furthermore, if it determines that motivation is low, it displays encouraging messages or interactive content on the learner's device. 【0381】 For example, suppose a user is feeling stressed before a math test. When the emotion engine detects this, the server sends a relaxing audio guide or warm-up questions to the user's device. This allows the user to approach the test in a calm state. 【0382】 An example of a prompt used in this system would be, "Please provide something to help you relax before the math test." This enables personalized education that takes into account the learner's psychological and emotional state. 【0383】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0384】 Step 1: 【0385】 The server collects data on past plans, test results, and learning outcomes from the database. Input is done using SQL queries, and output is a data frame summarizing the collected data. This data frame is used in the next analysis step. 【0386】 Step 2: 【0387】 The server inputs the collected data into a generating AI model for analysis. This AI model is built using the TensorFlow library. The input is the data frame obtained in the previous step, and the output is the trend analysis results used to optimize the educational plan. This analysis predicts what kind of plan will be effective. 【0388】 Step 3: 【0389】 The server generates an educational plan based on the analysis results of the AI ​​model. The generated plan is output in PDF format and delivered to the educator's terminal. It is then delivered as an email using the SMTP protocol, and the educator reviews its contents. At this point, the educator can revise the plan as needed. 【0390】 Step 4: 【0391】 The server selects learning materials that are appropriate to the learner's needs based on the analysis results. The input is the analysis results obtained in the previous stage, and the output is a list of learning materials delivered to the learner's device. Past performance and learning objectives are taken into consideration in this selection. The learning materials are provided as PDFs, videos, or interactive content. 【0392】 Step 5: 【0393】 The device uses a camera and microphone to capture the learner's emotional state in real time. Input consists of the learner's facial expressions and voice data, which the device sends to a server. Output is a dataset for emotion analysis. This data is processed using a facial expression analysis API. 【0394】 Step 6: 【0395】 The server analyzes the received emotional data and estimates the learner's psychological state. The input is the emotional data obtained in step 5, and the output is an assessment of the learner's current emotional state. Here, it determines whether the learner is confused, stressed, or bored. 【0396】 Step 7: 【0397】 The server dynamically adjusts educational content based on the sentiment analysis results. The input is an assessment of the emotional state, and the output is improved educational materials or motivational content. Specifically, if there is a lack of understanding, additional materials are provided, and if motivation is low, encouraging messages are displayed on the device. 【0398】 (Application Example 2) 【0399】 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." 【0400】 Traditional education systems have faced challenges in recognizing and effectively responding to learners' emotions and learning progress in real time, making it difficult to provide individually optimized education. In particular, in home environments, limited learning support makes it difficult to maintain learners' motivation and learning effectiveness. This results in a decline in the quality of education and makes it difficult to achieve effective learning outcomes. 【0401】 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. 【0402】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes; means for using an artificial intelligence model to generate educational plans using the collected information; means for distributing the generated educational plans; and means for recognizing learners' emotions and dynamically adjusting educational activities based on those emotions. This makes it possible to provide individually optimized education to learners in real time. 【0403】 "Past educational plans" refers to the teaching policies and learning program designs that were previously formulated in educational activities. 【0404】 "Test results" refer to the results of tests used to evaluate the achievements of learners in educational activities. 【0405】 "Learning outcomes" refer to the extent to which learners have acquired knowledge and skills through educational activities. 【0406】 An "artificial intelligence model" is an algorithm that uses large amounts of data to make predictions and decisions for performing specific tasks. 【0407】 A "terminal" is a type of computing device capable of displaying and inputting information, and is used for receiving and displaying educational content. 【0408】 "Educational materials" refer to teaching materials and information resources that learners use to study. 【0409】 "Monitoring" is the process of continuously observing and recording the state and trends of a specific subject, and taking action as needed. 【0410】 "Feedback" refers to evaluations and suggestions provided to learners to encourage improvement and growth. 【0411】 "Recognizing emotions" is the process of determining a user's emotional state at any given moment through their facial expressions, voice, and other actions. 【0412】 "Dynamic adjustment" means changing systems and processes in real time in response to changes in circumstances and needs. 【0413】 To realize this application, the system is implemented using a combination of specific hardware and software. The server collects information on past teaching plans, test results, and learning outcomes from a database, and uses an artificial intelligence model to generate an optimal teaching plan based on this data. The generated teaching plan is then sent to the educator's terminal and used as the basis for teaching activities. 【0414】 Furthermore, the server uses devices such as cameras and microphones to capture the learner's facial expressions and voice in order to recognize the user's emotions. This data is analyzed by an emotion recognition engine (for example, Microsoft Azure's Face API). Based on the emotional state obtained from the analysis, the server dynamically adjusts the educational content. For example, if the server estimates that the learner is feeling stressed, it will send a voice message to help them relax or simple warm-up exercises to the learner's device to support their learning experience. 【0415】 The goal of this system is to provide personalized education to individual learners and support efficient and effective learning. The generative AI model optimizes the teaching plan and materials based on prompts. For example, a prompt might be: "This child has lost interest in today's math assignment. What would you suggest to rekindle his interest?" 【0416】 These technologies enable the system to provide appropriate educational content tailored to the learner's needs, thereby improving learning outcomes. 【0417】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0418】 Step 1: 【0419】 The server collects information on past educational plans, test results, and learning outcomes from a database. The input data consists of educational data provided by educational institutions, and based on this, an artificial intelligence model begins generating educational plans. The output is a dataset in the format necessary for analysis. At this stage, the main operations are data collection and organization. 【0420】 Step 2: 【0421】 The server uses the collected data to feed into an artificial intelligence model to generate an optimal educational plan. The input is educational information retrieved from a database, and data processing includes cleaning and pre-processing. The output is a detailed educational plan sent to the educator's terminal. Here, the AI ​​model analyzes the data and constructs the optimal learning plan. 【0422】 Step 3: 【0423】 The device captures the learner's facial expressions and voice through its camera and microphone and sends them to the server. The input is real-time audio and visual data. The output is formatted data for analysis based on this data. In this step, data on the user's emotions is collected through the device. 【0424】 Step 4: 【0425】 The server analyzes the learner's emotions from the acquired data using an emotion recognition engine. The input is video and audio data transmitted from the terminal, and an emotion recognition algorithm is applied as data processing. The output is information indicating the learner's emotional state. Specifically, the server calls an emotion recognition API to analyze emotions. 【0426】 Step 5: 【0427】 The server adjusts the educational content based on emotion analysis. For example, if the input indicates that the learner is experiencing stress, the server provides additional educational materials to promote relaxation. The output is the adjusted educational plan and supplementary materials. In this step, the AI ​​model generates the responses to enhance support for the learner. 【0428】 Step 6: 【0429】 The terminal displays pre-configured educational content provided by the server to the learner, facilitating interaction. Input is the instructions from the server, and output is feedback information based on the user's interaction. The system then captures how the user perceived the task and provides feedback accordingly. 【0430】 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. 【0431】 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. 【0432】 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. 【0433】 [Third Embodiment] 【0434】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0435】 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. 【0436】 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). 【0437】 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. 【0438】 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. 【0439】 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). 【0440】 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. 【0441】 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. 【0442】 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. 【0443】 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. 【0444】 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. 【0445】 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". 【0446】 This invention is a system that supports the automatic generation and efficient management of annual educational plans in educational institutions. To achieve this, it performs the following operations. 【0447】 As one form of the invention, the server collects relevant data such as past educational plans, test results, and learning outcomes from a school or educational institution. This data includes not only digitally recorded information but also publicly available data on best practices from other schools. The server organizes this information and corrects missing values ​​and errors using data cleansing techniques. 【0448】 Next, the server runs an artificial intelligence model to generate educational plans based on the collected clean data. This model uses machine learning algorithms to analyze the data and proposes the optimal lesson plan for each subject and semester. 