System

Abstract

To provide a system capable of creating an individual learning plan, identifying the problems and weak points of a learner, and taking a proper measure.SOLUTION: A system according to an embodiment comprises a plan creation unit, an identification unit, a tracking unit, and a motivation unit. The plan creation unit analyzes the learning history and goal of a user, and creates a learning plan according to the user. The identification unit identifies the problems and weak points of the user in a learning process of the learning plan created by the plan creation unit. The provision unit provides the user with descriptions and a simulation test on the basis of the problems and weak points identified by the identification unit. The tracking unit tracks the progress of the user in the learning plan created by the plan creation unit. The motivation unit improves motivation on the basis of the progress state tracked by the tracking unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately create individual learning plans, identify learners' problems and weaknesses, and take appropriate measures, leaving room for improvement.

[0005] The system according to the embodiment aims to create an individual learning plan, identify problems and weaknesses of a learner, and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a plan creation unit, an identification unit, a provision unit, a tracking unit, and a motivation unit. The plan creation unit analyzes a user's learning history and goals, and creates a study plan appropriate for the user. The identification unit identifies the user's problems and weaknesses in the course of learning with the study plan created by the plan creation unit. The provision unit provides explanations and mock tests to the user based on the problems and weaknesses identified by the identification unit. The tracking unit tracks the user's progress with the study plan created by the plan creation unit. The motivation unit improves motivation based on the progress tracked by the tracking unit. Effect of the Invention

[0007] The system according to the embodiment can create an individual learning plan, identify problems and weaknesses of a learner, and take appropriate measures. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] 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.

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

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

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

[0013] In the following embodiments, the coded 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), and magnetic tapes.

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The AI ​​tutor AI assistant system for education and learning support according to the embodiment of the present invention is a system that analyzes a user's learning history and goals and creates a learning plan according to the user. This system analyzes the user's learning history and goals and creates a learning plan according to the user. Next, in the process of proceeding with learning based on the created learning plan, the system identifies the user's problems and weaknesses. Based on the identified problems and weaknesses, the system provides the user with explanations and mock exams. In addition, the system tracks the user's progress in the learning plan and provides measures to improve motivation based on the progress. This maximizes the user's learning effect and supports efficient learning. For example, the system analyzes the user's learning history and goals. Data such as past test results, study time, and learning content are collected and analyzed by AI. This makes it possible to grasp the user's strengths and weaknesses and create an individual learning plan. Next, in the process of proceeding with learning based on the created learning plan, the system identifies the user's problems and weaknesses. If the correct answer rate for a specific problem is low, the system identifies that problem as the user's weakness. Based on the identified problems and weaknesses, the system provides the user with explanations and mock exams. For problems that the user is weak at, detailed explanations are provided to deepen understanding. In addition, mock exams can help users prepare for the actual exam. In addition, the system tracks the user's progress in the learning plan. The system monitors the learning progress in real time and generates an alert if an abnormality is detected. This makes it easier for users to grasp the progress of their learning. Based on the progress, measures to improve motivation are provided. Gamification technology is used to improve the user's motivation to learn. In addition, customized encouraging messages can be sent according to the learning progress to maintain the user's motivation. In this way, the AI ​​tutor AI assistant has functions such as creating individual learning plans, identifying problems and weaknesses, providing explanations and mock exams, tracking progress, and measures to improve motivation, and can comprehensively support the user's learning. As a result, the AI ​​tutor AI assistant system for education and learning support can maximize the user's learning effect and support efficient learning.

[0029] An AI tutor AI assistant system for education and learning support according to an embodiment includes a plan creation unit, a discrimination unit, a provision unit, a tracking unit, and a motivation unit. The plan creation unit analyzes the user's learning history and goals, and creates a learning plan according to the user. The user's learning history includes, but is not limited to, past study time, study content, and grades. The plan creation unit collects data such as past test results, study time, and study content, and the AI ​​analyzes it. This makes it possible to grasp the user's strengths and weaknesses and create an individual learning plan. The discrimination unit identifies the user's problems and weaknesses in the learning process using the study plan created by the plan creation unit. For example, if the correct answer rate for a specific problem is low, the discrimination unit identifies the problem as the user's weakness. The provision unit provides the user with explanations and mock exams based on the problems and weaknesses identified by the discrimination unit. The provision unit provides detailed explanations for problems that the user is weak at, for example, to deepen understanding. In addition, the mock exam allows the user to prepare for the actual exam. The tracking unit tracks the user's progress in the learning plan created by the plan creation unit. For example, the tracking unit monitors the learning progress in real time and generates an alert when an abnormality is detected. This makes it easier for the user to grasp the learning progress. The motivation unit improves motivation based on the progress tracked by the tracking unit. The motivation unit improves the user's motivation to learn, for example, by using gamification technology. In addition, the motivation unit can maintain the user's motivation by sending encouraging messages customized according to the learning progress. As a result, the AI ​​tutor AI assistant system for education and learning support according to the embodiment can maximize the user's learning effect and support efficient learning.

