system

Abstract

We provide the system. [Solution] An information processing device that collects and analyzes student learning data, An information processing device that generates an individualized learning plan based on the above analysis, A presentation device that outputs learning content based on the aforementioned learning plan, A communication device that provides real-time feedback on the learning content, A voice dialogue device that uses speech recognition technology to analyze students' questions and provide learning support, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional educational systems, it has been difficult to individually analyze the learning styles and progress of each student and provide an optimal learning plan based on that. For this reason, students have had to study with teaching materials and at a pace that does not suit them, resulting in a problem of reduced learning efficiency. Also, for teachers, there has been a problem that they have to spend a lot of time and effort on grading and progress management of students and cannot concentrate on creative educational activities. Furthermore, the mechanism for students to continuously feel the results of learning and improve their motivation has not been sufficient.

Means for Solving the Problems

[0005] This invention provides a means for generating individualized learning plans for each student by collecting and analyzing student learning data using an electronic computer. Furthermore, it outputs learning content based on the generated learning plan to a display device, enabling students to progress through their studies at an appropriate pace. By using a communication means that provides real-time feedback, students can immediately identify and improve their learning problems. In addition, by performing automatic grading and progress management using an electronic computer, the burden on teachers is reduced, allowing them to shift to more creative educational activities. Furthermore, by providing a visualization means that generates points and badges according to the students' level of achievement, it enhances motivation to learn and creates a mechanism that promotes further learning.

[0006] An "electronic computing device" is a computer used to process students' learning data and perform calculations such as analysis, plan generation, and grading.

[0007] An "individualized learning plan" is a learning plan that includes specific materials and assignments tailored to a student's learning style and progress.

[0008] A "display device" is a screen or display that visualizes learning content output from an electronic computer and provides it to students.

[0009] "Communication means" refers to a network or protocol for sending and receiving data between a computer and a display device and providing real-time feedback.

[0010] "Grading" is an evaluation process in which points are assigned to assignments submitted by students based on factors such as the percentage of correct answers and the content of the work.

[0011] "Progress management" is a management method that tracks the progress of students' learning and makes appropriate adjustments to help them achieve their goals.

[0012] "Points and badges" are reward systems that visually represent students' progress and skill development as they learn, thereby increasing their motivation to learn. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of 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 a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is embodied in the following manner as a form for realizing an AI educational tutor that operates on a computer system. The system of the present invention includes an electronic computing device built on a server, a terminal accessed by a user, and communication means connecting the two.

[0035] The server, as an electronic computing device, is equipped with multiple machine learning algorithms. This allows the server to collect student learning data transmitted from terminals and analyze individual learning styles and performance. Based on the analysis results, the server automatically generates a learning plan optimized for each student. This plan includes specific learning materials, assignments, and a study schedule.

[0036] The terminal is a device used by students, and the learning plan generated through this terminal is displayed. Users can progress through the learning content presented on the terminal at their own pace. The terminal communicates with the server in real time and provides immediate feedback on the student's answers. This allows users to correct mistakes immediately during the learning process.

[0037] Furthermore, this invention also includes a function that allows the server to automatically grade assignments submitted by students. The AI ​​uses grading criteria to provide a fair evaluation and records the results in a progress management database. Based on this, the server can re-evaluate the students' progress and adjust their learning plans as needed.

[0038] Furthermore, to enhance motivation, the server generates points and badges based on the student's progress and notifies the device. This visualization allows users to feel their own progress and maintain motivation for further learning. For example, in the case of a middle school student learning the basics of mathematics, the server proposes a plan to focus on strengthening their weakest areas, and each time the user progresses according to the plan and achieves the set goals, the device displays a badge as a reward.

[0039] In this way, the present invention, as an AI-powered educational support system, can provide effective learning support tailored to each individual student.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects past learning data and current progress data from the user's terminal. This data includes questions answered, scores obtained, and study time.

[0043] Step 2:

[0044] The server analyzes the collected data using machine learning algorithms. The algorithms identify patterns in students' learning styles, strengths, and weaknesses, and generate personalized learning plans for each student.

[0045] Step 3:

[0046] The server sends the generated learning plan to the user's device, which then displays learning materials and a learning schedule based on that plan.

[0047] Step 4:

[0048] The user works on learning tasks displayed on their device. When the answer is finalized or in progress, the device sends the input back to the server in real time.

[0049] Step 5:

[0050] The server instantly evaluates the received answers and generates feedback on what the user should improve. This ensures that students receive appropriate support during their learning process.

[0051] Step 6:

[0052] The server automatically grades user-submitted assignments using an AI scoring system and records the scores and feedback on the answers in a progress management database.

[0053] Step 7:

[0054] The server updates progress management data, generates game-like elements (e.g., points or badges) based on the user's achievement level, and notifies the terminal of this.

[0055] Step 8:

[0056] The device displays generated points and badges, showing the user their current performance and next goals. This helps motivate the user to progress to the next stage of learning.

[0057] (Example 1)

[0058] 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."

[0059] To provide effective and efficient educational support tailored to individual learning styles and progress, it is crucial to properly collect and analyze student learning data and provide real-time feedback. However, the current education system makes it difficult to immediately understand each student's learning pattern and propose the optimal learning plan. As a result, there is a challenge in that students do not receive learning experiences that are tailored to their individual progress.

[0060] 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.

[0061] In this invention, the server includes information processing means for collecting and analyzing student progress information, information processing means for forming an individualized educational plan based on the analysis, and display means for presenting assignments based on the educational plan. This enables individualized educational support that responds immediately to the student's learning progress.

[0062] A "student" is a person who participates in a specific learning program within an educational institution or learning environment.

[0063] "Progress information" refers to data that shows the history and status of students' learning activities, and includes items such as study time, answer results, and progress.

[0064] An "information processing system" is a computer system that performs information processing such as data collection, analysis, and result output.

[0065] An "educational plan" is a teaching plan that consists of optimized materials, assignments, and study schedules based on students' learning goals and progress.

[0066] "Display means" refers to devices or interfaces that visually present students with assignments and information based on the educational plan.

[0067] "Communication means" refers to a system or protocol for sending and receiving data between an information processing device and a display means, or between a student and an information processing device.

[0068] "Evaluation" is the process of measuring students' performance on assignments and tests, and providing feedback based on those performance results.

[0069] "Rewards" refer to incentives that enhance students' motivation to learn, such as points or badges that indicate their level of academic achievement.

[0070] This invention aims to provide an optimized educational experience for each student within a learning support system. Specifically, it focuses on building a system that facilitates the smooth flow of information between servers, terminals, and users.

[0071] The server is an information processing device that has multiple machine learning algorithms internally. The server collects progress information sent from students and analyzes it using a generative AI model. This analysis is performed adaptively through the generative AI model using specific prompt sentences (e.g., "Suggest the best topic to study next"). Starting with these prompt sentences, the server can generate an educational plan tailored to the student's level of understanding. This plan includes teaching materials, assignments, and a learning schedule, and is dynamically modified as needed.

[0072] The terminal is a device used by students (users) for learning. The terminal displays the learning plan sent from the server and provides an interface for users to work on assignments. The terminal has a communication mechanism to send user input to the server in real time and receive immediate feedback from the server. This allows users to quickly correct mistakes during learning and proceed with effective learning.

[0073] Users execute educational plans generated by the server via their devices, learning at their own pace. Evaluations and feedback from the server are visually displayed on the device, allowing users to recognize their own learning progress. Rewards and badges are displayed according to learning progress, further enhancing motivation to learn.

[0074] As a concrete example, in the case of a middle school student learning mathematics, the system analyzes the user's past error patterns and suggests appropriate learning materials and assignments. An example of a prompt message is "Suggest the next mathematical topic to learn and generate appropriate practice problems," and by following these instructions, the user can efficiently improve their understanding.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives student learning progress information sent from the terminal. This progress information includes answer history, study time, and correct answer rate. Based on this input data, the server uses machine learning algorithms to analyze the data. This analysis identifies students' strengths and weaknesses and extracts learning patterns.

[0078] Step 2:

[0079] The server uses a generative AI model to create personalized learning plans from the analysis results. This model takes specific prompt statements (e.g., "Suggest the best topic to study next") as input and generates learning materials, assignments, and study schedules tailored to each student's level of understanding and progress. At this stage, the output is a personalized learning plan.

[0080] Step 3:

[0081] The server sends the generated lesson plan to the terminal. The terminal receives this lesson plan and displays it visually to the user. Specifically, the next tasks and learning content to be completed are displayed on the terminal's screen, and the user proceeds with their learning accordingly.

[0082] Step 4:

[0083] The user works on a task presented through their device. They input their answer, which is sent to the server in real time. The input in this step is the user's answer data, which the server immediately evaluates and generates feedback. As output, the feedback is immediately returned to the device and displayed to the user. This allows the user to correct their mistakes on the spot.

[0084] Step 5:

[0085] The server automatically grades the user's assignment answers and records the evaluation results in a progress management database. These evaluation results are used to improve future educational plans. Furthermore, based on the outputted evaluation results, the server generates prompts again to suggest the next learning steps. Based on these results, the server adjusts the new learning plan and prepares to send it to the terminal again.

[0086] Step 6:

[0087] The server generates rewards and badges based on the user's academic achievement and notifies the user's device. These notifications are displayed on the device screen, allowing the user to check their grades and progress. This helps maintain the user's motivation to learn and increases their enthusiasm for the next assignment.

[0088] (Application Example 1)

[0089] 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."

[0090] In today's educational environment, individualized instruction tailored to each student's learning style and progress is required, but providing individual support to a large number of students is a significant burden for educators. Furthermore, when students study at home, it is difficult for them to receive appropriate feedback and support. This leads to challenges such as decreased student motivation and limited learning effectiveness. In addition, since learning support via audio is not yet common, it is difficult to provide an intuitive and flexible learning experience.

[0091] 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.

[0092] In this invention, the server includes an information processing device that collects and analyzes student learning data, an information processing device that generates an individualized learning plan based on the analysis, a presentation device that outputs learning content based on the learning plan, and a voice dialogue device that analyzes student questions using speech recognition technology and provides learning support. This makes it possible to automatically generate individualized learning plans for each student and provide real-time feedback, thereby realizing an individualized instruction environment that enhances learning motivation.

[0093] An "information processing device" is a device that has the ability to collect student learning data and perform calculations based on that data.

[0094] A "learning plan" is a guideline for educational instruction that includes optimized materials, assignments, and schedules based on a student's learning style and progress.

[0095] A "presentation device" is a device that displays or communicates learning content to students visually or audibly, based on a generated learning plan.

[0096] A "communication device" is a device that has data transmission and reception capabilities to provide real-time feedback on learning content and connect students with a server.

[0097] A "voice dialogue device" is a device that uses speech recognition technology to analyze students' oral questions and provides dialogue-based learning support accordingly.

[0098] To realize this invention, it is necessary to build an information processing system equipped with multiple machine learning algorithms on a server. The server collects learning data from students' terminals and uses that data to analyze students' learning styles and performance. Specifically, it performs data analysis using machine learning libraries such as TENSORFLOW® and PyTorch. Based on the analysis results, the server automatically generates an individualized learning plan. The learning plan includes learning materials, assignments, and schedules tailored to the student's weaknesses and learning goals.

[0099] The terminal is a device used by students and displays the generated learning plan in real time. The terminal utilizes the Google® Speech-to-Text API to convert student voice input into text, and the server provides rapid feedback based on this. In addition, a speech synthesis system is used to communicate feedback to students verbally, allowing them to adjust their actions immediately.

[0100] On the user side, the server evaluates progress based on the learning status and adjusts the learning plan as needed. The evaluation process uses intuitive visualization methods to grasp the student's achievement level, generating points and badges to visualize it and increase student motivation.

[0101] For example, in a scenario where an elementary school student is solving basic math problems, the server identifies the areas where the student is struggling and generates a plan that includes additional practice problems needed to overcome them. If successful, the robot provides feedback such as "Well done!" and a new badge appears on the device.

[0102] Examples of prompts to input into a generative AI model are as follows:

[0103] "Elementary school students are solving basic math problems. I want to provide feedback on their mistakes and offer appropriate advice. Please generate a feedback message."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects student learning data transmitted from terminals. The input here consists of the student's past learning history and test results. Based on this data, an initial data analysis is performed using a machine learning algorithm. The output provides an overview of the student's learning style and current level of proficiency.