【0449】 The generated lesson plans are delivered directly from the server to the educators' and learners' devices. Educators receive these plans via tablets or computers and can check and modify them as needed. Learning materials, assignments, and test questions based on the plan are automatically provided to the learners' devices. 【0450】 Furthermore, the terminal has a function to monitor learners' progress in real time, and the progress data generated based on this is sent to the server. The server analyzes this data and provides individualized feedback to educators. This feedback serves as important reference information for educators when providing individualized instruction. 【0451】 Furthermore, the server generates information to optimize the school's own educational plan by comparing the accumulated learning outcomes with those of other educational institutions. Based on this comparative analysis, revised plans are proposed and used when formulating the educational plan for the following year. 【0452】 As a concrete example, in one school, an AI model proposed a new lesson plan based on past math lesson plans and test results. This plan included additional practice time to address students' weaknesses, resulting in improved overall grades. This freed teachers from the daily task of preparing materials, allowing them to focus on interacting with students and providing individualized instruction. 【0453】 The following describes the processing flow. 【0454】 Step 1: 【0455】 The server automatically collects data on past educational plans, test results, and learning outcomes. This includes access from the school's internal database and cloud storage. The server stores the collected data while maintaining consistency and avoiding duplication. 【0456】 Step 2: 【0457】 The server cleanses the collected data. During this process, the server detects missing values ​​and uses statistical methods to estimate and impute them. It also detects and corrects obvious data errors. 【0458】 Step 3: 【0459】 The server generates educational plans using an artificial intelligence model based on the cleansed data. This model utilizes machine learning algorithms to extract trends and patterns from the data and design an optimized plan. 【0460】 Step 4: 【0461】 The generated lesson plan is distributed from the server to the teacher's terminal. The terminal notifies the teacher that a new plan has arrived via a notification function. The teacher can review the plan and make modifications as needed. 【0462】 Step 5: 【0463】 The server selects or creates the most suitable educational materials and tests based on the educational plan. These materials are provided to learners' devices and are automatically updated as needed. 【0464】 Step 6: 【0465】 The device monitors the learner's learning progress. Progress data is collected based on the learner's activity logs and test responses, and this information is sent to the server. 【0466】 Step 7: 【0467】 The server analyzes progress data and generates personalized feedback. This feedback is provided to the teacher's terminal and used to determine the teaching strategy for the students. 【0468】 Step 8: 【0469】 The server compares the collected learning outcomes with data from other educational institutions. Through this process, the school identifies areas for improvement and strengths in its curriculum, and adjusts its plans for the following year as needed. 【0470】 (Example 1) 【0471】 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." 【0472】 Developing educational plans in educational institutions requires the proper organization and analysis of multiple data and information, which is time-consuming and labor-intensive. Furthermore, a lack of progress monitoring and feedback for individual learners can lead to missed opportunities for personalized instruction. Additionally, incomplete comparative analyses with other educational institutions can result in unoptimized plans. These challenges hinder the improvement of educational quality. 【0473】 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. 【0474】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes of educational institutions; means for using a data analysis model to generate educational plans using the collected information; and means for distributing the generated educational plans to the terminals of educators and learners. This enables the efficient generation of educational plans and the provision of progress management and feedback tailored to individual learners. 【0475】 An "educational plan" is a plan that systematically and comprehensively arranges the lessons, assignments, and other educational activities that an educational institution will provide over a specific period of time. 【0476】 "Test results" refer to information that shows the grades and evaluation scores of tests taken by learners. 【0477】 "Learning outcomes" refer to the results that demonstrate the knowledge, skills, and level of understanding that learners acquire through educational activities. 【0478】 A "data analysis model" refers to computational methods and algorithms used to obtain insights and predictions tailored to specific purposes, based on collected data. 【0479】 A "terminal" is an electronic device used by educators and learners to receive and transmit information. 【0480】 "Educational information" refers to all information provided based on the educational plan, including teaching materials, assignments, and test questions. 【0481】 "Progress monitoring" refers to the activity of continuously observing and recording learners' learning status and achievements. 【0482】 "Feedback" refers to information provided for evaluation and guidance based on the results of monitoring and analysis. 【0483】 "Data cleansing" is the process of detecting and correcting missing values ​​and errors in data. 【0484】 "Visual presentation" refers to the activity of making information easier to understand by using visual media such as graphs and charts. 【0485】 This system is designed to efficiently and automatically generate and manage annual educational plans for educational institutions. The specific implementation of this system is described below. 【0486】 The server collects data from educational institutions, including past teaching plans, test results, and learning outcomes. This data also includes publicly available information on best practices from other educational institutions. The collected data is cleansed using Python's Pandas library and SQL, with missing values ​​imputed and errors corrected. 【0487】 The server executes machine learning algorithms based on the cleansed data. A data analysis model, built using software such as Sci-kit Learn and TensorFlow, generates optimal lesson plans for each subject and semester. This AI model has the ability to analyze data and propose educational plans for the following academic year. 【0488】 The generated lesson plans are delivered directly from the server to the educators' and learners' devices. Educators can review the plans using tablets or PCs and make necessary modifications using Microsoft Excel or Google Sheets. Educational materials, assignments, and test questions are provided to the devices via the Learning Management System (LMS). 【0489】 The terminal monitors learners' progress in real time and sends the data to the server. Users (educators) receive feedback from a dashboard provided by the server, enabling them to plan individualized instruction and additional support. The server also performs comparative analysis with data from other educational institutions to help adjust future teaching plans. 【0490】 As a concrete example, in one school, an AI model proposed a new lesson plan based on past math lesson plans and test results. This plan included additional practice time to strengthen students' weaknesses, resulting in improved grades. An example of a prompt would be, "Please propose a lesson plan for the next academic year based on the math lesson plans and test results from the past three years." 【0491】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0492】 Step 1: 【0493】 The server collects data on educational institutions' past teaching plans, test results, and learning outcomes. Inputs include digitized historical education data and publicly available information on best practices from other schools. The server achieves centralized information management by collecting data using scraping techniques and APIs and storing it in a database. Output is a unified set of educational data. 【0494】 Step 2: 【0495】 The server applies data cleansing to the collected data. The input is training data containing missing values ​​and errors. The server uses the Python Pandas library to impute missing values ​​with mean or mode values ​​and automatically correct obvious errors. The output is a cleansed, accurate dataset. 【0496】 Step 3: 【0497】 The server runs a machine learning model based on cleansed data. The input is well-organized educational data. The server implements a data analysis model using Sci-kit Learn and TensorFlow, applies machine learning algorithms, and analyzes the data. The output is an optimized educational plan for the following year. 【0498】 Step 4: 【0499】 The server distributes the generated lesson plans to the educators' and learners' devices. The input is the lesson plan generated by a machine learning model. The server distributes the plan to the devices via the educational management platform, allowing educators to view and edit it. The output is the lesson plan data accessible on the devices. 【0500】 Step 5: 【0501】 The terminal monitors the learner's progress in real time. Inputs include the learner's assignment submission status and test results. The terminal collects progress data using a learning management system and automatically sends it to the server. Output is the progress data sent to the server. 【0502】 Step 6: 【0503】 The server analyzes progress data and provides feedback to educators. The input is learner progress data. The server analyzes the data and generates feedback based on performance on assignments and tests. Educators can use this feedback to create individualized instruction plans. The output is the feedback information provided to educators. 【0504】 Step 7: 【0505】 The server compares collected learning outcomes with data from other educational institutions. The inputs are the educational institution's learning outcomes data and publicly available benchmark data. The server uses BI tools to perform comparative analysis and generate revised educational plans. The output is the revised educational plan based on the analysis results. 【0506】 (Application Example 1) 【0507】 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." 【0508】 Developing annual educational plans in educational institutions requires extensive data analysis and planning, placing a significant burden on educators. Furthermore, customizing education to individual learners and adjusting plans based on performance comparisons with other institutions are cumbersome when done manually, highlighting the need for greater efficiency. Additionally, the lack of an environment that allows educators and learners to easily review and edit generated educational plans poses a problem for their implementation. 【0509】 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. 