[0030] The plan creation unit analyzes the user's learning history and goals, and creates a study plan according to the user. The user's learning history includes, but is not limited to, past study time, study content, and grades. The plan creation unit collects data such as past test results, study time, and study content, and the AI ​​analyzes it. Specifically, the AI ​​analyzes the user's past study data using a machine learning algorithm to grasp the user's study patterns and tendencies. For example, if the user has obtained high scores in a specific subject, the AI ​​determines that the subject is a good subject, and conversely, if the user has obtained low scores, the AI ​​determines that the subject is a weak subject. In addition, by analyzing the study time data, it is possible to identify which time of day the user can study most efficiently. This allows the plan creation unit to create an individual study plan that takes into account the user's strong and weak areas and the optimal study time period. Furthermore, the plan creation unit sets short-term and long-term goals according to the user's goals, and provides a study plan based on them. For example, if the user's goal is to pass a specific exam, the AI ​​creates a plan that focuses on learning the knowledge and skills required for the exam. In addition, the plan creation unit receives feedback from the user and updates the study plan at any time, thereby providing an optimal learning environment. This enables the plan creation unit to maximize the effect of the user's learning and support efficient learning.

[0031] The identification unit identifies the user's problems and weaknesses in the learning process with the learning plan created by the plan creation unit. For example, if the correct answer rate for a specific problem is low, the identification unit identifies that problem as the user's weak point. Specifically, the AI ​​analyzes the user's answer data and identifies the user's weak points based on indicators such as the correct answer rate and answer time. For example, if the user has a low correct answer rate for a specific math problem, it determines that the problem is the user's weak point. In addition, if the answer time is long, it can also be identified as the user's insufficient understanding of the problem. Furthermore, the identification unit can comprehensively evaluate weaknesses in a specific field or topic based on the user's learning history and past grades. This allows the identification unit to accurately identify problems and weaknesses in the user's learning process and use them to improve individual learning plans. Furthermore, the identification unit can also suggest the most suitable learning method taking into account the user's learning style and learning pace. For example, if the user prefers visual learning, it provides teaching materials that make extensive use of visual aids. In addition, the identification unit can receive feedback from the user to evaluate the effectiveness of the learning plan and modify the plan as necessary. This enables the recognition unit to maximize the learning effect of the user and support efficient learning.

[0032] The providing unit provides the user with explanations and mock exams based on the problems and weaknesses identified by the identifying unit. For example, the providing unit provides detailed explanations for problems that the user is weak at, deepening the user's understanding. In addition, the mock exam allows the user to prepare for the actual exam. Specifically, the providing unit uses AI to generate explanatory videos and texts specialized for the user's weak points and provides them to the user. For example, if the user does not fully understand a particular math problem, the providing unit provides a detailed explanatory video for that problem and provides a step-by-step explanation. In addition, the providing unit provides mock exams at appropriate times according to the user's learning progress, allowing the user to prepare for the actual exam. The mock exams are created based on the actual exam format and are designed to allow the user to become familiar with the exam environment. In addition, the providing unit analyzes the user's answer data and provides feedback based on indicators such as the accuracy of the answers and the answer time. This allows the user to reaffirm his or her weak points and understand the areas for improvement. In addition, the providing unit can provide customized teaching materials according to the user's learning style and learning pace. For example, if the user prefers visual learning, the providing unit provides learning materials that make extensive use of visual aids, thereby maximizing the user's learning effect and supporting efficient learning.

[0033] The tracking unit tracks the progress of the user in the learning plan created by the plan creation unit. For example, the tracking unit monitors the learning progress in real time and generates an alert when an abnormality is detected. Specifically, the tracking unit collects the user's learning data in real time and visualizes the learning progress. For example, the tracking unit displays the current progress in graphs and charts for the learning goals set by the user, allowing the user to grasp his / her learning situation at a glance. The tracking unit also monitors the user's learning pace and learning time, and generates an alert when an abnormality is detected. For example, if the user significantly exceeds the scheduled learning time or the learning pace is slow, an alert is generated and the user is notified. This allows the user to grasp his / her learning situation in real time and adjust the learning plan as necessary. Furthermore, the tracking unit can analyze long-term learning trends based on the user's learning history and use them to improve future learning plans. For example, the tracking unit analyzes past learning data to grasp fluctuations in learning effects at specific times and use them to optimize future learning plans. The tracking unit can also continuously improve the accuracy and functionality of the tracking system based on user feedback. This enables the tracking unit to maximize the learning effect of the user and support efficient learning.

[0034] The motivation unit improves the motivation based on the progress tracked by the tracking unit. The motivation unit improves the user's motivation to learn, for example, by using gamification technology. In addition, the motivation unit can maintain the user's motivation by sending a customized encouraging message according to the learning progress. Specifically, the motivation unit grants points and badges according to the user's learning progress, allowing the user to feel a sense of accomplishment. For example, when a specific learning goal is achieved, points are granted and the user can use the points to earn rewards. In addition, the motivation unit transmits a customized encouraging message according to the user's learning progress. For example, when the user achieves a learning goal, an encouraging message is sent to increase the user's motivation. In addition, the motivation unit can also propose optimal motivation improvement measures in consideration of the user's learning history and learning style. For example, if the user prefers visual learning, an encouraging message that makes heavy use of visual aids is provided. In addition, the motivation unit can receive feedback from the user, evaluate the effectiveness of the motivation improvement measures, and improve them as necessary. This allows the motivation unit to maximize the user's learning effect and support efficient learning.