[0107] Step 2:

[0108] Based on the learning style obtained in Step 1, the server creates an optimal learning plan using TensorFlow or PyTorch implemented in Python. The input is the analyzed learning style data, and based on this information, it generates a list of materials and assignments best suited to the student. As output, an individualized learning plan is formed.

[0109] Step 3:

[0110] The terminal displays the generated learning plan to the student. The input here is learning plan data received from the server, and the output is a visualized list of learning materials and a schedule. This display is shown on the terminal screen, presented in a way that students can intuitively understand.

[0111] Step 4:

[0112] Users (students) complete assignments according to their learning plan via a device and input their answers via voice or text. The input is answer data provided by the student, which the device collects and converts to text using the Google Speech-to-Text API in the case of voice input.

[0113] Step 5:

[0114] The server evaluates the collected answer data. The input is student answer data, and the server uses a machine learning algorithm to score it and generate feedback. The output is the correctness rating of the answer and a feedback message based on that rating.

[0115] Step 6:

[0116] The terminal provides students with feedback received from the server. The input is a feedback message generated by the server, which is then conveyed to the student as voice feedback using a speech synthesis system. Simultaneously, it is also displayed on the screen as visual feedback.

[0117] Step 7:

[0118] The user receives feedback and selects the next step. The main inputs in this step are the student's own responses and their selection of the next learning step. Based on this, the server re-evaluates the learning plan, generates an updated plan if necessary, and prepares for the next step.

[0119] Step 8:

[0120] The server evaluates students' progress and generates points and badges based on their achievement level. The input is student progress data, and a generation AI model is used to calculate achievement indicators according to evaluation criteria. As output, rewards (points and badges) are displayed on the student's device, improving their motivation to learn.

[0121] 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.

[0122] This invention provides an individualized learning experience that takes into account the emotional state of students by combining an emotion engine with an AI educational tutor system. The system consists of a server, terminals, and an emotion engine.

[0123] The server collects student learning progress data and uses this data to generate individually optimized learning plans. In addition, an emotion engine collects emotional data through facial recognition and voice analysis of students and sends it to the server. Based on this emotional data, the server adjusts the content and pace of the learning plan in real time, enabling more effective learning support.

[0124] The terminal is a device that students access and is responsible for displaying learning plans and materials to them. Users engage in learning activities and input answers through this terminal. The terminal is constantly connected to the server and provides guidance to students, including feedback based on data from the emotion engine.

[0125] Specifically, the server receives real-time information on students' emotional states (e.g., focused, tired, excited) from the emotion engine and can adjust the on-screen learning materials accordingly. For example, if the system analyzes that a student is tired, it can provide learning content with a temporarily reduced difficulty level.

[0126] Furthermore, the server distributes points and badges based on emotional state in addition to the progress of the learning plan. This allows the device to show students their learning achievements and provide emotional support as well. For example, a "focus badge" can be awarded for sessions in which students have concentrated particularly hard work, thereby increasing their motivation.

[0127] Thus, the system of the present invention aims to improve learning effectiveness by providing a learning environment that is more suitable for each individual student through the integration of emotional feedback.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user logs into their device and begins learning. The device sends learning progress data to the server and receives the latest learning plan.

[0131] Step 2:

[0132] The emotion engine acquires user facial recognition and voice data to analyze their emotional state in real time. This data is then sent to the server.

[0133] Step 3:

[0134] The server receives the learning plan and emotional data, and selects learning content optimized for the student's emotional state. It adjusts the difficulty and type of content and sends it to the device.

[0135] Step 4:

[0136] The device displays the user the pre-configured learning content received from the server. The user then proceeds with their learning based on that content.

[0137] Step 5:

[0138] When a user answers a learning question, the device sends the answer data to the server. The server receives this data, immediately scores it, and performs an evaluation.

[0139] Step 6:

[0140] The server generates feedback that takes into account the user's emotional state and provides it to the user via the terminal. This feedback includes the accuracy of the answer and areas for improvement.

[0141] Step 7:

[0142] The server generates points and badges based on user progress and sentiment data, rewarding users to increase their motivation to learn.

[0143] Step 8:

[0144] The device displays points and badges sent from the server to the user, visually communicating their progress. It also suggests the next learning activity.

[0145] Through this series of processes, the system of the present invention can improve the user's learning efficiency and motivation.

[0146] (Example 2)

[0147] 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."

[0148] Traditional electronic learning systems have faced challenges in providing individualized education that adequately considers each student's learning progress and emotional state. Furthermore, they lack real-time feedback and features to promote increased motivation, making it difficult to maintain student engagement.

[0149] 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.

[0150] In this invention, the server includes information processing means for collecting and analyzing student learning data, means for generating personalized learning plans, and means for dynamically adjusting learning content based on student emotional data. This makes it possible to provide each student with an optimized learning experience in real time and to enhance their motivation to learn.

[0151] An "information processing device" is an electronic device used to collect and analyze students' learning data and emotional data.

[0152] A "learning plan" refers to an individualized educational program based on students' learning and emotional data.

[0153] "Emotional data" refers to information about students' emotions, obtained in real time from their facial expressions and voice.

[0154] A "display device" is a device used to present learning content to students based on their learning plan.

[0155] A "communication device" is a device that has the function of transmitting information to provide real-time feedback to students.

[0156] A "generative AI model" is a machine learning model that generates personalized learning plans and prompts based on data.

[0157] A "prompt sentence" is an instructional sentence generated to guide students to the next learning step.

[0158] "Rewards and badges" are incentives given to students based on their learning achievements and emotional state.

[0159] This invention is an electronic education system that utilizes advanced information processing technology to personalize students' learning experiences. The system mainly consists of a server, terminals, and an emotion engine.

[0160] The server functions as the core of learning data collection. It records all learning activities performed by students through their devices and manages them using a database management system. Specifically, a general-purpose database management system can be used for efficient database management. The server also utilizes a generative AI model to generate personalized learning plans based on the collected data. Machine learning libraries are expected to be used for the generative AI model.

[0161] The terminal is a device that students directly interact with as a learning interface, and devices such as computers and tablets are used. The terminal is equipped with a means to display learning content sent from the server. Furthermore, an emotion engine is integrated to capture students' facial expressions and voices, and analyzes the students' emotional state in real time. As a result, the terminal can provide optimal learning content and motivation according to the user's current state.

[0162] Through this system, users learn while receiving feedback based on emotional data. For example, if concentration wavers, the device sends emotional data to the server, and learning materials with adjusted difficulty levels are provided according to prompts generated by the server, preventing interruptions to learning. On the other hand, when the user is concentrating, the device displays a "concentration badge" to visually recognize the user's efforts and maintain motivation.

[0163] As a concrete example, suppose a student is using a device to solve a math problem. The emotion engine analyzes the student's tired expression and generates a prompt message such as, "Let's take a short break. Afterwards, try again with an easier problem." This allows the student to continue learning at their own pace.

[0164] In this way, the system can provide an educational experience tailored to the individual characteristics of each student and improve learning effectiveness through real-time feedback.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server receives student learning data sent from terminals and records it in a database. The inputs are learning time, accuracy rate, and response speed, and the output is the storage of this data in the database. This process is performed using a database management system.

[0168] Step 2:

[0169] The device uses an emotion engine to analyze students' facial expressions and voices, generating emotional data about the students. The input is real-time video and audio data of the students, and the output is the analyzed emotional state (e.g., concentration, fatigue, motivation). This data is sent to a server.

[0170] Step 3:

[0171] The server inputs collected training data and sentiment data into a generating AI model to create an individualized learning plan. The input consists of historical training data and the most recent sentiment data, while the output is an individually optimized learning plan. Machine learning libraries are used throughout this process.

[0172] Step 4:

[0173] The server generates learning content and prompts based on the generated learning plan. The prompts include instructions on what the student should learn next. The input is the individualized learning plan, and the output is the learning content and prompts.

[0174] Step 5:

[0175] The terminal displays learning content and prompt messages received from the server to the student. The input is content data from the server, and the output is the screen display for the student. This allows the user to clearly understand what they should learn next.

[0176] Step 6:

[0177] Users continue learning on their devices, answering questions while receiving feedback. This feedback includes motivational badges and comments tailored to their emotional state. Based on user input, the system prepares to move to the next step in real time.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] Traditional education systems allow for content customization based on learners' progress, but they are insufficient in considering learners' emotional states. This makes it difficult to maintain learner motivation and hinders the maximization of learning effectiveness. Furthermore, the lack of emotionally responsive feedback prevents appropriate support from being provided to learners.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes information processing means for collecting and analyzing learner learning information, means for collecting learner emotional information using an emotional analysis engine, and information processing means for generating an individualized learning plan based on the learning information and emotional information. This makes it possible to provide an optimal learning plan that takes the learner's emotional state into account, maintain motivation, and improve learning effectiveness.

[0183] An "information processing device" is a device that collects and analyzes information and performs processing based on that information as needed.

[0184] A "learning plan" is a plan that includes educational content and a schedule optimized for each individual learner.

[0185] An "output device" is a device that displays or transmits information or plans, either physically or digitally.

[0186] "Communication method" refers to a means of sending and receiving information, enabling data exchange between different devices.

[0187] The "emotion analysis engine" is a system that analyzes the emotional state of a learner based on facial recognition and voice patterns, and outputs the results as data.

[0188] An "indicator" is a standard used to express a learner's level of achievement or progress in numerical or other formats.

[0189] "Rewards" are incentives provided to acknowledge and enhance learners' success and efforts.

[0190] This invention configures the system as follows to provide an individualized learning experience that takes into account the learner's emotional state.

[0191] First, the server uses an information processing device equipped with multiple sensors to collect and analyze student learning data and emotional data. The information processing device utilizes a cloud-based database to record learning progress, and the emotional analysis engine employs facial recognition technology (e.g., camera device) and voice analysis technology (e.g., microphone device). Specifically, cloud services such as Amazon Rekognition and Google Cloud Vision are used.

[0192] Next, the server applies machine learning algorithms to generate personalized learning plans based on the collected data. This utilizes algorithms implemented in programming languages ​​such as Python. These algorithms analyze the learner's progress and sentiment data to adjust appropriate educational content and its presentation method.

[0193] The terminal is an output device that displays educational content generated based on the learning plan to the user. This terminal can be a digital device such as a tablet or a personal computer. This allows the user to access learning content that changes in real time and receive feedback through the terminal.

[0194] Furthermore, based on emotional data, the system incorporates techniques to engage learners and deliver targeted instruction. For example, if the server detects that a user is feeling tired, it will temporarily lower the difficulty level of the learning material or provide content that encourages a break. This feature reduces the burden on learners and enables the provision of an efficient and sustainable learning environment.

[0195] Specifically, the system provides a way to enhance a learner's sense of accomplishment by automatically awarding them a "focus badge" via a visual interface when they complete a session in which they are particularly focused. Generative AI models can also be used for learning content and feedback. For example, one possible prompt command could be to "generate an optimal learning plan based on the learner's current emotional state" to the AI ​​model.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The server receives learner learning data and emotional data from the terminal. Learning data includes answer history to questions and performance data, while emotional data includes information on facial expressions and tone of voice acquired through the camera and microphone. The server receives this data as input and performs initial data conversion.

[0199] Step 2:

[0200] The server processes the received emotional data using an emotion analysis engine. Specifically, it uses facial recognition technology to analyze facial expressions and voice analysis technology to check the tone and rhythm of the voice. This analysis quantifies the learner's emotional state and outputs states such as "concentrated" or "tired." Based on the evaluation results of the outputted emotional state, it generates real-time feedback data.

[0201] Step 3:

[0202] The server utilizes machine learning algorithms to integrate training data and sentiment data to generate personalized learning plans. This plan generation process uses Python programming to determine the optimal learning content and pace for each learner. The generated learning plan is output in the form of learning content and a progress schedule.

[0203] Step 4:

[0204] The device receives the learning plan sent from the server and presents the educational content to the learner visually and audibly. The learning materials are presented in text and video format via the display device, and if audio guides are available, they are played through the built-in speaker. The user works through the materials and enters their answers using an input device.