【0510】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes; means for using an artificial intelligence model to generate educational plans using the collected information; and means for providing a terminal application that presents the generated educational plans to the terminals of educators and learners, allowing them to review and modify them. This automates the formulation and management of educational plans, reduces the burden on educators, and improves the feasibility and effectiveness of the plans. 【0511】 "Past educational plans" refers to outlines of learning programs and schedules previously established at educational institutions. 【0512】 "Test results" refers to data that includes the grades and evaluations of tests taken by learners. 【0513】 "Learning outcomes" refer to information that demonstrates the knowledge and skills that learners have acquired through an educational program. 【0514】 "Means of information gathering" refers to the process or technology of collecting necessary education-related data through databases or the internet. 【0515】 An "artificial intelligence model" refers to an algorithm or system that analyzes large amounts of data to derive patterns and predictions. 【0516】 A "terminal application" is software that runs on a user's device and enables the provision of information and operation. 【0517】 "Educational materials" refer to learning materials such as textbooks, videos, audiobooks, and practice problems that learners use during their learning process. 【0518】 "Means of monitoring learning progress" refers to the process or technology of tracking learners' learning status in real time and collecting that data. 【0519】 "Means of providing feedback" refers to functions that provide learners and educators with advice and information based on learning progress and evaluation. 【0520】 "Using prompt statements" refers to a method of inputting instructions in natural language into an AI model to obtain a specific output. 【0521】 The system that realizes this invention efficiently generates educational plans by collecting data on past educational plans, test results, and learning outcomes on a server and analyzing it with an artificial intelligence model. Specifically, the server extracts the necessary data from information sources such as databases and cleans the data using the Python programming language. After the data is prepared, it trains an artificial intelligence model using an AI framework such as TensorFlow and generates educational plans. 【0522】 The generated lesson plans are delivered to educators and learners through a terminal application developed using React Native. This application allows educators to review and edit the plan, while learners can access necessary educational materials using QR codes and an intuitive user interface. The terminal also monitors learning progress in real time and sends this data back to the server. The server analyzes this data and provides individualized feedback to educators. 【0523】 As a concrete example, in a middle school mathematics class, using this system allows educators to gain a detailed understanding of each student's learning progress. Based on this, the AI ​​can suggest optimal practice problems, thereby improving the learners' comprehension. 【0524】 An example of an input prompt for the generating AI model is expected to be the text, "Based on past educational plan data and test results, please generate the optimal lesson plans for mathematics, Japanese language, and English for the new school year." By using such prompts, the AI ​​will be able to optimize the educational plans. 【0525】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0526】 Step 1: 【0527】 The server collects past educational plans, test results, and learning outcomes from the educational institution's database and external sources. This data is retrieved via an API and imported into the server in JSON format. 【0528】 Step 2: 【0529】 The server cleanses the collected raw data using the Python programming language and the Pandas library. After performing processes such as imputing missing values ​​and ensuring data consistency, it generates a clean dataset. The input to this process is raw data in JSON format, and the output is cleansed structured data. 【0530】 Step 3: 【0531】 The server feeds a clean dataset into an AI model built using TensorFlow to generate educational plans. This model executes machine learning algorithms to output optimal class schedules for each subject and semester. The input is cleansed structured data, and the output is an optimized educational plan. 【0532】 Step 4: 【0533】 The server delivers the generated lesson plan to the terminal application. This application is developed in React Native and provides an interface accessible to both educators and learners. The input is the optimized lesson plan, and the output is the display of the lesson plan on the terminal. 【0534】 Step 5: 【0535】 The terminal monitors the learner's learning progress in real time and sends the data to the server. This monitoring is performed by collecting user interaction data and learning status logs. The input is user interaction data, and the output is the aggregation of progress data on the server. 【0536】 Step 6: 【0537】 The server analyzes progress data and provides individualized feedback to educators. This feedback is generated through data analysis using Python and sent to the educator's terminal in text format. The input is student-specific progress data, and the output is feedback information. 【0538】 Step 7: 【0539】 The user enters a prompt to review or edit the content of the educational plan. The server re-evaluates the AI ​​model based on this prompt and modifies the plan as needed. The input is the prompt, and the output is the updated educational plan. 【0540】 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. 【0541】 This invention provides a system that improves the quality of education by incorporating an emotion engine that recognizes the user's emotions and adjusts educational activities based on those emotions into a system that efficiently generates educational plans using past educational plans and test results. The operation of the processing in this system is described below. 【0542】 First, the server collects information from a database, such as past teaching plans, test results, and learning outcomes. This data is then analyzed by an artificial intelligence model. Based on the analysis, the server generates an optimized teaching plan. This plan is delivered to the educator's terminal, where they can review the information and make modifications as needed. 【0543】 Furthermore, the server selects learning materials appropriate to the learning content based on the collected data. This includes materials that match the learner's past performance and educational goals. The selected materials are delivered directly to the learner's device. 【0544】 Furthermore, a key aspect of this invention is the integration of an emotion engine into the system that recognizes the user's emotions. The terminal is equipped with a camera and microphone, which are used to capture the learner's facial expressions and voice tone in real time. The server uses the emotion engine to analyze this data and estimate the learner's current emotional state—for example, whether they understand, are stressed, or are bored. 【0545】 Based on the emotional state identified by the emotion engine, the server dynamically adjusts the educational content. Specifically, if the server determines that the learner's understanding is insufficient, it provides additional supplementary materials and hints. Similarly, if motivation is low, the server can present encouraging messages and interactive content through the device. 【0546】 For example, suppose a student shows signs of anxiety when taking a unit test. The emotion engine recognizes this anxiety, and the server displays relaxing audio guidance and simple warm-up exercises on the student's device. This allows the learner to take the test in a more relaxed state. 【0547】 This emotional recognition-based adjustment function enables educators to provide individualized education tailored to each learner, which is expected to improve overall learning outcomes. 【0548】 The following describes the processing flow. 【0549】 Step 1: 【0550】 The server collects past educational plans, test results, and learning outcomes from a database. This lays the foundation for analyzing educational patterns and effectiveness. 【0551】 Step 2: 【0552】 The server detects missing data and uses statistical algorithms to fill in the gaps. This process enhances data integrity. 【0553】 Step 3: 【0554】 The server runs an artificial intelligence model using the consistent data to generate an optimal educational plan. The AI ​​builds the plan based on past trends and future goals. 【0555】 Step 4: 【0556】 The generated lesson plans are distributed from the server to the terminals. The terminals then send notifications to the teachers, prompting them to review the plans and make any necessary revisions. 【0557】 Step 5: 【0558】 The device captures the user's facial expressions and voice through its camera and microphone. This data is transmitted to the server in real time. 【0559】 Step 6: 【0560】 The server's emotion engine analyzes the received visual and auditory data to estimate the user's emotional state. For example, it can determine whether the user is stressed or unable to concentrate. 【0561】 Step 7: 【0562】 The server dynamically adjusts educational content based on the results of the emotion engine. If it is estimated that there is a lack of understanding, it delivers additional support materials to the device. 【0563】 Step 8: 【0564】 The device displays the user with adjusted educational materials to help improve their understanding. It also presents interactive content to increase engagement. 【0565】 Step 9: 【0566】 Users progress through their learning using the provided content. Progress data and sentiment data continue to be sent from the device to the server and used to inform subsequent actions. 【0567】 (Example 2) 【0568】 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." 【0569】 Modern education demands individualized instruction tailored to each learner's level of understanding and psychological state, but traditional systems struggle to accurately grasp and adjust these factors in real time. Furthermore, there is a need to provide education that considers learners' emotional states while efficiently utilizing past educational plans and outcomes. 【0570】 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. 【0571】 In this invention, the server includes means for collecting information on past plans, test results, and outcomes; means for using a model to generate plans using the collected information; and means for collecting and analyzing emotional information. This makes it possible to precisely understand the current state of learners and provide effective education tailored to their individual psychological states. 【0572】 "Past plans" refer to past strategies and progress in education, and serve as foundational information for analyzing learner performance. 【0573】 "Test results" refer to the grades and evaluations of tests taken by learners, and are objective information that shows each individual's level of understanding and achievements. 【0574】 "Outcomes" are indicators that show the level of skills and knowledge acquired by learners through educational activities. 