[0035] The plan creation unit can create a learning plan suitable for the user using a machine learning model. The plan creation unit creates a learning plan suitable for the user using a machine learning model such as a neural network, a decision tree, or a support vector machine. For example, a neural network has the ability to learn a large amount of data and recognize complex patterns. A decision tree hierarchically divides data and performs classification and regression. A support vector machine finds a boundary for classifying data in a high-dimensional space. This allows the use of a machine learning model to create an optimal learning plan for the user. A part or all of the above-mentioned processing in the plan creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan creation unit can input the user's learning history data to the generation AI and cause the generation AI to generate a learning plan.

[0036] The providing unit can provide a commentary customized according to the learning style of the user. The providing unit provides a commentary customized according to a learning style such as visual, auditory, or tactile. For example, a visual user is provided with a commentary that uses a lot of diagrams and graphs. An auditory user is provided with a commentary that uses a lot of audio commentary and podcasts. A tactile user is provided with a commentary that uses a lot of experiments and hands-on activities. This allows the user to improve their understanding by providing a commentary that matches their learning style. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's learning style data into the generating AI and cause the generating AI to generate a customized commentary.

[0037] The tracking unit can monitor the progress of the user in real time and generate an alert when a specific abnormality is detected. The tracking unit, for example, monitors the progress of learning in real time and generates an alert when an abnormality is detected. For example, it detects abnormalities such as stagnation of learning and a sudden drop in grades. This makes it possible to support the user's learning by monitoring the progress in real time and generating an alert when an abnormality is detected. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's learning data to the generation AI and cause the generation AI to detect an abnormality.

[0038] The motivation unit can increase the user's motivation to learn using gamification technology. The motivation unit can increase the user's motivation to learn using gamification technology such as a point system, leveling up, and badges. For example, the point system is a mechanism that allows a user to earn points each time they progress in their learning. Leveling up is a mechanism that allows a user to increase their level when they earn a certain number of points. A badge is a digital badge that can be earned when a specific condition is met. As a result, the use of gamification technology can increase the user's motivation to learn. Some or all of the above-mentioned processing in the motivation unit may be performed using, for example, AI, or may be performed without using AI. For example, the motivation unit can input the user's learning progress data into the generation AI and cause the generation AI to generate gamification elements.

[0039] The motivation unit can send a customized encouraging message according to the user's learning progress. The motivation unit sends the encouraging message in the form of, for example, a text message, a voice message, a video message, or the like. For example, the text message includes words of encouragement customized according to the user's learning progress. The voice message provides a customized voice encouragement according to the user's learning progress. The video message provides a customized video encouragement according to the user's learning progress. This makes it possible to maintain the motivation of the user by sending an encouraging message according to the learning progress. A part or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI or may be performed without using AI. For example, the motivation unit can input the user's learning progress data to the generation AI and cause the generation AI to generate the encouraging message.

[0040] The plan creation unit can use the biometric data from the wearable device to analyze the user's emotional state, measure the stress level, and create a study plan based on the result. The plan creation unit acquires biometric data such as heart rate, electrodermal activity, and body temperature from the wearable device and analyzes the user's emotional state. For example, the plan creation unit analyzes the fluctuation of the heart rate to measure the user's stress level. The plan creation unit analyzes the electrodermal activity to measure the user's tension state. The plan creation unit analyzes the body temperature fluctuation to measure the user's relaxed state. In this way, by using the biometric data from the wearable device, a study plan according to the user's stress level can be created. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plan creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan creation unit can input the biometric data acquired from the wearable device to the generation AI and cause the generation AI to execute the analysis of the emotional state.

[0041] The motivation unit allows the AI ​​character to interactively provide a cheering message according to the user's learning progress. The motivation unit provides a cheering message according to the user's learning progress using an AI character having functions such as voice dialogue, facial expression change, and movement. For example, the voice dialogue function is used to provide words of encouragement according to the user's learning progress. The facial expression change function is used to cheer the user with a facial expression according to the user's learning progress. The movement function is used to cheer the user with a movement according to the user's learning progress. This allows the AI ​​character to interactively provide a cheering message, thereby improving the user's motivation. Some or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI, or may be performed without using AI. For example, the motivation unit can input the user's learning progress data to the generation AI and cause the generation AI to generate a cheering message.

[0042] The providing unit can provide a live commentary by an AI lecturer generated in real time based on the user's learning theme. The providing unit provides a live commentary by an AI lecturer in the form of, for example, real-time generation, pre-recording, interactive commentary, etc. For example, in the real-time generation, an AI generates a commentary in real time based on the user's learning theme. In the pre-recording, a commentary recorded in advance is provided based on the user's learning theme. In the interactive commentary, an AI interactively provides a commentary based on the user's learning theme. This allows the user's understanding to be deepened by providing a live commentary by an AI lecturer generated in real time. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's learning theme data to the generating AI and cause the generating AI to generate a live commentary.

[0043] The motivation unit can provide information on the AI ​​pet that grows according to the user's learning progress. The motivation unit can, for example, improve the user's motivation to learn by using an AI pet that grows according to the learning progress. For example, the AI ​​pet grows according to the user's learning progress and has an interaction function. As the user progresses with learning, the AI ​​pet grows and acquires new functions and actions. In this way, by providing information on the growing AI pet, the user's motivation to learn can be improved. Some or all of the above-mentioned processing in the motivation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the motivation unit can input the user's learning progress data to the generation AI and cause the generation AI to execute the growth of the AI ​​pet.