[0205] Step 5:

[0206] The server scores and evaluates learners' answers in real time. Specifically, it uses an automated scoring algorithm to determine whether an answer is correct or incorrect. The scoring results are stored in a progress management database, and evaluation indicators such as achievement level are displayed to the user on the screen.

[0207] Step 6:

[0208] The server generates indicators and rewards to motivate learners based on their emotional state. For example, if a learner concentrates, a "concentration badge" is generated and displayed on the learner's interface. Based on the generation AI model, the prompt "Generate the optimal learning reward based on the learner's current emotional state" is used.

[0209] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0210] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0211] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0215] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0216] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0217] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0219] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0220] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0221] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0222] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0223] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0224] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0225] This invention is embodied in the following manner as a form for realizing an AI educational tutor that operates on a computer system. The system of the present invention includes an electronic computing device built on a server, a terminal accessed by a user, and communication means connecting the two.

[0226] The server, as an electronic computing device, is equipped with multiple machine learning algorithms. This allows the server to collect student learning data transmitted from terminals and analyze individual learning styles and performance. Based on the analysis results, the server automatically generates a learning plan optimized for each student. This plan includes specific learning materials, assignments, and a study schedule.

[0227] The terminal is a device used by students, and the learning plan generated through this terminal is displayed. Users can progress through the learning content presented on the terminal at their own pace. The terminal communicates with the server in real time and provides immediate feedback on the student's answers. This allows users to correct mistakes immediately during the learning process.

[0228] Furthermore, this invention also includes a function that allows the server to automatically grade assignments submitted by students. The AI ​​uses grading criteria to provide a fair evaluation and records the results in a progress management database. Based on this, the server can re-evaluate the students' progress and adjust their learning plans as needed.

[0229] Furthermore, to enhance motivation, the server generates points and badges based on the student's progress and notifies the device. This visualization allows users to feel their own progress and maintain motivation for further learning. For example, in the case of a middle school student learning the basics of mathematics, the server proposes a plan to focus on strengthening their weakest areas, and each time the user progresses according to the plan and achieves the set goals, the device displays a badge as a reward.

[0230] In this way, the present invention, as an AI-powered educational support system, can provide effective learning support tailored to each individual student.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The server collects past learning data and current progress data from the user's terminal. This data includes questions answered, scores obtained, and study time.

[0234] Step 2:

[0235] The server analyzes the collected data using machine learning algorithms. The algorithms identify patterns in students' learning styles, strengths, and weaknesses, and generate personalized learning plans for each student.

[0236] Step 3:

[0237] The server sends the generated learning plan to the user's device, which then displays learning materials and a learning schedule based on that plan.

[0238] Step 4:

[0239] The user works on learning tasks displayed on their device. When the answer is finalized or in progress, the device sends the input back to the server in real time.

[0240] Step 5:

[0241] The server instantly evaluates the received answers and generates feedback on what the user should improve. This ensures that students receive appropriate support during their learning process.

[0242] Step 6:

[0243] The server automatically grades user-submitted assignments using an AI scoring system and records the scores and feedback on the answers in a progress management database.

[0244] Step 7:

[0245] The server updates progress management data, generates game-like elements (e.g., points or badges) based on the user's achievement level, and notifies the terminal of this.

[0246] Step 8:

[0247] The device displays generated points and badges, showing the user their current performance and next goals. This helps motivate the user to progress to the next stage of learning.

[0248] (Example 1)

[0249] 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."

[0250] To provide effective and efficient educational support tailored to individual learning styles and progress, it is crucial to properly collect and analyze student learning data and provide real-time feedback. However, the current education system makes it difficult to immediately understand each student's learning pattern and propose the optimal learning plan. As a result, there is a challenge in that students do not receive learning experiences that are tailored to their individual progress.

[0251] 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.

[0252] In this invention, the server includes information processing means for collecting and analyzing student progress information, information processing means for forming an individualized educational plan based on the analysis, and display means for presenting assignments based on the educational plan. This enables individualized educational support that responds immediately to the student's learning progress.

[0253] A "student" is a person who participates in a specific learning program within an educational institution or learning environment.

[0254] "Progress information" refers to data that shows the history and status of students' learning activities, and includes items such as study time, answer results, and progress.

[0255] An "information processing system" is a computer system that performs information processing such as data collection, analysis, and result output.

[0256] An "educational plan" is a teaching plan that consists of optimized materials, assignments, and study schedules based on students' learning goals and progress.

[0257] "Display means" refers to devices or interfaces that visually present students with assignments and information based on the educational plan.

[0258] "Communication means" refers to a system or protocol for sending and receiving data between an information processing device and a display means, or between a student and an information processing device.

[0259] "Evaluation" is the process of measuring students' performance on assignments and tests, and providing feedback based on those performance results.

[0260] "Rewards" refer to incentives that enhance students' motivation to learn, such as points or badges that indicate their level of academic achievement.

[0261] This invention aims to provide an optimized educational experience for each student within a learning support system. Specifically, it focuses on building a system that facilitates the smooth flow of information between servers, terminals, and users.

[0262] The server is an information processing device that has multiple machine learning algorithms internally. The server collects progress information sent from students and analyzes it using a generative AI model. This analysis is performed adaptively through the generative AI model using specific prompt sentences (e.g., "Suggest the best topic to study next"). Starting with these prompt sentences, the server can generate an educational plan tailored to the student's level of understanding. This plan includes teaching materials, assignments, and a learning schedule, and is dynamically modified as needed.

[0263] The terminal is a device used by students (users) for learning. The terminal displays the learning plan sent from the server and provides an interface for users to work on assignments. The terminal has a communication mechanism to send user input to the server in real time and receive immediate feedback from the server. This allows users to quickly correct mistakes during learning and proceed with effective learning.

[0264] Users execute educational plans generated by the server via their devices, learning at their own pace. Evaluations and feedback from the server are visually displayed on the device, allowing users to recognize their own learning progress. Rewards and badges are displayed according to learning progress, further enhancing motivation to learn.

[0265] As a concrete example, in the case of a middle school student learning mathematics, the system analyzes the user's past error patterns and suggests appropriate learning materials and assignments. An example of a prompt message is "Suggest the next mathematical topic to learn and generate appropriate practice problems," and by following these instructions, the user can efficiently improve their understanding.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server receives student learning progress information sent from the terminal. This progress information includes answer history, study time, and correct answer rate. Based on this input data, the server uses machine learning algorithms to analyze the data. This analysis identifies students' strengths and weaknesses and extracts learning patterns.

[0269] Step 2:

[0270] The server uses a generative AI model to create personalized learning plans from the analysis results. This model takes specific prompt statements (e.g., "Suggest the best topic to study next") as input and generates learning materials, assignments, and study schedules tailored to each student's level of understanding and progress. At this stage, the output is a personalized learning plan.

[0271] Step 3:

[0272] The server sends the generated lesson plan to the terminal. The terminal receives this lesson plan and displays it visually to the user. Specifically, the next tasks and learning content to be completed are displayed on the terminal's screen, and the user proceeds with their learning accordingly.

[0273] Step 4:

[0274] The user works on a task presented through their device. They input their answer, which is sent to the server in real time. The input in this step is the user's answer data, which the server immediately evaluates and generates feedback. As output, the feedback is immediately returned to the device and displayed to the user. This allows the user to correct their mistakes on the spot.

[0275] Step 5:

[0276] The server automatically grades the user's assignment answers and records the evaluation results in a progress management database. These evaluation results are used to improve future educational plans. Furthermore, based on the outputted evaluation results, the server generates prompts again to suggest the next learning steps. Based on these results, the server adjusts the new learning plan and prepares to send it to the terminal again.

[0277] Step 6:

[0278] The server generates rewards and badges based on the user's academic achievement and notifies the user's device. These notifications are displayed on the device screen, allowing the user to check their grades and progress. This helps maintain the user's motivation to learn and increases their enthusiasm for the next assignment.

[0279] (Application Example 1)

[0280] 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."

[0281] In today's educational environment, individualized instruction tailored to each student's learning style and progress is required, but providing individual support to a large number of students is a significant burden for educators. Furthermore, when students study at home, it is difficult for them to receive appropriate feedback and support. This leads to challenges such as decreased student motivation and limited learning effectiveness. In addition, since learning support via audio is not yet common, it is difficult to provide an intuitive and flexible learning experience.

[0282] 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.

[0283] In this invention, the server includes an information processing device that collects and analyzes students' learning data, an information processing device that generates an individualized learning plan based on the analysis, a presentation device that outputs learning content based on the learning plan, and a voice dialogue device that analyzes students' questions using voice recognition technology and provides learning support. As a result, it becomes possible to automatically generate an individualized learning plan for each student and provide real-time feedback, and it becomes possible to realize an individualized guidance environment that improves learning motivation.

[0284] The "information processing device" is a device having a computing function that collects students' learning data and performs analysis based on it.

[0285] The "learning plan" is a guideline for educational guidance including optimized teaching materials, tasks, and schedules based on students' learning styles and progress.

[0286] The "presentation device" is a device that displays or conveys learning content to students visually or audibly based on the generated learning plan.

[0287] The "communication device" is a device having a data transmission / reception function for providing real-time feedback on learning content and connecting students and the server.

[0288] The "voice dialogue device" is a device having a dialogue function that analyzes students' oral questions using voice recognition technology and provides corresponding learning support.

[0289] To realize this invention, it is necessary to construct an information processing system equipped with a plurality of machine learning algorithms on the server. The server collects learning data from students' terminals and analyzes students' learning styles and performance using this data. Specifically, data analysis is performed using machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, the server automatically generates an individualized learning plan. The learning plan includes teaching materials, tasks, and schedules according to students' weaknesses and learning goals.

[0290] The terminal is a device used by students and displays the generated learning plan in real time. The terminal utilizes the Google Speech-to-Text API to convert student voice input into text, and the server provides rapid feedback based on this. In addition, a speech synthesis system is used to deliver feedback to students verbally, allowing them to adjust their actions immediately.

[0291] On the user side, the server evaluates progress based on the learning status and adjusts the learning plan as needed. The evaluation process uses intuitive visualization methods to grasp the student's achievement level, generating points and badges to visualize it and increase student motivation.

[0292] For example, in a scenario where an elementary school student is solving basic math problems, the server identifies the areas where the student is struggling and generates a plan that includes additional practice problems needed to overcome them. If successful, the robot provides feedback such as "Well done!" and a new badge appears on the device.

[0293] Examples of prompts to input into a generative AI model are as follows:

[0294] "Elementary school students are solving basic math problems. I want to provide feedback on their mistakes and offer appropriate advice. Please generate a feedback message."

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] The server collects student learning data transmitted from terminals. The input here consists of the student's past learning history and test results. Based on this data, an initial data analysis is performed using a machine learning algorithm. The output provides an overview of the student's learning style and current level of proficiency.

[0298] Step 2:

[0299] Based on the learning style obtained in Step 1, the server creates an optimal learning plan using TensorFlow or PyTorch implemented in Python. The input is the analyzed learning style data, and based on this information, it generates a list of materials and assignments best suited to the student. As output, an individualized learning plan is formed.

[0300] Step 3:

[0301] The terminal displays the generated learning plan to the student. The input here is learning plan data received from the server, and the output is a visualized list of learning materials and a schedule. This display is shown on the terminal screen, presented in a way that students can intuitively understand.

[0302] Step 4:

[0303] Users (students) complete assignments according to their learning plan via a device and input their answers via voice or text. The input is answer data provided by the student, which the device collects and converts to text using the Google Speech-to-Text API in the case of voice input.

[0304] Step 5:

[0305] The server evaluates the collected answer data. The input is student answer data, and the server uses a machine learning algorithm to score it and generate feedback. The output is the correctness rating of the answer and a feedback message based on that rating.

[0306] Step 6:

[0307] The terminal provides students with feedback received from the server. The input is a feedback message generated by the server, which is then conveyed to the student as voice feedback using a speech synthesis system. Simultaneously, it is also displayed on the screen as visual feedback.

[0308] Step 7:

[0309] The user receives feedback and selects the next step. The main inputs at this step are the student's own reactions and the selection of the next learning step. Based on this, the server re-evaluates the learning plan, generates an updated plan if necessary, and prepares for the next step.