【0575】 "Means of collection" refers to methods and techniques for efficiently gathering necessary information. 【0576】 A "model" is an algorithm or system designed for a specific purpose, which processes data to derive the optimal solution. 【0577】 "Means of distribution" refers to the technologies and methods for transmitting generated information and educational materials to the appropriate target audience. 【0578】 "Optimal materials" refer to teaching materials and information that are most suitable for the learner's needs and progress. 【0579】 "Emotional information" refers to data that indicates the psychological state of learners and is a factor that influences the learning environment and outcomes. 【0580】 "Means of analysis" refer to methods and techniques for analyzing collected data in detail and understanding its meaning and trends. 【0581】 "Means of monitoring progress" refer to methods for continuously tracking and evaluating learners' activities and progress. 【0582】 "Means of providing feedback" refer to methods for presenting learners and educators with improvements and indicators based on the analysis results. 【0583】 This invention is an innovative system that efficiently generates educational plans and provides dynamic education based on user emotions. The server collects past plans, test results, and learning outcomes from a database and utilizes a generative AI model to generate educational plans based on this information. This model uses machine learning libraries such as TensorFlow and can highly analyze the collected data. The analyzed results are delivered to the educator's terminal as an optimized educational plan. 【0584】 Furthermore, the server collects and analyzes emotional information to understand the learner's psychological state. For this purpose, the camera and microphone installed on each learner's terminal are used to capture facial expressions and voice in real time. This data is sent to the server and analyzed by the emotion engine. Specifically, facial expression analysis is performed using libraries such as OpenCV to estimate the learner's psychological state. 【0585】 Based on the results of sentiment analysis, the server dynamically adjusts the educational content. For example, if a learner is having difficulty understanding, it can provide additional supplementary materials or hints. Furthermore, if it determines that motivation is low, it displays encouraging messages or interactive content on the learner's device. 【0586】 For example, suppose a user is feeling stressed before a math test. When the emotion engine detects this, the server sends a relaxing audio guide or warm-up questions to the user's device. This allows the user to approach the test in a calm state. 【0587】 An example of a prompt used in this system would be, "Please provide something to help you relax before the math test." This enables personalized education that takes into account the learner's psychological and emotional state. 【0588】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0589】 Step 1: 【0590】 The server collects data on past plans, test results, and learning outcomes from the database. Input is done using SQL queries, and output is a data frame summarizing the collected data. This data frame is used in the next analysis step. 【0591】 Step 2: 【0592】 The server inputs the collected data into a generating AI model for analysis. This AI model is built using the TensorFlow library. The input is the data frame obtained in the previous step, and the output is the trend analysis results used to optimize the educational plan. This analysis predicts what kind of plan will be effective. 【0593】 Step 3: 【0594】 The server generates an educational plan based on the analysis results of the AI ​​model. The generated plan is output in PDF format and delivered to the educator's terminal. It is then delivered as an email using the SMTP protocol, and the educator reviews its contents. At this point, the educator can revise the plan as needed. 【0595】 Step 4: 【0596】 The server selects learning materials that are appropriate to the learner's needs based on the analysis results. The input is the analysis results obtained in the previous stage, and the output is a list of learning materials delivered to the learner's device. Past performance and learning objectives are taken into consideration in this selection. The learning materials are provided as PDFs, videos, or interactive content. 【0597】 Step 5: 【0598】 The device uses a camera and microphone to capture the learner's emotional state in real time. Input consists of the learner's facial expressions and voice data, which the device sends to a server. Output is a dataset for emotion analysis. This data is processed using a facial expression analysis API. 【0599】 Step 6: 【0600】 The server analyzes the received emotional data and estimates the learner's psychological state. The input is the emotional data obtained in step 5, and the output is an assessment of the learner's current emotional state. Here, it determines whether the learner is confused, stressed, or bored. 【0601】 Step 7: 【0602】 The server dynamically adjusts educational content based on the sentiment analysis results. The input is an assessment of the emotional state, and the output is improved educational materials or motivational content. Specifically, if there is a lack of understanding, additional materials are provided, and if motivation is low, encouraging messages are displayed on the device. 【0603】 (Application Example 2) 【0604】 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." 【0605】 Traditional education systems have faced challenges in recognizing and effectively responding to learners' emotions and learning progress in real time, making it difficult to provide individually optimized education. In particular, in home environments, limited learning support makes it difficult to maintain learners' motivation and learning effectiveness. This results in a decline in the quality of education and makes it difficult to achieve effective learning outcomes. 【0606】 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. 【0607】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes; means for using an artificial intelligence model to generate educational plans using the collected information; means for distributing the generated educational plans; and means for recognizing learners' emotions and dynamically adjusting educational activities based on those emotions. This makes it possible to provide individually optimized education to learners in real time. 【0608】 "Past educational plans" refers to the teaching policies and learning program designs that were previously formulated in educational activities. 【0609】 "Test results" refer to the results of tests used to evaluate the achievements of learners in educational activities. 【0610】 "Learning outcomes" refer to the extent to which learners have acquired knowledge and skills through educational activities. 【0611】 An "artificial intelligence model" is an algorithm that uses large amounts of data to make predictions and decisions for performing specific tasks. 【0612】 A "terminal" is a type of computing device capable of displaying and inputting information, and is used for receiving and displaying educational content. 【0613】 "Educational materials" refer to teaching materials and information resources that learners use to study. 【0614】 "Monitoring" is the process of continuously observing and recording the state and trends of a specific subject, and taking action as needed. 【0615】 "Feedback" refers to evaluations and suggestions provided to learners to encourage improvement and growth. 【0616】 "Recognizing emotions" is the process of determining a user's emotional state at any given moment through their facial expressions, voice, and other actions. 【0617】 "Dynamic adjustment" means changing systems and processes in real time in response to changes in circumstances and needs. 【0618】 To realize this application, the system is implemented using a combination of specific hardware and software. The server collects information on past teaching plans, test results, and learning outcomes from a database, and uses an artificial intelligence model to generate an optimal teaching plan based on this data. The generated teaching plan is then sent to the educator's terminal and used as the basis for teaching activities. 【0619】 Furthermore, the server uses devices such as cameras and microphones to capture the learner's facial expressions and voice in order to recognize the user's emotions. This data is analyzed by an emotion recognition engine (for example, Microsoft Azure's Face API). Based on the emotional state obtained from the analysis, the server dynamically adjusts the educational content. For example, if the server estimates that the learner is feeling stressed, it will send a voice message to help them relax or simple warm-up exercises to the learner's device to support their learning experience. 【0620】 The goal of this system is to provide personalized education to individual learners and support efficient and effective learning. The generative AI model optimizes the teaching plan and materials based on prompts. For example, a prompt might be: "This child has lost interest in today's math assignment. What would you suggest to rekindle his interest?" 【0621】 These technologies enable the system to provide appropriate educational content tailored to the learner's needs, thereby improving learning outcomes. 【0622】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0623】 Step 1: 【0624】 The server collects information on past educational plans, test results, and learning outcomes from a database. The input data consists of educational data provided by educational institutions, and based on this, an artificial intelligence model begins generating educational plans. The output is a dataset in the format necessary for analysis. At this stage, the main operations are data collection and organization. 【0625】 Step 2: 【0626】 The server uses the collected data to feed into an artificial intelligence model to generate an optimal educational plan. The input is educational information retrieved from a database, and data processing includes cleaning and pre-processing. The output is a detailed educational plan sent to the educator's terminal. Here, the AI ​​model analyzes the data and constructs the optimal learning plan. 【0627】 Step 3: 【0628】 The device captures the learner's facial expressions and voice through its camera and microphone and sends them to the server. The input is real-time audio and visual data. The output is formatted data for analysis based on this data. In this step, data on the user's emotions is collected through the device. 【0629】 Step 4: 【0630】 The server analyzes the learner's emotions from the acquired data using an emotion recognition engine. The input is video and audio data transmitted from the terminal, and an emotion recognition algorithm is applied as data processing. The output is information indicating the learner's emotional state. Specifically, the server calls an emotion recognition API to analyze emotions. 【0631】 Step 5: 【0632】 The server adjusts the educational content based on emotion analysis. For example, if the input indicates that the learner is experiencing stress, the server provides additional educational materials to promote relaxation. The output is the adjusted educational plan and supplementary materials. In this step, the AI ​​model generates the responses to enhance support for the learner. 