[0044] The plan creation unit can analyze the genetic information of the user and create a learning plan based on the results. The plan creation unit can analyze genetic information such as DNA sequence and genetic characteristic evaluation, and create a learning plan based on the genetic characteristics of the user. For example, the plan creation unit can analyze the DNA sequence and evaluate the genetic characteristics of the user. Based on the genetic characteristics, a learning plan optimal for the user is created. In this way, a learning plan based on the genetic characteristics of the user can be created by analyzing the genetic information. A part or all of the above-mentioned processing in the plan creation unit can be performed, for example, using AI, or can be performed without using AI. For example, the plan creation unit can input the genetic information data of the user to the generation AI and cause the generation AI to generate a learning plan.

[0045] The plan creation unit can set the progress speed of the learning plan individually based on the user's learning history. The plan creation unit, for example, analyzes data such as the user's past study time, study content, and grades, and sets the progress speed of the learning plan individually. For example, if the user has studied at a fast pace in the past, the progress speed of the learning plan is set to be fast. If the user has studied slowly in the past, the progress speed of the learning plan is set to be slow. In this way, by setting the progress speed based on the learning history, it is possible to proceed with learning at an optimal pace for the user. Part or all of the above-mentioned processing in the plan creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan creation unit can input the user's learning history data to the generation AI and cause the generation AI to set the progress speed.

[0046] The plan creation unit can customize the contents of the learning plan according to the learning style of the user. The plan creation unit customizes the contents of the learning plan according to the learning style, such as visual, auditory, and tactile. For example, a visual learner is provided with a learning plan that uses a lot of diagrams and graphs. An auditory learner is provided with a learning plan that uses a lot of audio commentary and podcasts. A tactile learner is provided with a learning plan that uses a lot of experiments and hands-on activities. This makes it possible to improve the learning effect by providing content according to the learning style. A part or all of the above-mentioned processing in the plan creation unit may be performed, for example, using AI or may be performed without using AI. For example, the plan creation unit can input the user's learning style data into the generation AI and cause the generation AI to customize the learning plan.

[0047] The plan creation unit can propose an appropriate study time period based on the user's life rhythm. The plan creation unit, for example, analyzes data such as the user's sleep pattern and activity time, and proposes an optimal study time period. For example, if the user is a morning person, the study plan is set for the morning time period. If the user is a night person, the study plan is set for the evening time period. This makes it possible to maximize the effect of learning by proposing an optimal study time period based on the user's life rhythm. Some or all of the above-mentioned processing in the plan creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan creation unit can input the user's life rhythm data to the generation AI and cause the generation AI to execute the proposal of a study time period.

[0048] The plan creation unit can provide additional learning resources related to the learning plan based on the user's interests. The plan creation unit analyzes data such as the user's hobbies, past learning history, and survey results, and provides additional learning resources related to the learning plan. For example, articles and videos related to topics in which the user is interested are added to the learning plan. Lectures by experts in the field in which the user is interested are added to the learning plan. This can maximize the effectiveness of learning by providing additional learning resources based on the user's interests. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's interest data to the generation AI and cause the generation AI to provide additional learning resources.

[0049] The identification unit can analyze the answer pattern of the user and evaluate the level of understanding of a specific problem. The identification unit can analyze data such as the user's correct answer rate, answer time, and answer consistency to evaluate the level of understanding of a specific problem. For example, if the user consistently answers a specific problem correctly, the level of understanding of the problem is evaluated as high. If the user consistently answers a specific problem incorrectly, the level of understanding of the problem is evaluated as low. In this way, the user's level of understanding can be accurately evaluated by analyzing the answer pattern. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's answer data to the generation AI and cause the generation AI to execute the evaluation of the level of understanding.

[0050] The identification unit can refer to the user's learning history and track the improvement status of past problems and weaknesses. The identification unit, for example, refers to data such as the user's past study time, learning content, and grades, and tracks the improvement status of past problems and weaknesses. For example, it tracks changes in the correct answer rate for questions that the user had difficulty with in the past. It tracks the improvement status of weaknesses that the user was pointed out in the past. In this way, by referring to the learning history, it is possible to accurately grasp the improvement status of past problems and weaknesses. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's learning history data to the generation AI and cause the generation AI to track the improvement status.

[0051] The identification unit can change the method of presenting the identification result according to the user's learning environment. The identification unit analyzes data such as the physical environment (lighting, sound) and the digital environment (devices used, software) and changes the method of presenting the identification result. For example, in a quiet place, detailed feedback is provided in text. In a noisy place, brief feedback is provided in audio. This makes it possible to effectively convey the identification result by changing the presentation method according to the learning environment. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's learning environment data to the generation AI and cause the generation AI to change the presentation method.

[0052] The identification unit can determine the priority of the identification results based on the user's learning progress. The identification unit, for example, analyzes data such as the user's learning progress, achievement level, and time management, and determines the priority of the identification results. For example, if the user's progress is delayed, important identification results are displayed with priority. If the user's progress is good, detailed identification results are displayed. In this way, important identification results can be conveyed with priority by determining the priority based on the learning progress. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's learning progress data to the generation AI and cause the generation AI to determine the priority.