[0310] Step 8:

[0311] The server evaluates the student's progress and generates points or badges according to the degree of achievement. The input is the student's progress data, and the generation AI model is used to calculate the achievement indicators in line with the evaluation criteria. As output, rewards (points or badges) are displayed on the student's terminal to improve learning motivation.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0313] The present invention provides an individualized learning experience considering the emotional state of students by combining an emotion engine with an AI education tutoring system. This system is configured to include a server, a terminal, and an emotion engine.

[0314] The server collects the student's learning progress data and uses this data to generate a learning plan optimized individually. In addition, the emotion engine collects emotion data through the student's face recognition and voice analysis and transmits it to the server. Based on this emotion data, the server adjusts the content and progress pace of the learning plan in real time to achieve more effective learning support.

[0315] The terminal is a device that students access and is responsible for displaying learning plans and materials to them. Users engage in learning activities and input answers through this terminal. The terminal is constantly connected to the server and provides guidance to students, including feedback based on data from the emotion engine.

[0316] Specifically, the server receives real-time information on students' emotional states (e.g., focused, tired, excited) from the emotion engine and can adjust the on-screen learning materials accordingly. For example, if the system analyzes that a student is tired, it can provide learning content with a temporarily reduced difficulty level.

[0317] Furthermore, the server distributes points and badges based on emotional state in addition to the progress of the learning plan. This allows the device to show students their learning achievements and provide emotional support as well. For example, a "focus badge" can be awarded for sessions in which students have concentrated particularly hard work, thereby increasing their motivation.

[0318] Thus, the system of the present invention aims to improve learning effectiveness by providing a learning environment that is more suitable for each individual student through the integration of emotional feedback.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user logs into their device and begins learning. The device sends learning progress data to the server and receives the latest learning plan.

[0322] Step 2:

[0323] The emotion engine acquires user facial recognition and voice data to analyze their emotional state in real time. This data is then sent to the server.

[0324] Step 3:

[0325] The server receives the learning plan and emotional data, and selects learning content optimized for the student's emotional state. It adjusts the difficulty and type of content and sends it to the device.

[0326] Step 4:

[0327] The device displays the user the pre-configured learning content received from the server. The user then proceeds with their learning based on that content.

[0328] Step 5:

[0329] When a user answers a learning question, the device sends the answer data to the server. The server receives this data, immediately scores it, and performs an evaluation.

[0330] Step 6:

[0331] The server generates feedback that takes into account the user's emotional state and provides it to the user via the terminal. This feedback includes the accuracy of the answer and areas for improvement.

[0332] Step 7:

[0333] The server generates points and badges based on user progress and sentiment data, rewarding users to increase their motivation to learn.

[0334] Step 8:

[0335] The device displays points and badges sent from the server to the user, visually communicating their progress. It also suggests the next learning activity.

[0336] Through this series of processes, the system of the present invention can improve the user's learning efficiency and motivation.

[0337] (Example 2)

[0338] 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".

[0339] Traditional electronic learning systems have faced challenges in providing individualized education that adequately considers each student's learning progress and emotional state. Furthermore, they lack real-time feedback and features to promote increased motivation, making it difficult to maintain student engagement.

[0340] 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.

[0341] In this invention, the server includes information processing means for collecting and analyzing student learning data, means for generating personalized learning plans, and means for dynamically adjusting learning content based on student emotional data. This makes it possible to provide each student with an optimized learning experience in real time and to enhance their motivation to learn.

[0342] An "information processing device" is an electronic device used to collect and analyze students' learning data and emotional data.

[0343] A "learning plan" refers to an individualized educational program based on students' learning and emotional data.

[0344] "Emotional data" refers to information about students' emotions, obtained in real time from their facial expressions and voice.

[0345] A "display device" is a device used to present learning content to students based on their learning plan.

[0346] A "communication device" is a device that has the function of transmitting information to provide real-time feedback to students.

[0347] A "generative AI model" is a machine learning model that generates personalized learning plans and prompts based on data.

[0348] A "prompt sentence" is an instructional sentence generated to guide students to the next learning step.

[0349] "Rewards and badges" are incentives given to students based on their learning achievements and emotional state.

[0350] This invention is an electronic education system that utilizes advanced information processing technology to personalize students' learning experiences. The system mainly consists of a server, terminals, and an emotion engine.

[0351] The server functions as the core of learning data collection. It records all learning activities performed by students through their devices and manages them using a database management system. Specifically, a general-purpose database management system can be used for efficient database management. The server also utilizes a generative AI model to generate personalized learning plans based on the collected data. Machine learning libraries are expected to be used for the generative AI model.

[0352] The terminal is a device that students directly interact with as a learning interface, and devices such as computers and tablets are used. The terminal is equipped with a means to display learning content sent from the server. Furthermore, an emotion engine is integrated to capture students' facial expressions and voices, and analyzes the students' emotional state in real time. As a result, the terminal can provide optimal learning content and motivation according to the user's current state.

[0353] Through this system, users learn while receiving feedback based on emotional data. For example, if concentration wavers, the device sends emotional data to the server, and learning materials with adjusted difficulty levels are provided according to prompts generated by the server, preventing interruptions to learning. On the other hand, when the user is concentrating, the device displays a "concentration badge" to visually recognize the user's efforts and maintain motivation.

[0354] As a concrete example, suppose a student is using a device to solve a math problem. The emotion engine analyzes the student's tired expression and generates a prompt message such as, "Let's take a short break. Afterwards, try again with an easier problem." This allows the student to continue learning at their own pace.

[0355] In this way, the system can provide an educational experience tailored to the individual characteristics of each student and improve learning effectiveness through real-time feedback.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The server receives student learning data sent from terminals and records it in a database. The inputs are learning time, accuracy rate, and response speed, and the output is the storage of this data in the database. This process is performed using a database management system.

[0359] Step 2:

[0360] The device uses an emotion engine to analyze students' facial expressions and voices, generating emotional data about the students. The input is real-time video and audio data of the students, and the output is the analyzed emotional state (e.g., concentration, fatigue, motivation). This data is sent to a server.

[0361] Step 3:

[0362] The server inputs collected training data and sentiment data into a generating AI model to create an individualized learning plan. The input consists of historical training data and the most recent sentiment data, while the output is an individually optimized learning plan. Machine learning libraries are used throughout this process.

[0363] Step 4:

[0364] The server generates learning content and prompts based on the generated learning plan. The prompts include instructions on what the student should learn next. The input is the individualized learning plan, and the output is the learning content and prompts.

[0365] Step 5:

[0366] The terminal displays learning content and prompt messages received from the server to the student. The input is content data from the server, and the output is the screen display for the student. This allows the user to clearly understand what they should learn next.

[0367] Step 6:

[0368] Users continue learning on their devices, answering questions while receiving feedback. This feedback includes motivational badges and comments tailored to their emotional state. Based on user input, the system prepares to move to the next step in real time.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] Traditional education systems allow for content customization based on learners' progress, but they are insufficient in considering learners' emotional states. This makes it difficult to maintain learner motivation and hinders the maximization of learning effectiveness. Furthermore, the lack of emotionally responsive feedback prevents appropriate support from being provided to learners.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes information processing means for collecting and analyzing learner learning information, means for collecting learner emotional information using an emotional analysis engine, and information processing means for generating an individualized learning plan based on the learning information and emotional information. This makes it possible to provide an optimal learning plan that takes the learner's emotional state into account, maintain motivation, and improve learning effectiveness.

[0374] An "information processing device" is a device that collects and analyzes information and performs processing based on that information as needed.

[0375] A "learning plan" is a plan that includes educational content and a schedule optimized for each individual learner.

[0376] An "output device" is a device that displays or transmits information or plans, either physically or digitally.

[0377] "Communication method" refers to a means of sending and receiving information, enabling data exchange between different devices.

[0378] The "emotion analysis engine" is a system that analyzes the emotional state of a learner based on facial recognition and voice patterns, and outputs the results as data.

[0379] An "indicator" is a standard used to express a learner's level of achievement or progress in numerical or other formats.

[0380] "Rewards" are incentives provided to acknowledge and enhance learners' success and efforts.

[0381] This invention configures the system as follows to provide an individualized learning experience that takes into account the learner's emotional state.

[0382] First, the server uses an information processing device equipped with multiple sensors to collect and analyze student learning data and emotional data. The information processing device utilizes a cloud-based database to record learning progress, and the emotional analysis engine employs facial recognition technology (e.g., camera device) and voice analysis technology (e.g., microphone device). Specifically, cloud services such as Amazon Rekognition and Google Cloud Vision are used.

[0383] Next, the server applies machine learning algorithms to generate personalized learning plans based on the collected data. This utilizes algorithms implemented in programming languages ​​such as Python. These algorithms analyze the learner's progress and sentiment data to adjust appropriate educational content and its presentation method.

[0384] The terminal is an output device that displays educational content generated based on the learning plan to the user. This terminal can be a digital device such as a tablet or a personal computer. This allows the user to access learning content that changes in real time and receive feedback through the terminal.

[0385] Furthermore, based on emotional data, the system incorporates techniques to engage learners and deliver targeted instruction. For example, if the server detects that a user is feeling tired, it will temporarily lower the difficulty level of the learning material or provide content that encourages a break. This feature reduces the burden on learners and enables the provision of an efficient and sustainable learning environment.

[0386] Specifically, the system provides a way to enhance a learner's sense of accomplishment by automatically awarding them a "focus badge" via a visual interface when they complete a session in which they are particularly focused. Generative AI models can also be used for learning content and feedback. For example, one possible prompt command could be to "generate an optimal learning plan based on the learner's current emotional state" to the AI ​​model.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The server receives learner learning data and emotional data from the terminal. Learning data includes answer history to questions and performance data, while emotional data includes information on facial expressions and tone of voice acquired through the camera and microphone. The server receives this data as input and performs initial data conversion.

[0390] Step 2:

[0391] The server processes the received emotional data using an emotion analysis engine. Specifically, it uses facial recognition technology to analyze facial expressions and voice analysis technology to check the tone and rhythm of the voice. This analysis quantifies the learner's emotional state and outputs states such as "concentrated" or "tired." Based on the evaluation results of the outputted emotional state, it generates real-time feedback data.

[0392] Step 3:

[0393] The server utilizes machine learning algorithms to integrate training data and sentiment data to generate personalized learning plans. This plan generation process uses Python programming to determine the optimal learning content and pace for each learner. The generated learning plan is output in the form of learning content and a progress schedule.

[0394] Step 4:

[0395] The device receives the learning plan sent from the server and presents the educational content to the learner visually and audibly. The learning materials are presented in text and video format via the display device, and if audio guides are available, they are played through the built-in speaker. The user works through the materials and enters their answers using an input device.

[0396] Step 5:

[0397] The server scores and evaluates learners' answers in real time. Specifically, it uses an automated scoring algorithm to determine whether an answer is correct or incorrect. The scoring results are stored in a progress management database, and evaluation indicators such as achievement level are displayed to the user on the screen.

[0398] Step 6:

[0399] The server generates indicators and rewards to motivate learners based on their emotional state. For example, if a learner concentrates, a "concentration badge" is generated and displayed on the learner's interface. Based on the generation AI model, the prompt "Generate the optimal learning reward based on the learner's current emotional state" is used.

[0400] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0401] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0402] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0406] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0407] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0408] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0409] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0410] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0411] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0412] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0413] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0414] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0415] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0416] This invention is embodied in the following manner as a form for realizing an AI educational tutor that operates on a computer system. The system of the present invention includes an electronic computing device built on a server, a terminal accessed by a user, and communication means connecting the two.

[0417] The server, as an electronic computing device, is equipped with multiple machine learning algorithms. This allows the server to collect student learning data transmitted from terminals and analyze individual learning styles and performance. Based on the analysis results, the server automatically generates a learning plan optimized for each student. This plan includes specific learning materials, assignments, and a study schedule.

[0418] The terminal is a device used by students, and the learning plan generated through this terminal is displayed. Users can progress through the learning content presented on the terminal at their own pace. The terminal communicates with the server in real time and provides immediate feedback on the student's answers. This allows users to correct mistakes immediately during the learning process.

[0419] Furthermore, this invention also includes a function that allows the server to automatically grade assignments submitted by students. The AI ​​uses grading criteria to provide a fair evaluation and records the results in a progress management database. Based on this, the server can re-evaluate the students' progress and adjust their learning plans as needed.