【0633】 Step 6: 【0634】 The terminal displays pre-configured educational content provided by the server to the learner, facilitating interaction. Input is the instructions from the server, and output is feedback information based on the user's interaction. The system then captures how the user perceived the task and provides feedback accordingly. 【0635】 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. 【0636】 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. 【0637】 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. 【0638】 [Fourth Embodiment] 【0639】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0640】 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. 【0641】 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). 【0642】 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. 【0643】 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. 【0644】 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). 【0645】 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. 【0646】 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. 【0647】 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. 【0648】 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. 【0649】 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. 【0650】 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. 【0651】 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". 【0652】 This invention is a system that supports the automatic generation and efficient management of annual educational plans in educational institutions. To achieve this, it performs the following operations. 【0653】 As one form of the invention, the server collects relevant data such as past educational plans, test results, and learning outcomes from a school or educational institution. This data includes not only digitally recorded information but also publicly available data on best practices from other schools. The server organizes this information and corrects missing values ​​and errors using data cleansing techniques. 【0654】 Next, the server runs an artificial intelligence model to generate educational plans based on the collected clean data. This model uses machine learning algorithms to analyze the data and proposes the optimal lesson plan for each subject and semester. 【0655】 The generated lesson plans are delivered directly from the server to the educators' and learners' devices. Educators receive these plans via tablets or computers and can check and modify them as needed. Learning materials, assignments, and test questions based on the plan are automatically provided to the learners' devices. 【0656】 Furthermore, the terminal has a function to monitor learners' progress in real time, and the progress data generated based on this is sent to the server. The server analyzes this data and provides individualized feedback to educators. This feedback serves as important reference information for educators when providing individualized instruction. 【0657】 Furthermore, the server generates information to optimize the school's own educational plan by comparing the accumulated learning outcomes with those of other educational institutions. Based on this comparative analysis, revised plans are proposed and used when formulating the educational plan for the following year. 【0658】 As a concrete example, in one school, an AI model proposed a new lesson plan based on past math lesson plans and test results. This plan included additional practice time to address students' weaknesses, resulting in improved overall grades. This freed teachers from the daily task of preparing materials, allowing them to focus on interacting with students and providing individualized instruction. 【0659】 The following describes the processing flow. 【0660】 Step 1: 【0661】 The server automatically collects data on past educational plans, test results, and learning outcomes. This includes access from the school's internal database and cloud storage. The server stores the collected data while maintaining consistency and avoiding duplication. 【0662】 Step 2: 【0663】 The server cleanses the collected data. During this process, the server detects missing values ​​and uses statistical methods to estimate and impute them. It also detects and corrects obvious data errors. 【0664】 Step 3: 【0665】 The server generates educational plans using an artificial intelligence model based on the cleansed data. This model utilizes machine learning algorithms to extract trends and patterns from the data and design an optimized plan. 【0666】 Step 4: 【0667】 The generated lesson plan is distributed from the server to the teacher's terminal. The terminal notifies the teacher that a new plan has arrived via a notification function. The teacher can review the plan and make modifications as needed. 【0668】 Step 5: 【0669】 The server selects or creates the most suitable educational materials and tests based on the educational plan. These materials are provided to learners' devices and are automatically updated as needed. 【0670】 Step 6: 【0671】 The device monitors the learner's learning progress. Progress data is collected based on the learner's activity logs and test responses, and this information is sent to the server. 【0672】 Step 7: 【0673】 The server analyzes progress data and generates personalized feedback. This feedback is provided to the teacher's terminal and used to determine the teaching strategy for the students. 【0674】 Step 8: 【0675】 The server compares the collected learning outcomes with data from other educational institutions. Through this process, the school identifies areas for improvement and strengths in its curriculum, and adjusts its plans for the following year as needed. 【0676】 (Example 1) 【0677】 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". 【0678】 Developing educational plans in educational institutions requires the proper organization and analysis of multiple data and information, which is time-consuming and labor-intensive. Furthermore, a lack of progress monitoring and feedback for individual learners can lead to missed opportunities for personalized instruction. Additionally, incomplete comparative analyses with other educational institutions can result in unoptimized plans. These challenges hinder the improvement of educational quality. 【0679】 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. 【0680】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes of educational institutions; means for using a data analysis model to generate educational plans using the collected information; and means for distributing the generated educational plans to the terminals of educators and learners. This enables the efficient generation of educational plans and the provision of progress management and feedback tailored to individual learners. 【0681】 An "educational plan" is a plan that systematically and comprehensively arranges the lessons, assignments, and other educational activities that an educational institution will provide over a specific period of time. 【0682】 "Test results" refer to information that shows the grades and evaluation scores of tests taken by learners. 【0683】 "Learning outcomes" refer to the results that demonstrate the knowledge, skills, and level of understanding that learners acquire through educational activities. 【0684】 A "data analysis model" refers to computational methods and algorithms used to obtain insights and predictions tailored to specific purposes, based on collected data. 【0685】 A "terminal" is an electronic device used by educators and learners to receive and transmit information. 【0686】 "Educational information" refers to all information provided based on the educational plan, including teaching materials, assignments, and test questions. 【0687】 "Progress monitoring" refers to the activity of continuously observing and recording learners' learning status and achievements. 【0688】 "Feedback" refers to information provided for evaluation and guidance based on the results of monitoring and analysis. 【0689】 "Data cleansing" is the process of detecting and correcting missing values ​​and errors in data. 【0690】 "Visual presentation" refers to the activity of making information easier to understand by using visual media such as graphs and charts. 【0691】 This system is designed to efficiently and automatically generate and manage annual educational plans for educational institutions. The specific implementation of this system is described below. 【0692】 The server collects data from educational institutions, including past teaching plans, test results, and learning outcomes. This data also includes publicly available information on best practices from other educational institutions. The collected data is cleansed using Python's Pandas library and SQL, with missing values ​​imputed and errors corrected. 【0693】 The server executes machine learning algorithms based on the cleansed data. A data analysis model, built using software such as Sci-kit Learn and TensorFlow, generates optimal lesson plans for each subject and semester. This AI model has the ability to analyze data and propose educational plans for the following academic year. 【0694】 The generated lesson plans are delivered directly from the server to the educators' and learners' devices. Educators can review the plans using tablets or PCs and make necessary modifications using Microsoft Excel or Google Sheets. Educational materials, assignments, and test questions are provided to the devices via the Learning Management System (LMS). 【0695】 The terminal monitors learners' progress in real time and sends the data to the server. Users (educators) receive feedback from a dashboard provided by the server, enabling them to plan individualized instruction and additional support. The server also performs comparative analysis with data from other educational institutions to help adjust future teaching plans. 【0696】 As a concrete example, in one school, an AI model proposed a new lesson plan based on past math lesson plans and test results. This plan included additional practice time to strengthen students' weaknesses, resulting in improved grades. An example of a prompt would be, "Please propose a lesson plan for the next academic year based on the math lesson plans and test results from the past three years." 【0697】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0698】 Step 1: 【0699】 The server collects data on educational institutions' past teaching plans, test results, and learning outcomes. Inputs include digitized historical education data and publicly available information on best practices from other schools. The server achieves centralized information management by collecting data using scraping techniques and APIs and storing it in a database. Output is a unified set of educational data. 【0700】 Step 2: 【0701】 The server applies data cleansing to the collected data. The input is training data containing missing values ​​and errors. The server uses the Python Pandas library to impute missing values ​​with mean or mode values ​​and automatically correct obvious errors. The output is a cleansed, accurate dataset. 