[0053] The providing unit can dynamically change the level of detail of the commentary according to the user's level of understanding. The providing unit analyzes data such as test results, self-assessment, and teacher evaluation, and dynamically changes the level of detail of the commentary according to the user's level of understanding. For example, if the user has a high level of understanding, a concise explanation is provided. If the user has a low level of understanding, a detailed explanation is provided. This makes it possible to support effective learning by changing the level of detail of the commentary according to the level of understanding. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's level of understanding data to the generating AI and cause the generating AI to change the level of detail of the commentary.

[0054] The providing unit can provide relevant examples and case studies based on the user's learning theme. The providing unit, for example, analyzes data such as actual case studies, success stories, and failure stories, and provides relevant examples and case studies based on the user's learning theme. For example, it provides examples related to the theme the user is studying. It provides case studies related to the theme the user is studying. This allows the user to improve their understanding by providing examples and case studies based on the learning theme. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's learning theme data into the generating AI and cause the generating AI to provide examples and case studies.

[0055] The providing unit can re-ask questions that the user got wrong in the past based on the user's learning history. The providing unit, for example, analyzes data such as the user's past study time, study content, and grades, and re-asks questions that the user got wrong in the past. For example, the providing unit re-asks questions that the user got wrong in the past. A specific question is re-asked from the user's learning history. This allows the user to improve their understanding by re-asking questions that the user got wrong in the past based on the learning history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's learning history data into the generating AI and cause the generating AI to select questions to be re-asked.

[0056] The providing unit can adjust the frequency of the explanations and mock tests according to the user's learning progress. The providing unit, for example, analyzes data such as the user's learning progress, achievement level, and time management, and adjusts the frequency of the explanations and mock tests. For example, if the user's learning progress is delayed, the frequency of the explanations and mock tests is increased. If the user's learning progress is good, the frequency of the explanations and mock tests is decreased. This makes it possible to support effective learning by adjusting the frequency of the explanations and mock tests according to the learning progress. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's learning progress data to the generating AI and cause the generating AI to adjust the frequency of the explanations and mock tests.

[0057] The tracking unit can analyze the user's learning data in real time and generate an alert when an abnormality is detected. For example, the tracking unit analyzes the user's learning data in real time and generates an alert when progress is delayed. The tracking unit analyzes the user's learning data in real time and generates an alert when the correct answer rate for a specific question is low. This allows for rapid response by analyzing the learning data in real time and detecting an abnormality. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's learning data to the generation AI and cause the generation AI to detect an abnormality.

[0058] The tracking unit can refer to the user's learning history and analyze the progress trend. The tracking unit can refer to data such as the user's past study time, learning content, and grades, and analyze the progress trend. For example, the tracking unit can refer to the user's learning history and display the progress trend in a graph. The tracking unit can refer to the user's learning history and analyze the progress trend for a specific subject or topic. This allows effective progress management by analyzing the progress trend with reference to the learning history. Some or all of the above-mentioned processing in the tracking unit can be performed, for example, using AI, or can be performed without using AI. For example, the tracking unit can input the user's learning history data to the generation AI and cause the generation AI to perform an analysis of the progress trend.

[0059] The tracking unit can change the progress feedback method according to the user's learning environment. The tracking unit analyzes data such as the physical environment (lighting, sound) and the digital environment (devices used, software) and changes the progress feedback method. For example, in a quiet place, detailed feedback is provided in text. In a noisy place, brief feedback is provided by voice. This enables effective progress management by changing the feedback method according to the learning environment. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's learning environment data to the generation AI and cause the generation AI to change the feedback method.

[0060] The tracking unit can determine the priority of progress based on the user's learning goals. The tracking unit, for example, analyzes data such as the user's short-term goals, long-term goals, and achievement criteria, and determines the priority of progress. For example, important progress is displayed with priority based on the user's learning goals. Detailed progress is displayed based on the user's learning goals. This enables effective progress management by determining the priority based on the learning goals. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's learning goal data to the generation AI and cause the generation AI to determine the priority.

[0061] The motivation unit can propose individual motivation improvement measures based on the user's learning history. The motivation unit, for example, analyzes data such as the user's past study time, learning content, and grades, and proposes individual motivation improvement measures. For example, the motivation unit refers to the user's learning history and proposes motivation improvement measures that have been effective in the past. The motivation unit refers to the user's learning history and proposes motivation improvement measures for specific subjects or topics. This enables effective motivation management by proposing individual motivation improvement measures based on the learning history. Some or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI, or may be performed without using AI. For example, the motivation unit can input the user's learning history data to the generation AI and have the generation AI execute the proposal of motivation improvement measures.

[0062] The motivation unit can evaluate the effectiveness of the motivation improvement measures according to the user's learning progress. The motivation unit, for example, analyzes data such as the user's learning progress, achievement level, and time management to evaluate the effectiveness of the motivation improvement measures. For example, the motivation unit refers to the user's learning progress and quantifies and evaluates the effectiveness of the motivation improvement measures. The motivation unit refers to the user's learning progress and evaluates the effectiveness of a specific motivation improvement measure. This enables effective motivation management by evaluating the effectiveness of the motivation improvement measures according to the learning progress. Some or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI or may be performed without using AI. For example, the motivation unit can input the user's learning progress data into the generation AI and cause the generation AI to execute an evaluation of the effectiveness of the motivation improvement measures.