[0420] Furthermore, to enhance motivation, the server generates points and badges based on the student's progress and notifies the device. This visualization allows users to feel their own progress and maintain motivation for further learning. For example, in the case of a middle school student learning the basics of mathematics, the server proposes a plan to focus on strengthening their weakest areas, and each time the user progresses according to the plan and achieves the set goals, the device displays a badge as a reward.

[0421] In this way, the present invention, as an AI-powered educational support system, can provide effective learning support tailored to each individual student.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The server collects past learning data and current progress data from the user's terminal. This data includes questions answered, scores obtained, and study time.

[0425] Step 2:

[0426] The server analyzes the collected data using machine learning algorithms. The algorithms identify patterns in students' learning styles, strengths, and weaknesses, and generate personalized learning plans for each student.

[0427] Step 3:

[0428] The server sends the generated learning plan to the user's device, which then displays learning materials and a learning schedule based on that plan.

[0429] Step 4:

[0430] The user works on learning tasks displayed on their device. When the answer is finalized or in progress, the device sends the input back to the server in real time.

[0431] Step 5:

[0432] The server instantly evaluates the received answers and generates feedback on what the user should improve. This ensures that students receive appropriate support during their learning process.

[0433] Step 6:

[0434] The server automatically grades user-submitted assignments using an AI scoring system and records the scores and feedback on the answers in a progress management database.

[0435] Step 7:

[0436] The server updates progress management data, generates game-like elements (e.g., points or badges) based on the user's achievement level, and notifies the terminal of this.

[0437] Step 8:

[0438] The device displays generated points and badges, showing the user their current performance and next goals. This helps motivate the user to progress to the next stage of learning.

[0439] (Example 1)

[0440] 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."

[0441] To provide effective and efficient educational support tailored to individual learning styles and progress, it is crucial to properly collect and analyze student learning data and provide real-time feedback. However, the current education system makes it difficult to immediately understand each student's learning pattern and propose the optimal learning plan. As a result, there is a challenge in that students do not receive learning experiences that are tailored to their individual progress.

[0442] 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.

[0443] In this invention, the server includes information processing means for collecting and analyzing student progress information, information processing means for forming an individualized educational plan based on the analysis, and display means for presenting assignments based on the educational plan. This enables individualized educational support that responds immediately to the student's learning progress.

[0444] A "student" is a person who participates in a specific learning program within an educational institution or learning environment.

[0445] "Progress information" refers to data that shows the history and status of students' learning activities, and includes items such as study time, answer results, and progress.

[0446] An "information processing system" is a computer system that performs information processing such as data collection, analysis, and result output.

[0447] An "educational plan" is a teaching plan that consists of optimized materials, assignments, and study schedules based on students' learning goals and progress.

[0448] "Display means" refers to devices or interfaces that visually present students with assignments and information based on the educational plan.

[0449] "Communication means" refers to a system or protocol for sending and receiving data between an information processing device and a display means, or between a student and an information processing device.

[0450] "Evaluation" is the process of measuring students' performance on assignments and tests, and providing feedback based on those performance results.

[0451] "Rewards" refer to incentives that enhance students' motivation to learn, such as points or badges that indicate their level of academic achievement.

[0452] This invention aims to provide an optimized educational experience for each student within a learning support system. Specifically, it focuses on building a system that facilitates the smooth flow of information between servers, terminals, and users.

[0453] The server is an information processing device that has multiple machine learning algorithms internally. The server collects progress information sent from students and analyzes it using a generative AI model. This analysis is performed adaptively through the generative AI model using specific prompt sentences (e.g., "Suggest the best topic to study next"). Starting with these prompt sentences, the server can generate an educational plan tailored to the student's level of understanding. This plan includes teaching materials, assignments, and a learning schedule, and is dynamically modified as needed.

[0454] The terminal is a device used by students (users) for learning. The terminal displays the learning plan sent from the server and provides an interface for users to work on assignments. The terminal has a communication mechanism to send user input to the server in real time and receive immediate feedback from the server. This allows users to quickly correct mistakes during learning and proceed with effective learning.

[0455] Users execute educational plans generated by the server via their devices, learning at their own pace. Evaluations and feedback from the server are visually displayed on the device, allowing users to recognize their own learning progress. Rewards and badges are displayed according to learning progress, further enhancing motivation to learn.

[0456] As a concrete example, in the case of a middle school student learning mathematics, the system analyzes the user's past error patterns and suggests appropriate learning materials and assignments. An example of a prompt message is "Suggest the next mathematical topic to learn and generate appropriate practice problems," and by following these instructions, the user can efficiently improve their understanding.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server receives student learning progress information sent from the terminal. This progress information includes answer history, study time, and correct answer rate. Based on this input data, the server uses machine learning algorithms to analyze the data. This analysis identifies students' strengths and weaknesses and extracts learning patterns.

[0460] Step 2:

[0461] The server uses a generative AI model to create personalized learning plans from the analysis results. This model takes specific prompt statements (e.g., "Suggest the best topic to study next") as input and generates learning materials, assignments, and study schedules tailored to each student's level of understanding and progress. At this stage, the output is a personalized learning plan.

[0462] Step 3:

[0463] The server sends the generated lesson plan to the terminal. The terminal receives this lesson plan and displays it visually to the user. Specifically, the next tasks and learning content to be completed are displayed on the terminal's screen, and the user proceeds with their learning accordingly.

[0464] Step 4:

[0465] The user works on a task presented through their device. They input their answer, which is sent to the server in real time. The input in this step is the user's answer data, which the server immediately evaluates and generates feedback. As output, the feedback is immediately returned to the device and displayed to the user. This allows the user to correct their mistakes on the spot.

[0466] Step 5:

[0467] The server automatically grades the user's assignment answers and records the evaluation results in a progress management database. These evaluation results are used to improve future educational plans. Furthermore, based on the outputted evaluation results, the server generates prompts again to suggest the next learning steps. Based on these results, the server adjusts the new learning plan and prepares to send it to the terminal again.

[0468] Step 6:

[0469] The server generates rewards and badges based on the user's academic achievement and notifies the user's device. These notifications are displayed on the device screen, allowing the user to check their grades and progress. This helps maintain the user's motivation to learn and increases their enthusiasm for the next assignment.

[0470] (Application Example 1)

[0471] 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."

[0472] In today's educational environment, individualized instruction tailored to each student's learning style and progress is required, but providing individual support to a large number of students is a significant burden for educators. Furthermore, when students study at home, it is difficult for them to receive appropriate feedback and support. This leads to challenges such as decreased student motivation and limited learning effectiveness. In addition, since learning support via audio is not yet common, it is difficult to provide an intuitive and flexible learning experience.

[0473] 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.

[0474] In this invention, the server includes an information processing device that collects and analyzes student learning data, an information processing device that generates an individualized learning plan based on the analysis, a presentation device that outputs learning content based on the learning plan, and a voice dialogue device that analyzes student questions using speech recognition technology and provides learning support. This makes it possible to automatically generate individualized learning plans for each student and provide real-time feedback, thereby realizing an individualized instruction environment that enhances learning motivation.

[0475] An "information processing device" is a device that has the ability to collect student learning data and perform calculations based on that data.

[0476] A "learning plan" is a guideline for educational instruction that includes optimized materials, assignments, and schedules based on a student's learning style and progress.

[0477] A "presentation device" is a device that displays or communicates learning content to students visually or audibly, based on a generated learning plan.

[0478] A "communication device" is a device that has data transmission and reception capabilities to provide real-time feedback on learning content and connect students with a server.

[0479] A "voice dialogue device" is a device that uses speech recognition technology to analyze students' oral questions and provides dialogue-based learning support accordingly.

[0480] To realize this invention, it is necessary to build an information processing system equipped with multiple machine learning algorithms on a server. The server collects learning data from students' terminals and uses that data to analyze students' learning styles and performance. Specifically, it performs data analysis using machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, the server automatically generates an individualized learning plan. The learning plan includes learning materials, assignments, and schedules tailored to the student's weaknesses and learning goals.

[0481] The terminal is a device used by students and displays the generated learning plan in real time. The terminal utilizes the Google Speech-to-Text API to convert student voice input into text, and the server provides rapid feedback based on this. In addition, a speech synthesis system is used to deliver feedback to students verbally, allowing them to adjust their actions immediately.

[0482] On the user side, the server evaluates progress based on the learning status and adjusts the learning plan as needed. The evaluation process uses intuitive visualization methods to grasp the student's achievement level, generating points and badges to visualize it and increase student motivation.

[0483] For example, in a scenario where an elementary school student is solving basic math problems, the server identifies the areas where the student is struggling and generates a plan that includes additional practice problems needed to overcome them. If successful, the robot provides feedback such as "Well done!" and a new badge appears on the device.

[0484] Examples of prompts to input into a generative AI model are as follows:

[0485] "Elementary school students are solving basic math problems. I want to provide feedback on their mistakes and offer appropriate advice. Please generate a feedback message."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The server collects student learning data transmitted from terminals. The input here consists of the student's past learning history and test results. Based on this data, an initial data analysis is performed using a machine learning algorithm. The output provides an overview of the student's learning style and current level of proficiency.

[0489] Step 2:

[0490] Based on the learning style obtained in Step 1, the server creates an optimal learning plan using TensorFlow or PyTorch implemented in Python. The input is the analyzed learning style data, and based on this information, it generates a list of materials and assignments best suited to the student. As output, an individualized learning plan is formed.

[0491] Step 3:

[0492] The terminal displays the generated learning plan to the student. The input here is learning plan data received from the server, and the output is a visualized list of learning materials and a schedule. This display is shown on the terminal screen, presented in a way that students can intuitively understand.

[0493] Step 4:

[0494] Users (students) complete assignments according to their learning plan via a device and input their answers via voice or text. The input is answer data provided by the student, which the device collects and converts to text using the Google Speech-to-Text API in the case of voice input.

[0495] Step 5:

[0496] The server evaluates the collected answer data. The input is student answer data, and the server uses a machine learning algorithm to score it and generate feedback. The output is the correctness rating of the answer and a feedback message based on that rating.

[0497] Step 6:

[0498] The terminal provides students with feedback received from the server. The input is a feedback message generated by the server, which is then conveyed to the student as voice feedback using a speech synthesis system. Simultaneously, it is also displayed on the screen as visual feedback.

[0499] Step 7:

[0500] The user receives feedback and selects the next step. The main inputs in this step are the student's own responses and their selection of the next learning step. Based on this, the server re-evaluates the learning plan, generates an updated plan if necessary, and prepares for the next step.

[0501] Step 8:

[0502] The server evaluates students' progress and generates points and badges based on their achievement level. The input is student progress data, and a generation AI model is used to calculate achievement indicators according to evaluation criteria. As output, rewards (points and badges) are displayed on the student's device, improving their motivation to learn.

[0503] 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.

[0504] This invention provides an individualized learning experience that takes into account the emotional state of students by combining an emotion engine with an AI educational tutor system. The system consists of a server, terminals, and an emotion engine.

[0505] The server collects student learning progress data and uses this data to generate individually optimized learning plans. In addition, an emotion engine collects emotional data through facial recognition and voice analysis of students and sends it to the server. Based on this emotional data, the server adjusts the content and pace of the learning plan in real time, enabling more effective learning support.

[0506] The terminal is a device that students access and is responsible for displaying learning plans and materials to them. Users engage in learning activities and input answers through this terminal. The terminal is constantly connected to the server and provides guidance to students, including feedback based on data from the emotion engine.

[0507] Specifically, the server receives real-time information on students' emotional states (e.g., focused, tired, excited) from the emotion engine and can adjust the on-screen learning materials accordingly. For example, if the system analyzes that a student is tired, it can provide learning content with a temporarily reduced difficulty level.

[0508] Furthermore, the server distributes points and badges based on emotional state in addition to the progress of the learning plan. This allows the device to show students their learning achievements and provide emotional support as well. For example, a "focus badge" can be awarded for sessions in which students have concentrated particularly hard work, thereby increasing their motivation.

[0509] Thus, the system of the present invention aims to improve learning effectiveness by providing a learning environment that is more suitable for each individual student through the integration of emotional feedback.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The user logs into their device and begins learning. The device sends learning progress data to the server and receives the latest learning plan.

[0513] Step 2:

[0514] The emotion engine acquires user facial recognition and voice data to analyze their emotional state in real time. This data is then sent to the server.