【0702】 Step 3: 【0703】 The server runs a machine learning model based on cleansed data. The input is well-organized educational data. The server implements a data analysis model using Sci-kit Learn and TensorFlow, applies machine learning algorithms, and analyzes the data. The output is an optimized educational plan for the following year. 【0704】 Step 4: 【0705】 The server distributes the generated lesson plans to the educators' and learners' devices. The input is the lesson plan generated by a machine learning model. The server distributes the plan to the devices via the educational management platform, allowing educators to view and edit it. The output is the lesson plan data accessible on the devices. 【0706】 Step 5: 【0707】 The terminal monitors the learner's progress in real time. Inputs include the learner's assignment submission status and test results. The terminal collects progress data using a learning management system and automatically sends it to the server. Output is the progress data sent to the server. 【0708】 Step 6: 【0709】 The server analyzes progress data and provides feedback to educators. The input is learner progress data. The server analyzes the data and generates feedback based on performance on assignments and tests. Educators can use this feedback to create individualized instruction plans. The output is the feedback information provided to educators. 【0710】 Step 7: 【0711】 The server compares collected learning outcomes with data from other educational institutions. The inputs are the educational institution's learning outcomes data and publicly available benchmark data. The server uses BI tools to perform comparative analysis and generate revised educational plans. The output is the revised educational plan based on the analysis results. 【0712】 (Application Example 1) 【0713】 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". 【0714】 Developing annual educational plans in educational institutions requires extensive data analysis and planning, placing a significant burden on educators. Furthermore, customizing education to individual learners and adjusting plans based on performance comparisons with other institutions are cumbersome when done manually, highlighting the need for greater efficiency. Additionally, the lack of an environment that allows educators and learners to easily review and edit generated educational plans poses a problem for their implementation. 【0715】 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. 【0716】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes; means for using an artificial intelligence model to generate educational plans using the collected information; and means for providing a terminal application that presents the generated educational plans to the terminals of educators and learners, allowing them to review and modify them. This automates the formulation and management of educational plans, reduces the burden on educators, and improves the feasibility and effectiveness of the plans. 【0717】 "Past educational plans" refers to outlines of learning programs and schedules previously established at educational institutions. 【0718】 "Test results" refers to data that includes the grades and evaluations of tests taken by learners. 【0719】 "Learning outcomes" refer to information that demonstrates the knowledge and skills that learners have acquired through an educational program. 【0720】 "Means of information gathering" refers to the process or technology of collecting necessary education-related data through databases or the internet. 【0721】 An "artificial intelligence model" refers to an algorithm or system that analyzes large amounts of data to derive patterns and predictions. 【0722】 A "terminal application" is software that runs on a user's device and enables the provision of information and operations. 【0723】 "Educational materials" refer to learning materials such as textbooks, videos, audiobooks, and practice problems that learners use during their learning process. 【0724】 "Means of monitoring learning progress" refers to the process or technology of tracking learners' learning status in real time and collecting that data. 【0725】 "Means of providing feedback" refers to functions that provide learners and educators with advice and information based on learning progress and evaluation. 【0726】 "Using prompt statements" refers to a method of inputting instructions in natural language into an AI model to obtain a specific output. 【0727】 The system that realizes this invention efficiently generates educational plans by collecting data on past educational plans, test results, and learning outcomes on a server and analyzing it with an artificial intelligence model. Specifically, the server extracts the necessary data from information sources such as databases and cleans the data using the Python programming language. After the data is prepared, it trains an artificial intelligence model using an AI framework such as TensorFlow and generates educational plans. 【0728】 The generated lesson plans are delivered to educators and learners through a terminal application developed using React Native. This application allows educators to review and edit the plan, while learners can access necessary educational materials using QR codes and an intuitive user interface. The terminal also monitors learning progress in real time and sends this data back to the server. The server analyzes this data and provides individualized feedback to educators. 【0729】 As a concrete example, in a middle school mathematics class, using this system allows educators to gain a detailed understanding of each student's learning progress. Based on this, the AI ​​can suggest optimal practice problems, thereby improving the learners' comprehension. 【0730】 An example of an input prompt for the generating AI model is expected to be the text, "Based on past educational plan data and test results, please generate the optimal lesson plans for mathematics, Japanese language, and English for the new school year." By using such prompts, the AI ​​will be able to optimize the educational plans. 【0731】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0732】 Step 1: 【0733】 The server collects past educational plans, test results, and learning outcomes from the educational institution's database and external sources. This data is retrieved via an API and imported into the server in JSON format. 【0734】 Step 2: 【0735】 The server cleanses the collected raw data using the Python programming language and the Pandas library. After performing processes such as imputing missing values ​​and ensuring data consistency, it generates a clean dataset. The input to this process is raw data in JSON format, and the output is cleansed structured data. 【0736】 Step 3: 【0737】 The server feeds a clean dataset into an AI model built using TensorFlow to generate educational plans. This model executes machine learning algorithms to output optimal class schedules for each subject and semester. The input is cleansed structured data, and the output is an optimized educational plan. 【0738】 Step 4: 【0739】 The server delivers the generated lesson plan to the terminal application. This application is developed in React Native and provides an interface accessible to both educators and learners. The input is the optimized lesson plan, and the output is the display of the lesson plan on the terminal. 【0740】 Step 5: 【0741】 The terminal monitors the learner's learning progress in real time and sends the data to the server. This monitoring is performed by collecting user interaction data and learning status logs. The input is user interaction data, and the output is the aggregation of progress data on the server. 【0742】 Step 6: 【0743】 The server analyzes progress data and provides individualized feedback to educators. This feedback is generated through data analysis using Python and sent to the educator's terminal in text format. The input is student-specific progress data, and the output is feedback information. 【0744】 Step 7: 【0745】 The user enters a prompt to review or edit the content of the educational plan. The server re-evaluates the AI ​​model based on this prompt and modifies the plan as needed. The input is the prompt, and the output is the updated educational plan. 【0746】 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. 【0747】 This invention provides a system that improves the quality of education by incorporating an emotion engine that recognizes the user's emotions and adjusts educational activities based on those emotions into a system that efficiently generates educational plans using past educational plans and test results. The operation of the processing in this system is described below. 【0748】 First, the server collects information from a database, such as past teaching plans, test results, and learning outcomes. This data is then analyzed by an artificial intelligence model. Based on the analysis, the server generates an optimized teaching plan. This plan is delivered to the educator's terminal, where they can review the information and make modifications as needed. 【0749】 Furthermore, the server selects learning materials appropriate to the learning content based on the collected data. This includes materials that match the learner's past performance and educational goals. The selected materials are delivered directly to the learner's device. 【0750】 Furthermore, a key aspect of this invention is the integration of an emotion engine into the system that recognizes the user's emotions. The terminal is equipped with a camera and microphone, which are used to capture the learner's facial expressions and voice tone in real time. The server uses the emotion engine to analyze this data and estimate the learner's current emotional state—for example, whether they understand, are stressed, or are bored. 【0751】 Based on the emotional state identified by the emotion engine, the server dynamically adjusts the educational content. Specifically, if the server determines that the learner's understanding is insufficient, it provides additional supplementary materials and hints. Similarly, if motivation is low, the server can present encouraging messages and interactive content through the device. 【0752】 For example, suppose a student shows signs of anxiety when taking a unit test. The emotion engine recognizes this anxiety, and the server displays relaxing audio guidance and simple warm-up exercises on the student's device. This allows the learner to take the test in a more relaxed state. 【0753】 This emotional recognition-based adjustment function enables educators to provide individualized education tailored to each learner, which is expected to improve overall learning outcomes. 【0754】 The following describes the processing flow. 【0755】 Step 1: 【0756】 The server collects past educational plans, test results, and learning outcomes from a database. This lays the foundation for analyzing educational patterns and effectiveness. 【0757】 Step 2: 【0758】 The server detects missing data and uses statistical algorithms to fill in the gaps. This process enhances data integrity. 【0759】 Step 3: 【0760】 The server runs an artificial intelligence model using the consistent data to generate an optimal educational plan. The AI ​​builds the plan based on past trends and future goals. 【0761】 Step 4: 【0762】 The generated lesson plans are distributed from the server to the terminals. The terminals then send notifications to the teachers, prompting them to review the plans and make any necessary revisions. 