[0063] The motivation unit can change the motivation improvement measures according to the user's learning environment. The motivation unit analyzes data such as the physical environment (lighting, sound) and the digital environment (devices used, software) and changes the motivation improvement measures. For example, in a quiet place, it provides a motivation improvement measure that allows the user to relax. In a noisy place, it provides a motivation improvement measure that allows the user to concentrate. This enables effective motivation management by changing the motivation improvement measures according to the learning environment. Some or all of the above-mentioned processing in the motivation unit may be performed using, for example, AI, or may be performed without using AI. For example, the motivation unit can input the user's learning environment data to the generation AI and cause the generation AI to execute the change of the motivation improvement measures.

[0064] The motivation unit can customize the content of the motivation improvement measures based on the user's learning goals. The motivation unit, for example, analyzes data such as the user's short-term goals, long-term goals, and achievement standards, and customizes the content of the motivation improvement measures. For example, a specific motivation improvement measure is provided based on the user's learning goals. A challenging motivation improvement measure is provided based on the user's learning goals. This enables effective motivation management by customizing the content of the motivation improvement measures based on the learning goals. A part or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI, or may be performed without using AI. For example, the motivation unit can input the user's learning goal data into the generation AI and cause the generation AI to customize the motivation improvement measures.

[0065] The motivation unit can evaluate the effectiveness of the motivation improvement measures according to the user's learning progress. The motivation unit, for example, analyzes data such as the user's learning progress, achievement level, and time management to evaluate the effectiveness of the motivation improvement measures. For example, the motivation unit refers to the user's learning progress and quantifies and evaluates the effectiveness of the motivation improvement measures. The motivation unit refers to the user's learning progress and evaluates the effectiveness of a specific motivation improvement measure. This enables effective motivation management by evaluating the effectiveness of the motivation improvement measures according to the learning progress. Some or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI or may be performed without using AI. For example, the motivation unit can input the user's learning progress data into the generation AI and cause the generation AI to execute an evaluation of the effectiveness of the motivation improvement measures.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The plan creation unit can analyze the user's learning history, as well as the user's life rhythm and daily activity data, to suggest optimal study times. For example, taking into account the user's sleep patterns and exercise habits, study times are set during times when concentration is high. This makes it possible to provide a study plan that matches the user's life rhythm and maximize the learning effect. In addition, by avoiding times when fatigue is high based on the user's activity data, it is possible to support efficient study. Furthermore, it is possible to suggest break times and timings for refreshing based on the user's life rhythm.

[0068] The identification unit can identify problems and weaknesses based on the user's learning history as well as the user's interests and hobbies. For example, if the user is interested in a particular topic, many questions related to that topic can be presented to deepen the user's understanding. In addition, by incorporating content related to the user's hobbies into the study plan, the user's motivation to study can be improved. This allows for individualized learning support based on the user's interests. Furthermore, by providing examples and case studies related to the user's hobbies and interests, the user can deepen their understanding of the study content.

[0069] The tracking unit can monitor not only the user's learning progress, but also the user's health condition in real time, generating an alert if an abnormality is detected. For example, it can monitor fluctuations in the user's heart rate and body temperature, and generate an alert encouraging the user to take a break if an abnormality is detected. This makes it possible to provide learning support that takes the user's health condition into consideration. It can also adjust the learning plan based on the user's health data. For example, if fatigue is accumulating, a plan to reduce the learning load is proposed.

[0070] The plan creation unit can set the progress speed of the learning plan individually based on the user's learning history. For example, data such as the user's past study time, study content, and grades is analyzed, and the progress speed of the learning plan is individually set. For example, if the user has studied at a fast pace in the past, the progress speed of the learning plan is set faster. If the user has studied slowly in the past, the progress speed of the learning plan is set slower. In this way, by setting the progress speed based on the learning history, it is possible to proceed with learning at an optimal pace for the user.

[0071] The identification unit can refer to the user's learning history and track the progress of improvements in past problems and weak points. For example, the identification unit refers to data such as the user's past study time, study content, and grades, and tracks the progress of improvements in past problems and weak points. For example, the identification unit tracks changes in the rate of correct answers to questions that the user had difficulty with in the past. The identification unit tracks the progress of improvements in weak points that were pointed out to the user in the past. In this way, by referring to the learning history, the identification unit can accurately grasp the progress of improvements in past problems and weak points.

[0072] The providing unit can provide a live commentary by an AI instructor generated in real time based on the user's learning theme. For example, the live commentary by the AI ​​instructor is provided in the form of real-time generation, pre-recording, interactive commentary, etc. For example, in the real-time generation, an AI generates a commentary in real time based on the user's learning theme. In the pre-recording, a commentary that is pre-recorded based on the user's learning theme is provided. In the interactive commentary, an AI interactively provides a commentary based on the user's learning theme. In this way, the user's understanding can be deepened by providing a live commentary by an AI instructor generated in real time.

[0073] The motivation unit can provide information about an AI pet that grows according to the user's learning progress. For example, the AI ​​pet that grows according to the user's learning progress can be used to improve the user's motivation to learn. For example, the AI ​​pet grows according to the user's learning progress and has an interaction function. As the user progresses with their learning, the AI ​​pet grows and acquires new functions and actions. In this way, by providing information about the growing AI pet, the user's motivation to learn can be improved.