[0515] Step 3:

[0516] The server receives the learning plan and emotional data, and selects learning content optimized for the student's emotional state. It adjusts the difficulty and type of content and sends it to the device.

[0517] Step 4:

[0518] The device displays the user the pre-configured learning content received from the server. The user then proceeds with their learning based on that content.

[0519] Step 5:

[0520] When a user answers a learning question, the device sends the answer data to the server. The server receives this data, immediately scores it, and performs an evaluation.

[0521] Step 6:

[0522] The server generates feedback that takes into account the user's emotional state and provides it to the user via the terminal. This feedback includes the accuracy of the answer and areas for improvement.

[0523] Step 7:

[0524] The server generates points and badges based on user progress and sentiment data, rewarding users to increase their motivation to learn.

[0525] Step 8:

[0526] The device displays points and badges sent from the server to the user, visually communicating their progress. It also suggests the next learning activity.

[0527] Through this series of processes, the system of the present invention can improve the user's learning efficiency and motivation.

[0528] (Example 2)

[0529] 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."

[0530] Traditional electronic learning systems have faced challenges in providing individualized education that adequately considers each student's learning progress and emotional state. Furthermore, they lack real-time feedback and features to promote increased motivation, making it difficult to maintain student engagement.

[0531] 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.

[0532] In this invention, the server includes information processing means for collecting and analyzing student learning data, means for generating personalized learning plans, and means for dynamically adjusting learning content based on student emotional data. This makes it possible to provide each student with an optimized learning experience in real time and to enhance their motivation to learn.

[0533] An "information processing device" is an electronic device used to collect and analyze students' learning data and emotional data.

[0534] A "learning plan" refers to an individualized educational program based on students' learning and emotional data.

[0535] "Emotional data" refers to information about students' emotions, obtained in real time from their facial expressions and voice.

[0536] A "display device" is a device used to present learning content to students based on their learning plan.

[0537] A "communication device" is a device that has the function of transmitting information to provide real-time feedback to students.

[0538] A "generative AI model" is a machine learning model that generates personalized learning plans and prompts based on data.

[0539] A "prompt sentence" is an instructional sentence generated to guide students to the next learning step.

[0540] "Rewards and badges" are incentives given to students based on their learning achievements and emotional state.

[0541] This invention is an electronic education system that utilizes advanced information processing technology to personalize students' learning experiences. The system mainly consists of a server, terminals, and an emotion engine.

[0542] The server functions as the core of learning data collection. It records all learning activities performed by students through their devices and manages them using a database management system. Specifically, a general-purpose database management system can be used for efficient database management. The server also utilizes a generative AI model to generate personalized learning plans based on the collected data. Machine learning libraries are expected to be used for the generative AI model.

[0543] The terminal is a device that students directly interact with as a learning interface, and devices such as computers and tablets are used. The terminal is equipped with a means to display learning content sent from the server. Furthermore, an emotion engine is integrated to capture students' facial expressions and voices, and analyzes the students' emotional state in real time. As a result, the terminal can provide optimal learning content and motivation according to the user's current state.

[0544] Through this system, users learn while receiving feedback based on emotional data. For example, if concentration wavers, the device sends emotional data to the server, and learning materials with adjusted difficulty levels are provided according to prompts generated by the server, preventing interruptions to learning. On the other hand, when the user is concentrating, the device displays a "concentration badge" to visually recognize the user's efforts and maintain motivation.

[0545] As a concrete example, suppose a student is using a device to solve a math problem. The emotion engine analyzes the student's tired expression and generates a prompt message such as, "Let's take a short break. Afterwards, try again with an easier problem." This allows the student to continue learning at their own pace.

[0546] In this way, the system can provide an educational experience tailored to the individual characteristics of each student and improve learning effectiveness through real-time feedback.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The server receives student learning data sent from terminals and records it in a database. The inputs are learning time, accuracy rate, and response speed, and the output is the storage of this data in the database. This process is performed using a database management system.

[0550] Step 2:

[0551] The device uses an emotion engine to analyze students' facial expressions and voices, generating emotional data about the students. The input is real-time video and audio data of the students, and the output is the analyzed emotional state (e.g., concentration, fatigue, motivation). This data is sent to a server.

[0552] Step 3:

[0553] The server inputs collected training data and sentiment data into a generating AI model to create an individualized learning plan. The input consists of historical training data and the most recent sentiment data, while the output is an individually optimized learning plan. Machine learning libraries are used throughout this process.

[0554] Step 4:

[0555] The server generates learning content and prompts based on the generated learning plan. The prompts include instructions on what the student should learn next. The input is the individualized learning plan, and the output is the learning content and prompts.

[0556] Step 5:

[0557] The terminal displays learning content and prompt messages received from the server to the student. The input is content data from the server, and the output is the screen display for the student. This allows the user to clearly understand what they should learn next.

[0558] Step 6:

[0559] Users continue learning on their devices, answering questions while receiving feedback. This feedback includes motivational badges and comments tailored to their emotional state. Based on user input, the system prepares to move to the next step in real time.

[0560] (Application Example 2)

[0561] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0562] Traditional education systems allow for content customization based on learners' progress, but they are insufficient in considering learners' emotional states. This makes it difficult to maintain learner motivation and hinders the maximization of learning effectiveness. Furthermore, the lack of emotionally responsive feedback prevents appropriate support from being provided to learners.

[0563] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0564] In this invention, the server includes information processing means for collecting and analyzing learner learning information, means for collecting learner emotional information using an emotional analysis engine, and information processing means for generating an individualized learning plan based on the learning information and emotional information. This makes it possible to provide an optimal learning plan that takes the learner's emotional state into account, maintain motivation, and improve learning effectiveness.

[0565] An "information processing device" is a device that collects and analyzes information and performs processing based on that information as needed.

[0566] A "learning plan" is a plan that includes educational content and a schedule optimized for each individual learner.

[0567] An "output device" is a device that displays or transmits information or plans, either physically or digitally.

[0568] "Communication method" refers to a means of sending and receiving information, enabling data exchange between different devices.

[0569] The "emotion analysis engine" is a system that analyzes the emotional state of a learner based on facial recognition and voice patterns, and outputs the results as data.

[0570] An "indicator" is a standard used to express a learner's level of achievement or progress in numerical or other formats.

[0571] "Rewards" are incentives provided to acknowledge and enhance learners' success and efforts.

[0572] This invention configures the system as follows to provide an individualized learning experience that takes into account the learner's emotional state.

[0573] First, the server uses an information processing device equipped with multiple sensors to collect and analyze student learning data and emotional data. The information processing device utilizes a cloud-based database to record learning progress, and the emotional analysis engine employs facial recognition technology (e.g., camera device) and voice analysis technology (e.g., microphone device). Specifically, cloud services such as Amazon Rekognition and Google Cloud Vision are used.

[0574] Next, the server applies machine learning algorithms to generate personalized learning plans based on the collected data. This utilizes algorithms implemented in programming languages ​​such as Python. These algorithms analyze the learner's progress and sentiment data to adjust appropriate educational content and its presentation method.

[0575] The terminal is an output device that displays educational content generated based on the learning plan to the user. This terminal can be a digital device such as a tablet or a personal computer. This allows the user to access learning content that changes in real time and receive feedback through the terminal.

[0576] Furthermore, based on emotional data, the system incorporates techniques to engage learners and deliver targeted instruction. For example, if the server detects that a user is feeling tired, it will temporarily lower the difficulty level of the learning material or provide content that encourages a break. This feature reduces the burden on learners and enables the provision of an efficient and sustainable learning environment.

[0577] Specifically, the system provides a way to enhance a learner's sense of accomplishment by automatically awarding them a "focus badge" via a visual interface when they complete a session in which they are particularly focused. Generative AI models can also be used for learning content and feedback. For example, one possible prompt command could be to "generate an optimal learning plan based on the learner's current emotional state" to the AI ​​model.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The server receives learner learning data and emotional data from the terminal. Learning data includes answer history to questions and performance data, while emotional data includes information on facial expressions and tone of voice acquired through the camera and microphone. The server receives this data as input and performs initial data conversion.

[0581] Step 2:

[0582] The server processes the received emotional data using an emotion analysis engine. Specifically, it uses facial recognition technology to analyze facial expressions and voice analysis technology to check the tone and rhythm of the voice. This analysis quantifies the learner's emotional state and outputs states such as "concentrated" or "tired." Based on the evaluation results of the outputted emotional state, it generates real-time feedback data.

[0583] Step 3:

[0584] The server utilizes machine learning algorithms to integrate training data and sentiment data to generate personalized learning plans. This plan generation process uses Python programming to determine the optimal learning content and pace for each learner. The generated learning plan is output in the form of learning content and a progress schedule.

[0585] Step 4:

[0586] The device receives the learning plan sent from the server and presents the educational content to the learner visually and audibly. The learning materials are presented in text and video format via the display device, and if audio guides are available, they are played through the built-in speaker. The user works through the materials and enters their answers using an input device.

[0587] Step 5:

[0588] The server scores and evaluates learners' answers in real time. Specifically, it uses an automated scoring algorithm to determine whether an answer is correct or incorrect. The scoring results are stored in a progress management database, and evaluation indicators such as achievement level are displayed to the user on the screen.

[0589] Step 6:

[0590] The server generates indicators and rewards to motivate learners based on their emotional state. For example, if a learner concentrates, a "concentration badge" is generated and displayed on the learner's interface. Based on the generation AI model, the prompt "Generate the optimal learning reward based on the learner's current emotional state" is used.

[0591] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0592] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0593] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0597] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0598] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0599] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0600] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0601] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0602] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0603] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0604] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0605] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0606] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0607] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0608] This invention is embodied in the following manner as a form for realizing an AI educational tutor that operates on a computer system. The system of the present invention includes an electronic computing device built on a server, a terminal accessed by a user, and communication means connecting the two.

[0609] The server, as an electronic computing device, is equipped with multiple machine learning algorithms. This allows the server to collect student learning data transmitted from terminals and analyze individual learning styles and performance. Based on the analysis results, the server automatically generates a learning plan optimized for each student. This plan includes specific learning materials, assignments, and a study schedule.

[0610] The terminal is a device used by students, and the learning plan generated through this terminal is displayed. Users can progress through the learning content presented on the terminal at their own pace. The terminal communicates with the server in real time and provides immediate feedback on the student's answers. This allows users to correct mistakes immediately during the learning process.

[0611] Furthermore, this invention also includes a function that allows the server to automatically grade assignments submitted by students. The AI ​​uses grading criteria to provide a fair evaluation and records the results in a progress management database. Based on this, the server can re-evaluate the students' progress and adjust their learning plans as needed.

[0612] Furthermore, to enhance motivation, the server generates points and badges based on the student's progress and notifies the device. This visualization allows users to feel their own progress and maintain motivation for further learning. For example, in the case of a middle school student learning the basics of mathematics, the server proposes a plan to focus on strengthening their weakest areas, and each time the user progresses according to the plan and achieves the set goals, the device displays a badge as a reward.

[0613] In this way, the present invention, as an AI-powered educational support system, can provide effective learning support tailored to each individual student.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The server collects past learning data and current progress data from the user's terminal. This data includes questions answered, scores obtained, and study time.

[0617] Step 2:

[0618] The server analyzes the collected data using machine learning algorithms. The algorithms identify patterns in students' learning styles, strengths, and weaknesses, and generate personalized learning plans for each student.

[0619] Step 3:

[0620] The server sends the generated learning plan to the user's device, which then displays learning materials and a learning schedule based on that plan.

[0621] Step 4:

[0622] The user works on learning tasks displayed on their device. When the answer is finalized or in progress, the device sends the input back to the server in real time.

[0623] Step 5:

[0624] The server instantly evaluates the received answers and generates feedback on what the user should improve. This ensures that students receive appropriate support during their learning process.

[0625] Step 6:

[0626] The server automatically grades user-submitted assignments using an AI scoring system and records the scores and feedback on the answers in a progress management database.

[0627] Step 7:

[0628] The server updates progress management data, generates game-like elements (e.g., points or badges) based on the user's achievement level, and notifies the terminal of this.

[0629] Step 8:

[0630] The device displays generated points and badges, showing the user their current performance and next goals. This helps motivate the user to progress to the next stage of learning.

[0631] (Example 1)

[0632] 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".