【0763】 Step 5: 【0764】 The device captures the user's facial expressions and voice through its camera and microphone. This data is transmitted to the server in real time. 【0765】 Step 6: 【0766】 The server's emotion engine analyzes the received visual and auditory data to estimate the user's emotional state. For example, it can determine whether the user is stressed or unable to concentrate. 【0767】 Step 7: 【0768】 The server dynamically adjusts educational content based on the results of the emotion engine. If it is estimated that there is a lack of understanding, it delivers additional support materials to the device. 【0769】 Step 8: 【0770】 The device displays the user with adjusted educational materials to help improve their understanding. It also presents interactive content to increase engagement. 【0771】 Step 9: 【0772】 Users progress through their learning using the provided content. Progress data and sentiment data continue to be sent from the device to the server and used to inform subsequent actions. 【0773】 (Example 2) 【0774】 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". 【0775】 Modern education demands individualized instruction tailored to each learner's level of understanding and psychological state, but traditional systems struggle to accurately grasp and adjust these factors in real time. Furthermore, there is a need to provide education that considers learners' emotional states while efficiently utilizing past educational plans and outcomes. 【0776】 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. 【0777】 In this invention, the server includes means for collecting information on past plans, test results, and outcomes; means for using a model to generate plans using the collected information; and means for collecting and analyzing emotional information. This makes it possible to precisely understand the current state of learners and provide effective education tailored to their individual psychological states. 【0778】 "Past plans" refer to past strategies and progress in education, and serve as foundational information for analyzing learner performance. 【0779】 "Test results" refer to the grades and evaluations of tests taken by learners, and are objective information that shows each individual's level of understanding and achievements. 【0780】 "Outcomes" are indicators that show the level of skills and knowledge acquired by learners through educational activities. 【0781】 "Means of collection" refers to methods and techniques for efficiently gathering necessary information. 【0782】 A "model" is an algorithm or system designed for a specific purpose, which processes data to derive the optimal solution. 【0783】 "Means of distribution" refers to the technologies and methods for transmitting generated information and educational materials to the appropriate target audience. 【0784】 "Optimal materials" refer to teaching materials and information that are most suitable for the learner's needs and progress. 【0785】 "Emotional information" refers to data that indicates the psychological state of learners and is a factor that influences the learning environment and outcomes. 【0786】 "Means of analysis" refer to methods and techniques for analyzing collected data in detail and understanding its meaning and trends. 【0787】 "Means of monitoring progress" refer to methods for continuously tracking and evaluating learners' activities and progress. 【0788】 "Means of providing feedback" refer to methods for presenting learners and educators with improvements and indicators based on the analysis results. 【0789】 This invention is an innovative system that efficiently generates educational plans and provides dynamic education based on user emotions. The server collects past plans, test results, and learning outcomes from a database and utilizes a generative AI model to generate educational plans based on this information. This model uses machine learning libraries such as TensorFlow and can highly analyze the collected data. The analyzed results are delivered to the educator's terminal as an optimized educational plan. 【0790】 Furthermore, the server collects and analyzes emotional information to understand the learner's psychological state. For this purpose, the camera and microphone installed on each learner's terminal are used to capture facial expressions and voice in real time. This data is sent to the server and analyzed by the emotion engine. Specifically, facial expression analysis is performed using libraries such as OpenCV to estimate the learner's psychological state. 【0791】 Based on the results of sentiment analysis, the server dynamically adjusts the educational content. For example, if a learner is having difficulty understanding, it can provide additional supplementary materials or hints. Furthermore, if it determines that motivation is low, it displays encouraging messages or interactive content on the learner's device. 【0792】 For example, suppose a user is feeling stressed before a math test. When the emotion engine detects this, the server sends a relaxing audio guide or warm-up questions to the user's device. This allows the user to approach the test in a calm state. 【0793】 An example of a prompt used in this system would be, "Please provide something to help you relax before the math test." This enables personalized education that takes into account the learner's psychological and emotional state. 【0794】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0795】 Step 1: 【0796】 The server collects data on past plans, test results, and learning outcomes from the database. Input is done using SQL queries, and output is a data frame summarizing the collected data. This data frame is used in the next analysis step. 【0797】 Step 2: 【0798】 The server inputs the collected data into a generating AI model for analysis. This AI model is built using the TensorFlow library. The input is the data frame obtained in the previous step, and the output is the trend analysis results used to optimize the educational plan. This analysis predicts what kind of plan will be effective. 【0799】 Step 3: 【0800】 The server generates an educational plan based on the analysis results of the AI ​​model. The generated plan is output in PDF format and delivered to the educator's terminal. It is then delivered as an email using the SMTP protocol, and the educator reviews its contents. At this point, the educator can revise the plan as needed. 【0801】 Step 4: 【0802】 The server selects learning materials that are appropriate to the learner's needs based on the analysis results. The input is the analysis results obtained in the previous stage, and the output is a list of learning materials delivered to the learner's device. Past performance and learning objectives are taken into consideration in this selection. The learning materials are provided as PDFs, videos, or interactive content. 【0803】 Step 5: 【0804】 The device uses a camera and microphone to capture the learner's emotional state in real time. Input consists of the learner's facial expressions and voice data, which the device sends to a server. Output is a dataset for emotion analysis. This data is processed using a facial expression analysis API. 【0805】 Step 6: 【0806】 The server analyzes the received emotional data and estimates the learner's psychological state. The input is the emotional data obtained in step 5, and the output is an assessment of the learner's current emotional state. Here, it determines whether the learner is confused, stressed, or bored. 【0807】 Step 7: 【0808】 The server dynamically adjusts educational content based on the sentiment analysis results. The input is an assessment of the emotional state, and the output is improved educational materials or motivational content. Specifically, if there is a lack of understanding, additional materials are provided, and if motivation is low, encouraging messages are displayed on the device. 【0809】 (Application Example 2) 【0810】 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". 【0811】 Traditional education systems have faced challenges in recognizing and effectively responding to learners' emotions and learning progress in real time, making it difficult to provide individually optimized education. In particular, in home environments, limited learning support makes it difficult to maintain learners' motivation and learning effectiveness. This results in a decline in the quality of education and makes it difficult to achieve effective learning outcomes. 【0812】 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. 【0813】 In this invention, the server includes means for collecting information on past educational plans, test results, and learning outcomes; means for using an artificial intelligence model to generate educational plans using the collected information; means for distributing the generated educational plans; and means for recognizing learners' emotions and dynamically adjusting educational activities based on those emotions. This makes it possible to provide individually optimized education to learners in real time. 【0814】 "Past educational plans" refers to the teaching policies and learning program designs that were previously formulated in educational activities. 【0815】 "Test results" refer to the results of tests used to evaluate the achievements of learners in educational activities. 【0816】 "Learning outcomes" refer to the extent to which learners have acquired knowledge and skills through educational activities. 【0817】 An "artificial intelligence model" is an algorithm that uses large amounts of data to make predictions and decisions for performing specific tasks. 【0818】 A "terminal" is a type of computing device capable of displaying and inputting information, and is used for receiving and displaying educational content. 【0819】 "Educational materials" refer to teaching materials and information resources that learners use to study. 【0820】 "Monitoring" is the process of continuously observing and recording the state and trends of a specific subject, and taking action as needed. 【0821】 "Feedback" refers to evaluations and suggestions provided to learners to encourage improvement and growth. 【0822】 "Recognizing emotions" is the process of determining a user's emotional state at any given moment through their facial expressions, voice, and other actions. 【0823】 "Dynamic adjustment" means changing systems and processes in real time in response to changes in circumstances and needs. 【0824】 To realize this application, the system is implemented using a combination of specific hardware and software. The server collects information on past teaching plans, test results, and learning outcomes from a database, and uses an artificial intelligence model to generate an optimal teaching plan based on this data. The generated teaching plan is then sent to the educator's terminal and used as the basis for teaching activities. 【0825】 Furthermore, the server uses devices such as cameras and microphones to capture the learner's facial expressions and voice in order to recognize the user's emotions. This data is analyzed by an emotion recognition engine (for example, Microsoft Azure's Face API). Based on the emotional state obtained from the analysis, the server dynamically adjusts the educational content. For example, if the server estimates that the learner is feeling stressed, it will send a voice message to help them relax or simple warm-up exercises to the learner's device to support their learning experience. 