[0074] The process flow of the first embodiment will be briefly described below.

[0075] Step 1: The plan creation unit analyzes the user's learning history and goals, and creates a learning plan tailored to the user. The user's learning history includes past study time, study content, grades, etc. The plan creation unit collects data such as past test results, study time, and study content, and the AI ​​analyzes it. This makes it possible to understand the user's strengths and weaknesses and create an individual learning plan. Step 2: The identification unit identifies the user's problems and weaknesses in the learning process using the learning plan created by the plan creation unit. For example, if the correct answer rate for a particular problem is low, the identification unit identifies that problem as the user's weakness. Step 3: The providing unit provides the user with explanations and mock exams based on the problems and weaknesses identified by the identifying unit. For example, the providing unit provides detailed explanations for problems that the user is weak at, deepening the user's understanding. In addition, the mock exams allow the user to prepare for the actual exam. Step 4: The tracking unit tracks the user's progress in the learning plan created by the plan creation unit. For example, the tracking unit monitors the learning progress in real time and generates an alert if an abnormality is detected. This makes it easier for the user to grasp the learning progress. Step 5: The motivation unit improves the motivation based on the progress tracked by the tracking unit. The motivation unit improves the user's motivation to learn, for example, by using gamification technology. In addition, the motivation unit can maintain the user's motivation by sending encouraging messages customized according to the learning progress.

[0076] (Example 2) The AI ​​tutor AI assistant system for education and learning support according to the embodiment of the present invention is a system that analyzes a user's learning history and goals and creates a learning plan according to the user. This system analyzes the user's learning history and goals and creates a learning plan according to the user. Next, in the process of proceeding with learning based on the created learning plan, the system identifies the user's problems and weaknesses. Based on the identified problems and weaknesses, the system provides the user with explanations and mock exams. In addition, the system tracks the user's progress in the learning plan and provides measures to improve motivation based on the progress. This maximizes the user's learning effect and supports efficient learning. For example, the system analyzes the user's learning history and goals. Data such as past test results, study time, and learning content are collected and analyzed by AI. This makes it possible to grasp the user's strengths and weaknesses and create an individual learning plan. Next, in the process of proceeding with learning based on the created learning plan, the system identifies the user's problems and weaknesses. If the correct answer rate for a specific problem is low, the system identifies that problem as the user's weakness. Based on the identified problems and weaknesses, the system provides the user with explanations and mock exams. For problems that the user is weak at, detailed explanations are provided to deepen understanding. In addition, mock exams can help users prepare for the actual exam. In addition, the system tracks the user's progress in the learning plan. The system monitors the learning progress in real time and generates an alert if an abnormality is detected. This makes it easier for users to grasp the progress of their learning. Based on the progress, measures to improve motivation are provided. Gamification technology is used to improve the user's motivation to learn. In addition, customized encouraging messages can be sent according to the learning progress to maintain the user's motivation. In this way, the AI ​​tutor AI assistant has functions such as creating individual learning plans, identifying problems and weaknesses, providing explanations and mock exams, tracking progress, and measures to improve motivation, and can comprehensively support the user's learning. As a result, the AI ​​tutor AI assistant system for education and learning support can maximize the user's learning effect and support efficient learning.

[0077] An AI tutor AI assistant system for education and learning support according to an embodiment includes a plan creation unit, a discrimination unit, a provision unit, a tracking unit, and a motivation unit. The plan creation unit analyzes the user's learning history and goals, and creates a learning plan according to the user. The user's learning history includes, but is not limited to, past study time, study content, and grades. The plan creation unit collects data such as past test results, study time, and study content, and the AI ​​analyzes it. This makes it possible to grasp the user's strengths and weaknesses and create an individual learning plan. The discrimination unit identifies the user's problems and weaknesses in the learning process using the study plan created by the plan creation unit. For example, if the correct answer rate for a specific problem is low, the discrimination unit identifies the problem as the user's weakness. The provision unit provides the user with explanations and mock exams based on the problems and weaknesses identified by the discrimination unit. The provision unit provides detailed explanations for problems that the user is weak at, for example, to deepen understanding. In addition, the mock exam allows the user to prepare for the actual exam. The tracking unit tracks the user's progress in the learning plan created by the plan creation unit. For example, the tracking unit monitors the learning progress in real time and generates an alert when an abnormality is detected. This makes it easier for the user to grasp the learning progress. The motivation unit improves motivation based on the progress tracked by the tracking unit. The motivation unit improves the user's motivation to learn, for example, by using gamification technology. In addition, the motivation unit can maintain the user's motivation by sending encouraging messages customized according to the learning progress. As a result, the AI ​​tutor AI assistant system for education and learning support according to the embodiment can maximize the user's learning effect and support efficient learning.