[0633] To provide effective and efficient educational support tailored to individual learning styles and progress, it is crucial to properly collect and analyze student learning data and provide real-time feedback. However, the current education system makes it difficult to immediately understand each student's learning pattern and propose the optimal learning plan. As a result, there is a challenge in that students do not receive learning experiences that are tailored to their individual progress.

[0634] 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.

[0635] In this invention, the server includes information processing means for collecting and analyzing student progress information, information processing means for forming an individualized educational plan based on the analysis, and display means for presenting assignments based on the educational plan. This enables individualized educational support that responds immediately to the student's learning progress.

[0636] A "student" is a person who participates in a specific learning program within an educational institution or learning environment.

[0637] "Progress information" refers to data that shows the history and status of students' learning activities, and includes items such as study time, answer results, and progress.

[0638] An "information processing system" is a computer system that performs information processing such as data collection, analysis, and result output.

[0639] An "educational plan" is a teaching plan that consists of optimized materials, assignments, and study schedules based on students' learning goals and progress.

[0640] "Display means" refers to devices or interfaces that visually present students with assignments and information based on the educational plan.

[0641] "Communication means" refers to a system or protocol for sending and receiving data between an information processing device and a display means, or between a student and an information processing device.

[0642] "Evaluation" is the process of measuring students' performance on assignments and tests, and providing feedback based on those performance results.

[0643] "Rewards" refer to incentives that enhance students' motivation to learn, such as points or badges that indicate their level of academic achievement.

[0644] This invention aims to provide an optimized educational experience for each student within a learning support system. Specifically, it focuses on building a system that facilitates the smooth flow of information between servers, terminals, and users.

[0645] The server is an information processing device that has multiple machine learning algorithms internally. The server collects progress information sent from students and analyzes it using a generative AI model. This analysis is performed adaptively through the generative AI model using specific prompt sentences (e.g., "Suggest the best topic to study next"). Starting with these prompt sentences, the server can generate an educational plan tailored to the student's level of understanding. This plan includes teaching materials, assignments, and a learning schedule, and is dynamically modified as needed.

[0646] The terminal is a device used by students (users) for learning. The terminal displays the learning plan sent from the server and provides an interface for users to work on assignments. The terminal has a communication mechanism to send user input to the server in real time and receive immediate feedback from the server. This allows users to quickly correct mistakes during learning and proceed with effective learning.

[0647] Users execute educational plans generated by the server via their devices, learning at their own pace. Evaluations and feedback from the server are visually displayed on the device, allowing users to recognize their own learning progress. Rewards and badges are displayed according to learning progress, further enhancing motivation to learn.

[0648] As a concrete example, in the case of a middle school student learning mathematics, the system analyzes the user's past error patterns and suggests appropriate learning materials and assignments. An example of a prompt message is "Suggest the next mathematical topic to learn and generate appropriate practice problems," and by following these instructions, the user can efficiently improve their understanding.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server receives student learning progress information sent from the terminal. This progress information includes answer history, study time, and correct answer rate. Based on this input data, the server uses machine learning algorithms to analyze the data. This analysis identifies students' strengths and weaknesses and extracts learning patterns.

[0652] Step 2:

[0653] The server uses a generative AI model to create personalized learning plans from the analysis results. This model takes specific prompt statements (e.g., "Suggest the best topic to study next") as input and generates learning materials, assignments, and study schedules tailored to each student's level of understanding and progress. At this stage, the output is a personalized learning plan.

[0654] Step 3:

[0655] The server sends the generated lesson plan to the terminal. The terminal receives this lesson plan and displays it visually to the user. Specifically, the next tasks and learning content to be completed are displayed on the terminal's screen, and the user proceeds with their learning accordingly.

[0656] Step 4:

[0657] The user works on a task presented through their device. They input their answer, which is sent to the server in real time. The input in this step is the user's answer data, which the server immediately evaluates and generates feedback. As output, the feedback is immediately returned to the device and displayed to the user. This allows the user to correct their mistakes on the spot.

[0658] Step 5:

[0659] The server automatically grades the user's assignment answers and records the evaluation results in a progress management database. These evaluation results are used to improve future educational plans. Furthermore, based on the outputted evaluation results, the server generates prompts again to suggest the next learning steps. Based on these results, the server adjusts the new learning plan and prepares to send it to the terminal again.

[0660] Step 6:

[0661] The server generates rewards and badges based on the user's academic achievement and notifies the user's device. These notifications are displayed on the device screen, allowing the user to check their grades and progress. This helps maintain the user's motivation to learn and increases their enthusiasm for the next assignment.

[0662] (Application Example 1)

[0663] 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".

[0664] In today's educational environment, individualized instruction tailored to each student's learning style and progress is required, but providing individual support to a large number of students is a significant burden for educators. Furthermore, when students study at home, it is difficult for them to receive appropriate feedback and support. This leads to challenges such as decreased student motivation and limited learning effectiveness. In addition, since learning support via audio is not yet common, it is difficult to provide an intuitive and flexible learning experience.

[0665] 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.

[0666] In this invention, the server includes an information processing device that collects and analyzes student learning data, an information processing device that generates an individualized learning plan based on the analysis, a presentation device that outputs learning content based on the learning plan, and a voice dialogue device that analyzes student questions using speech recognition technology and provides learning support. This makes it possible to automatically generate individualized learning plans for each student and provide real-time feedback, thereby realizing an individualized instruction environment that enhances learning motivation.

[0667] An "information processing device" is a device that has the ability to collect student learning data and perform calculations based on that data.

[0668] A "learning plan" is a guideline for educational instruction that includes optimized materials, assignments, and schedules based on a student's learning style and progress.

[0669] A "presentation device" is a device that displays or communicates learning content to students visually or audibly, based on a generated learning plan.

[0670] A "communication device" is a device that has data transmission and reception capabilities to provide real-time feedback on learning content and connect students with a server.

[0671] A "voice dialogue device" is a device that uses speech recognition technology to analyze students' oral questions and provides dialogue-based learning support accordingly.

[0672] To realize this invention, it is necessary to build an information processing system equipped with multiple machine learning algorithms on a server. The server collects learning data from students' terminals and uses that data to analyze students' learning styles and performance. Specifically, it performs data analysis using machine learning libraries such as TensorFlow and PyTorch. Based on the analysis results, the server automatically generates an individualized learning plan. The learning plan includes learning materials, assignments, and schedules tailored to the student's weaknesses and learning goals.

[0673] The terminal is a device used by students and displays the generated learning plan in real time. The terminal utilizes the Google Speech-to-Text API to convert student voice input into text, and the server provides rapid feedback based on this. In addition, a speech synthesis system is used to deliver feedback to students verbally, allowing them to adjust their actions immediately.

[0674] On the user side, the server evaluates progress based on the learning status and adjusts the learning plan as needed. The evaluation process uses intuitive visualization methods to grasp the student's achievement level, generating points and badges to visualize it and increase student motivation.

[0675] For example, in a scenario where an elementary school student is solving basic math problems, the server identifies the areas where the student is struggling and generates a plan that includes additional practice problems needed to overcome them. If successful, the robot provides feedback such as "Well done!" and a new badge appears on the device.

[0676] Examples of prompts to input into a generative AI model are as follows:

[0677] "Elementary school students are solving basic math problems. I want to provide feedback on their mistakes and offer appropriate advice. Please generate a feedback message."

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The server collects student learning data transmitted from terminals. The input here consists of the student's past learning history and test results. Based on this data, an initial data analysis is performed using a machine learning algorithm. The output provides an overview of the student's learning style and current level of proficiency.

[0681] Step 2:

[0682] Based on the learning style obtained in Step 1, the server creates an optimal learning plan using TensorFlow or PyTorch implemented in Python. The input is the analyzed learning style data, and based on this information, it generates a list of materials and assignments best suited to the student. As output, an individualized learning plan is formed.

[0683] Step 3:

[0684] The terminal displays the generated learning plan to the student. The input here is learning plan data received from the server, and the output is a visualized list of learning materials and a schedule. This display is shown on the terminal screen, presented in a way that students can intuitively understand.

[0685] Step 4:

[0686] Users (students) complete assignments according to their learning plan via a device and input their answers via voice or text. The input is answer data provided by the student, which the device collects and converts to text using the Google Speech-to-Text API in the case of voice input.

[0687] Step 5:

[0688] The server evaluates the collected answer data. The input is student answer data, and the server uses a machine learning algorithm to score it and generate feedback. The output is the correctness rating of the answer and a feedback message based on that rating.

[0689] Step 6:

[0690] The terminal provides students with feedback received from the server. The input is a feedback message generated by the server, which is then conveyed to the student as voice feedback using a speech synthesis system. Simultaneously, it is also displayed on the screen as visual feedback.

[0691] Step 7:

[0692] The user receives feedback and selects the next step. The main inputs in this step are the student's own responses and their selection of the next learning step. Based on this, the server re-evaluates the learning plan, generates an updated plan if necessary, and prepares for the next step.

[0693] Step 8:

[0694] The server evaluates students' progress and generates points and badges based on their achievement level. The input is student progress data, and a generation AI model is used to calculate achievement indicators according to evaluation criteria. As output, rewards (points and badges) are displayed on the student's device, improving their motivation to learn.

[0695] 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.

[0696] This invention provides an individualized learning experience that takes into account the emotional state of students by combining an emotion engine with an AI educational tutor system. The system consists of a server, terminals, and an emotion engine.

[0697] The server collects student learning progress data and uses this data to generate individually optimized learning plans. In addition, an emotion engine collects emotional data through facial recognition and voice analysis of students and sends it to the server. Based on this emotional data, the server adjusts the content and pace of the learning plan in real time, enabling more effective learning support.

[0698] The terminal is a device that students access and is responsible for displaying learning plans and materials to them. Users engage in learning activities and input answers through this terminal. The terminal is constantly connected to the server and provides guidance to students, including feedback based on data from the emotion engine.

[0699] Specifically, the server receives real-time information on students' emotional states (e.g., focused, tired, excited) from the emotion engine and can adjust the on-screen learning materials accordingly. For example, if the system analyzes that a student is tired, it can provide learning content with a temporarily reduced difficulty level.

[0700] Furthermore, the server distributes points and badges based on emotional state in addition to the progress of the learning plan. This allows the device to show students their learning achievements and provide emotional support as well. For example, a "focus badge" can be awarded for sessions in which students have concentrated particularly hard work, thereby increasing their motivation.

[0701] Thus, the system of the present invention aims to improve learning effectiveness by providing a learning environment that is more suitable for each individual student through the integration of emotional feedback.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The user logs into their device and begins learning. The device sends learning progress data to the server and receives the latest learning plan.

[0705] Step 2:

[0706] The emotion engine acquires user facial recognition and voice data to analyze their emotional state in real time. This data is then sent to the server.

[0707] Step 3:

[0708] The server receives the learning plan and emotional data, and selects learning content optimized for the student's emotional state. It adjusts the difficulty and type of content and sends it to the device.

[0709] Step 4:

[0710] The device displays the user the pre-configured learning content received from the server. The user then proceeds with their learning based on that content.

[0711] Step 5:

[0712] When a user answers a learning question, the device sends the answer data to the server. The server receives this data, immediately scores it, and performs an evaluation.

[0713] Step 6:

[0714] The server generates feedback that takes into account the user's emotional state and provides it to the user via the terminal. This feedback includes the accuracy of the answer and areas for improvement.

[0715] Step 7:

[0716] The server generates points and badges based on user progress and sentiment data, rewarding users to increase their motivation to learn.

[0717] Step 8:

[0718] The device displays points and badges sent from the server to the user, visually communicating their progress. It also suggests the next learning activity.

[0719] Through this series of processes, the system of the present invention can improve the user's learning efficiency and motivation.

[0720] (Example 2)

[0721] 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".

[0722] Traditional electronic learning systems have faced challenges in providing individualized education that adequately considers each student's learning progress and emotional state. Furthermore, they lack real-time feedback and features to promote increased motivation, making it difficult to maintain student engagement.

[0723] 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.

[0724] In this invention, the server includes information processing means for collecting and analyzing student learning data, means for generating personalized learning plans, and means for dynamically adjusting learning content based on student emotional data. This makes it possible to provide each student with an optimized learning experience in real time and to enhance their motivation to learn.

[0725] An "information processing device" is an electronic device used to collect and analyze students' learning data and emotional data.