【0826】 The goal of this system is to provide personalized education to individual learners and support efficient and effective learning. The generative AI model optimizes the teaching plan and materials based on prompts. For example, a prompt might be: "This child has lost interest in today's math assignment. What would you suggest to rekindle his interest?" 【0827】 These technologies enable the system to provide appropriate educational content tailored to the learner's needs, thereby improving learning outcomes. 【0828】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0829】 Step 1: 【0830】 The server collects information on past educational plans, test results, and learning outcomes from a database. The input data consists of educational data provided by educational institutions, and based on this, an artificial intelligence model begins generating educational plans. The output is a dataset in the format necessary for analysis. At this stage, the main operations are data collection and organization. 【0831】 Step 2: 【0832】 The server uses the collected data to feed into an artificial intelligence model to generate an optimal educational plan. The input is educational information retrieved from a database, and data processing includes cleaning and pre-processing. The output is a detailed educational plan sent to the educator's terminal. Here, the AI ​​model analyzes the data and constructs the optimal learning plan. 【0833】 Step 3: 【0834】 The device captures the learner's facial expressions and voice through its camera and microphone and sends them to the server. The input is real-time audio and visual data. The output is formatted data for analysis based on this data. In this step, data on the user's emotions is collected through the device. 【0835】 Step 4: 【0836】 The server analyzes the learner's emotions from the acquired data using an emotion recognition engine. The input is video and audio data transmitted from the terminal, and an emotion recognition algorithm is applied as data processing. The output is information indicating the learner's emotional state. Specifically, the server calls an emotion recognition API to analyze emotions. 【0837】 Step 5: 【0838】 The server adjusts the educational content based on emotion analysis. For example, if the input indicates that the learner is experiencing stress, the server provides additional educational materials to promote relaxation. The output is the adjusted educational plan and supplementary materials. In this step, the AI ​​model generates the responses to enhance support for the learner. 【0839】 Step 6: 【0840】 The terminal displays pre-configured educational content provided by the server to the learner, facilitating interaction. Input is the instructions from the server, and output is feedback information based on the user's interaction. The system then captures how the user perceived the task and provides feedback accordingly. 【0841】 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. 【0842】 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. 【0843】 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. 【0844】 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. 【0845】 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. 【0846】 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. 【0847】 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. 【0848】 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. 【0849】 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." 【0850】 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. 【0851】 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. 【0852】 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. 【0853】 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. 【0854】 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. 【0855】 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. 【0856】 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. 【0857】 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. 【0858】 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. 【0859】 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. 【0860】 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. 【0861】 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. 【0862】 The following is further disclosed regarding the embodiments described above. 【0863】 (Claim 1) 【0864】 Means for collecting information on past educational plans, test results, and learning outcomes, 【0865】 A method using an artificial intelligence model that generates educational plans using collected information, 【0866】 A means of distributing the generated educational plan to the educator's terminal, 【0867】 A means for selecting or generating the most suitable educational materials based on the educational content, 【0868】 A means of monitoring learning progress, analyzing results, and providing feedback, 【0869】 A means of adjusting plans by comparing educational outcomes with information from other educational institutions, 【0870】 A system that includes this. 【0871】 (Claim 2) 【0872】 The system according to claim 1, further comprising the step of detecting and correcting missing values ​​and errors in the collected information. 【0873】 (Claim 3) 【0874】 The system according to claim 1, further comprising means for visualizing educational activities in educational institutions and providing customized education to individual learners. 【0875】 "Example 1" 【0876】 (Claim 1) 【0877】 Means for collecting information on past educational plans, test results, and learning outcomes of educational institutions, 【0878】 A method using a data analysis model to generate an educational plan using collected information, 【0879】 A means for distributing the generated educational plan to the terminals of educators and learners, 【0880】 A means of providing optimal educational information based on the educational plan, 【0881】 A means to monitor learning progress in real time, analyze the results, and provide feedback, 【0882】 A means of correcting plans by comparing the analyzed educational outcomes with information from other educational institutions, 【0883】 A system that includes this. 【0884】 (Claim 2) 【0885】 The system according to claim 1, further comprising the step of detecting missing values ​​and errors in the collected information and performing data cleansing. 【0886】 (Claim 3) 【0887】 The system according to claim 1, further comprising means for visually displaying educational activities and providing educational content suitable for individual learners. 【0888】 "Application Example 1" 【0889】 (Claim 1) 【0890】 Means for collecting information on past educational plans, test results, and learning outcomes, 【0891】 A method using an artificial intelligence model that generates educational plans using collected information, 【0892】 A means of distributing the generated educational plan to the educator's terminal, 【0893】 A means for selecting or generating the most suitable educational materials based on the educational content, 【0894】 A means of monitoring learning progress, analyzing results, and providing feedback, 【0895】 A means of adjusting plans by comparing educational outcomes with information from other educational institutions, 【0896】 A means of providing a terminal application that allows educators and learners to review and edit educational plans, 【0897】 A means of generating an optimal lesson plan using prompt statements, 【0898】 A system that includes this. 【0899】 (Claim 2) 【0900】 The system according to claim 1, further comprising the step of detecting and correcting missing values ​​and errors in the collected information. 【0901】 (Claim 3) 【0902】 The system according to claim 1, further comprising means for visualizing educational activities in educational institutions and providing customized education to individual learners. 【0903】 "Example 2 of combining an emotion engine" 【0904】 (Claim 1) 【0905】 Means for collecting information on past plans, test results, and outcomes, 【0906】 A means of using a model that generates a plan using collected information, 【0907】 A means of distributing the generated plan to the educator's device, 【0908】 A means of selecting or generating the most suitable materials based on the content, 【0909】 A means of collecting and analyzing emotional information, 【0910】 A means of dynamically adjusting the content based on the results of emotion analysis, 【0911】 A means of monitoring progress, analyzing results, and providing feedback, 【0912】 A means of adjusting the plan by comparing the results with information from other institutions, 【0913】 A system that includes this. 【0914】 (Claim 2) 【0915】 The system according to claim 1, further comprising the step of detecting and correcting missing values ​​and errors in the collected information. 【0916】 (Claim 3) 【0917】 The system according to claim 1, further comprising means for visualizing activities within an institution and providing customized education to individual learners. 【0918】 "Application example 2 when combining with an emotional engine" 【0919】 (Claim 1) 【0920】 Means for collecting information on past educational plans, test results, and learning outcomes, 【0921】 A method using an artificial intelligence model that generates educational plans using collected information, 【0922】 A means of distributing the generated educational plan to the educator's terminal, 【0923】 A means for selecting or generating the most suitable educational materials based on the educational content, 【0924】 A means of monitoring learning progress, analyzing results, and providing feedback, 【0925】 A means of adjusting plans by comparing educational outcomes with information from other educational institutions, 【0926】 A means of recognizing learners' emotions and dynamically adjusting educational activities based on those emotions, 【0927】 A system that includes this. 【0928】 (Claim 2) 【0929】 The system according to claim 1, further comprising the step of detecting and correcting missing values ​​and errors in the collected information. 【0930】 (Claim 3) 【0931】 The system according to claim 1, further comprising means for visualizing educational activities in educational institutions and providing customized education to individual learners. [Explanation of symbols] 【0932】 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] Means for collecting information on past educational plans, test results, and learning outcomes, A method using an artificial intelligence model that generates educational plans using collected information, A means of distributing the generated educational plan to the educator's terminal, A means for selecting or generating the most suitable educational materials based on the educational content, A means of monitoring learning progress, analyzing results, and providing feedback, A means of adjusting plans by comparing educational outcomes with information from other educational institutions, A system that includes this. [Claim 2] The system according to claim 1, further comprising the step of detecting and correcting missing values ​​and errors in the collected information. [Claim 3] The system according to claim 1, further comprising means for visualizing educational activities in educational institutions and providing customized education to individual learners.

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