[0726] A "learning plan" refers to an individualized educational program based on students' learning and emotional data.

[0727] "Emotional data" refers to information about students' emotions, obtained in real time from their facial expressions and voice.

[0728] A "display device" is a device used to present learning content to students based on their learning plan.

[0729] A "communication device" is a device that has the function of transmitting information to provide real-time feedback to students.

[0730] A "generative AI model" is a machine learning model that generates personalized learning plans and prompts based on data.

[0731] A "prompt sentence" is an instructional sentence generated to guide students to the next learning step.

[0732] "Rewards and badges" are incentives given to students based on their learning achievements and emotional state.

[0733] This invention is an electronic education system that utilizes advanced information processing technology to personalize students' learning experiences. The system mainly consists of a server, terminals, and an emotion engine.

[0734] The server functions as the core of learning data collection. It records all learning activities performed by students through their devices and manages them using a database management system. Specifically, a general-purpose database management system can be used for efficient database management. The server also utilizes a generative AI model to generate personalized learning plans based on the collected data. Machine learning libraries are expected to be used for the generative AI model.

[0735] The terminal is a device that students directly interact with as a learning interface, and devices such as computers and tablets are used. The terminal is equipped with a means to display learning content sent from the server. Furthermore, an emotion engine is integrated to capture students' facial expressions and voices, and analyzes the students' emotional state in real time. As a result, the terminal can provide optimal learning content and motivation according to the user's current state.

[0736] Through this system, users learn while receiving feedback based on emotional data. For example, if concentration wavers, the device sends emotional data to the server, and learning materials with adjusted difficulty levels are provided according to prompts generated by the server, preventing interruptions to learning. On the other hand, when the user is concentrating, the device displays a "concentration badge" to visually recognize the user's efforts and maintain motivation.

[0737] As a concrete example, suppose a student is using a device to solve a math problem. The emotion engine analyzes the student's tired expression and generates a prompt message such as, "Let's take a short break. Afterwards, try again with an easier problem." This allows the student to continue learning at their own pace.

[0738] In this way, the system can provide an educational experience tailored to the individual characteristics of each student and improve learning effectiveness through real-time feedback.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] The server receives student learning data sent from terminals and records it in a database. The inputs are learning time, accuracy rate, and response speed, and the output is the storage of this data in the database. This process is performed using a database management system.

[0742] Step 2:

[0743] The device uses an emotion engine to analyze students' facial expressions and voices, generating emotional data about the students. The input is real-time video and audio data of the students, and the output is the analyzed emotional state (e.g., concentration, fatigue, motivation). This data is sent to a server.

[0744] Step 3:

[0745] The server inputs collected training data and sentiment data into a generating AI model to create an individualized learning plan. The input consists of historical training data and the most recent sentiment data, while the output is an individually optimized learning plan. Machine learning libraries are used throughout this process.

[0746] Step 4:

[0747] The server generates learning content and prompts based on the generated learning plan. The prompts include instructions on what the student should learn next. The input is the individualized learning plan, and the output is the learning content and prompts.

[0748] Step 5:

[0749] The terminal displays learning content and prompt messages received from the server to the student. The input is content data from the server, and the output is the screen display for the student. This allows the user to clearly understand what they should learn next.

[0750] Step 6:

[0751] Users continue learning on their devices, answering questions while receiving feedback. This feedback includes motivational badges and comments tailored to their emotional state. Based on user input, the system prepares to move to the next step in real time.

[0752] (Application Example 2)

[0753] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0754] Traditional education systems allow for content customization based on learners' progress, but they are insufficient in considering learners' emotional states. This makes it difficult to maintain learner motivation and hinders the maximization of learning effectiveness. Furthermore, the lack of emotionally responsive feedback prevents appropriate support from being provided to learners.

[0755] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0756] In this invention, the server includes information processing means for collecting and analyzing learner learning information, means for collecting learner emotional information using an emotional analysis engine, and information processing means for generating an individualized learning plan based on the learning information and emotional information. This makes it possible to provide an optimal learning plan that takes the learner's emotional state into account, maintain motivation, and improve learning effectiveness.

[0757] An "information processing device" is a device that collects and analyzes information and performs processing based on that information as needed.

[0758] A "learning plan" is a plan that includes educational content and a schedule optimized for each individual learner.

[0759] An "output device" is a device that displays or transmits information or plans, either physically or digitally.

[0760] "Communication method" refers to a means of sending and receiving information, enabling data exchange between different devices.

[0761] The "emotion analysis engine" is a system that analyzes the emotional state of a learner based on facial recognition and voice patterns, and outputs the results as data.

[0762] An "indicator" is a standard used to express a learner's level of achievement or progress in numerical or other formats.

[0763] "Rewards" are incentives provided to acknowledge and enhance learners' success and efforts.

[0764] This invention configures the system as follows to provide an individualized learning experience that takes into account the learner's emotional state.

[0765] First, the server uses an information processing device equipped with multiple sensors to collect and analyze student learning data and emotional data. The information processing device utilizes a cloud-based database to record learning progress, and the emotional analysis engine employs facial recognition technology (e.g., camera device) and voice analysis technology (e.g., microphone device). Specifically, cloud services such as Amazon Rekognition and Google Cloud Vision are used.

[0766] Next, the server applies machine learning algorithms to generate personalized learning plans based on the collected data. This utilizes algorithms implemented in programming languages ​​such as Python. These algorithms analyze the learner's progress and sentiment data to adjust appropriate educational content and its presentation method.

[0767] The terminal is an output device that displays educational content generated based on the learning plan to the user. This terminal can be a digital device such as a tablet or a personal computer. This allows the user to access learning content that changes in real time and receive feedback through the terminal.

[0768] Furthermore, based on emotional data, the system incorporates techniques to engage learners and deliver targeted instruction. For example, if the server detects that a user is feeling tired, it will temporarily lower the difficulty level of the learning material or provide content that encourages a break. This feature reduces the burden on learners and enables the provision of an efficient and sustainable learning environment.

[0769] Specifically, the system provides a way to enhance a learner's sense of accomplishment by automatically awarding them a "focus badge" via a visual interface when they complete a session in which they are particularly focused. Generative AI models can also be used for learning content and feedback. For example, one possible prompt command could be to "generate an optimal learning plan based on the learner's current emotional state" to the AI ​​model.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The server receives learner learning data and emotional data from the terminal. Learning data includes answer history to questions and performance data, while emotional data includes information on facial expressions and tone of voice acquired through the camera and microphone. The server receives this data as input and performs initial data conversion.

[0773] Step 2:

[0774] The server processes the received emotional data using an emotion analysis engine. Specifically, it uses facial recognition technology to analyze facial expressions and voice analysis technology to check the tone and rhythm of the voice. This analysis quantifies the learner's emotional state and outputs states such as "concentrated" or "tired." Based on the evaluation results of the outputted emotional state, it generates real-time feedback data.

[0775] Step 3:

[0776] The server utilizes machine learning algorithms to integrate training data and sentiment data to generate personalized learning plans. This plan generation process uses Python programming to determine the optimal learning content and pace for each learner. The generated learning plan is output in the form of learning content and a progress schedule.

[0777] Step 4:

[0778] The device receives the learning plan sent from the server and presents the educational content to the learner visually and audibly. The learning materials are presented in text and video format via the display device, and if audio guides are available, they are played through the built-in speaker. The user works through the materials and enters their answers using an input device.

[0779] Step 5:

[0780] The server scores and evaluates learners' answers in real time. Specifically, it uses an automated scoring algorithm to determine whether an answer is correct or incorrect. The scoring results are stored in a progress management database, and evaluation indicators such as achievement level are displayed to the user on the screen.

[0781] Step 6:

[0782] The server generates indicators and rewards to motivate learners based on their emotional state. For example, if a learner concentrates, a "concentration badge" is generated and displayed on the learner's interface. Based on the generation AI model, the prompt "Generate the optimal learning reward based on the learner's current emotional state" is used.

[0783] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0784] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0785] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0786] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0787] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0788] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0789] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0790] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0791] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0792] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0793] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0794] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0795] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0796] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0797] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0798] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0799] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0800] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0801] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0802] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0803] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0804] The following is further disclosed regarding the embodiments described above.

[0805] (Claim 1)

[0806] A computer that collects and analyzes student learning data,

[0807] A computer that generates an individualized learning plan based on the above analysis,

[0808] A display device that outputs learning content based on the aforementioned learning plan,

[0809] A communication means for providing real-time feedback on the learning content,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The aforementioned computer device includes means for automatically scoring students' learning results and recording their evaluations,

[0813] The system according to claim 1, further comprising means for managing the progress of students based on the aforementioned evaluation.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising a visualization means for generating points or badges according to the students' learning achievement level to improve their motivation to learn.

[0816] "Example 1"

[0817] (Claim 1)

[0818] An information processing device that collects and analyzes student progress information,

[0819] An information processing device that forms an individualized educational plan based on the aforementioned analysis,

[0820] A display means for presenting tasks based on the aforementioned educational plan,

[0821] A means of communication that provides immediate responses to student answers,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The aforementioned information processing device has a function to automatically evaluate the results of assignments given by students and to store those results,

[0825] The system according to claim 1, further comprising a function for evaluating the progress of students based on the results described above.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising a visualization function for improving educational motivation by generating rewards and badges according to the students' academic achievement.

[0828] "Application Example 1"

[0829] (Claim 1)

[0830] An information processing device that collects and analyzes student learning data,

[0831] An information processing device that generates an individualized learning plan based on the above analysis,

[0832] A presentation device that outputs learning content based on the aforementioned learning plan,

[0833] A communication device that provides real-time feedback on the learning content,

[0834] A voice dialogue device that uses speech recognition technology to analyze students' questions and provide learning support,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The aforementioned information processing device is a device that automatically scores students' learning results and records the evaluation,

[0838] The system according to claim 1, further comprising a device for managing student progress based on the aforementioned evaluation.

[0839] (Claim 3)

[0840] The system according to claim 1, further comprising a visualization device for generating points or badges according to the students' learning achievement level to improve their motivation to learn.

[0841] "Example 2 of combining an emotion engine"

[0842] (Claim 1)

[0843] An information processing device that collects and analyzes student learning data,

[0844] An information processing device that generates an individualized learning plan based on the above analysis,

[0845] A means for acquiring student emotional data and dynamically adjusting learning content based on said data,

[0846] A display means that outputs learning content based on the aforementioned learning plan,

[0847] A communication means for providing real-time feedback on the learning content,

[0848] A means for generating prompt sentences based on the generated training data and sentiment data,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The aforementioned information processing device includes means for automatically evaluating students' learning results and recording those results,

[0852] The system according to claim 1, further comprising means for managing the progress of students based on the aforementioned evaluation.

[0853] (Claim 3)

[0854] The system according to claim 1, further comprising means for generating rewards or badges in accordance with the student's learning and emotional state, and for improving motivation to learn.

[0855] "Application example 2 when combining with an emotional engine"

[0856] (Claim 1)

[0857] An information processing device that collects and analyzes students' learning information,

[0858] An information processing device that generates an individualized learning plan based on the above analysis,

[0859] An output device that outputs educational content based on the aforementioned learning plan,

[0860] A communication method that provides real-time responses to the aforementioned educational content,

[0861] We collect learners' emotional information using an emotion analysis engine.

[0862] A means of adjusting learning plans and educational content based on the aforementioned emotional information,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The aforementioned information processing device automatically evaluates the learner's learning performance and records the evaluation;

[0866] The system according to claim 1, further comprising a method for managing the learner's progress based on the aforementioned evaluation.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising a method of expression for generating indicators and rewards according to the learner's level of learning achievement and for improving learning motivation. [Explanation of Symbols]

[0869] 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

1. An information processing device that collects and analyzes student learning data, An information processing device that generates an individualized learning plan based on the above analysis, A presentation device that outputs learning content based on the aforementioned learning plan, A communication device that provides real-time feedback on the learning content, A voice dialogue device that uses speech recognition technology to analyze students' questions and provide learning support, A system that includes this.

2. A device that automatically grades students' learning results and records the evaluations, The system according to claim 1, further comprising a device for managing the progress of students based on the aforementioned evaluation.

3. The system according to claim 1, further comprising a visualization device for generating points or badges according to the students' learning achievement level to improve their motivation to learn.

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