[0078] The plan creation unit analyzes the user's learning history and goals, and creates a study plan according to the user. The user's learning history includes, but is not limited to, past study time, study content, and grades. The plan creation unit collects data such as past test results, study time, and study content, and the AI ​​analyzes it. Specifically, the AI ​​analyzes the user's past study data using a machine learning algorithm to grasp the user's study patterns and tendencies. For example, if the user has obtained high scores in a specific subject, the AI ​​determines that the subject is a good subject, and conversely, if the user has obtained low scores, the AI ​​determines that the subject is a weak subject. In addition, by analyzing the study time data, it is possible to identify which time of day the user can study most efficiently. This allows the plan creation unit to create an individual study plan that takes into account the user's strong and weak areas and the optimal study time period. Furthermore, the plan creation unit sets short-term and long-term goals according to the user's goals, and provides a study plan based on them. For example, if the user's goal is to pass a specific exam, the AI ​​creates a plan that focuses on learning the knowledge and skills required for the exam. In addition, the plan creation unit receives feedback from the user and updates the study plan at any time, thereby providing an optimal learning environment. This enables the plan creation unit to maximize the effect of the user's learning and support efficient learning.

[0079] The identification unit identifies the user's problems and weaknesses in the learning process with the learning plan created by the plan creation unit. For example, if the correct answer rate for a specific problem is low, the identification unit identifies that problem as the user's weak point. Specifically, the AI ​​analyzes the user's answer data and identifies the user's weak points based on indicators such as the correct answer rate and answer time. For example, if the user has a low correct answer rate for a specific math problem, it determines that the problem is the user's weak point. In addition, if the answer time is long, it can also be identified as the user's insufficient understanding of the problem. Furthermore, the identification unit can comprehensively evaluate weaknesses in a specific field or topic based on the user's learning history and past grades. This allows the identification unit to accurately identify problems and weaknesses in the user's learning process and use them to improve individual learning plans. Furthermore, the identification unit can also suggest the most suitable learning method taking into account the user's learning style and learning pace. For example, if the user prefers visual learning, it provides teaching materials that make extensive use of visual aids. In addition, the identification unit can receive feedback from the user to evaluate the effectiveness of the learning plan and modify the plan as necessary. This enables the recognition unit to maximize the learning effect of the user and support efficient learning.

[0080] The providing unit provides the user with explanations and mock exams based on the problems and weaknesses identified by the identifying unit. For example, the providing unit provides detailed explanations for problems that the user is weak at, deepening the user's understanding. In addition, the mock exam allows the user to prepare for the actual exam. Specifically, the providing unit uses AI to generate explanatory videos and texts specialized for the user's weak points and provides them to the user. For example, if the user does not fully understand a particular math problem, the providing unit provides a detailed explanatory video for that problem and provides a step-by-step explanation. In addition, the providing unit provides mock exams at appropriate times according to the user's learning progress, allowing the user to prepare for the actual exam. The mock exams are created based on the actual exam format and are designed to allow the user to become familiar with the exam environment. In addition, the providing unit analyzes the user's answer data and provides feedback based on indicators such as the accuracy of the answers and the answer time. This allows the user to reaffirm his or her weak points and understand the areas for improvement. In addition, the providing unit can provide customized teaching materials according to the user's learning style and learning pace. For example, if the user prefers visual learning, the providing unit provides learning materials that make extensive use of visual aids, thereby maximizing the user's learning effect and supporting efficient learning.

[0081] The tracking unit tracks the progress of the user in the learning plan created by the plan creation unit. For example, the tracking unit monitors the learning progress in real time and generates an alert when an abnormality is detected. Specifically, the tracking unit collects the user's learning data in real time and visualizes the learning progress. For example, the tracking unit displays the current progress in graphs and charts for the learning goals set by the user, allowing the user to grasp his / her learning situation at a glance. The tracking unit also monitors the user's learning pace and learning time, and generates an alert when an abnormality is detected. For example, if the user significantly exceeds the scheduled learning time or the learning pace is slow, an alert is generated and the user is notified. This allows the user to grasp his / her learning situation in real time and adjust the learning plan as necessary. Furthermore, the tracking unit can analyze long-term learning trends based on the user's learning history and use them to improve future learning plans. For example, the tracking unit analyzes past learning data to grasp fluctuations in learning effects at specific times and use them to optimize future learning plans. The tracking unit can also continuously improve the accuracy and functionality of the tracking system based on user feedback. This enables the tracking unit to maximize the learning effect of the user and support efficient learning.

[0082] The motivation unit improves the motivation based on the progress tracked by the tracking unit. The motivation unit improves the user's motivation to learn, for example, by using gamification technology. In addition, the motivation unit can maintain the user's motivation by sending a customized encouraging message according to the learning progress. Specifically, the motivation unit grants points and badges according to the user's learning progress, allowing the user to feel a sense of accomplishment. For example, when a specific learning goal is achieved, points are granted and the user can use the points to earn rewards. In addition, the motivation unit transmits a customized encouraging message according to the user's learning progress. For example, when the user achieves a learning goal, an encouraging message is sent to increase the user's motivation. In addition, the motivation unit can also propose optimal motivation improvement measures in consideration of the user's learning history and learning style. For example, if the user prefers visual learning, an encouraging message that makes heavy use of visual aids is provided. In addition, the motivation unit can receive feedback from the user, evaluate the effectiveness of the motivation improvement measures, and improve them as necessary. This allows the motivation unit to maximize the user's learning effect and support efficient learning.

[0083] The plan creation unit can create a learning plan suitable for the user using a machine learning model. The plan creation unit creates a learning plan suitable for the user using a machine learning model such as a neural network, a decision tree, or a support vector machine. For example, a neural network has the ability to learn a large amount of data and recognize complex patterns. A decision tree hierarchically divides data and performs classification and regression. A support vector machine finds a boundary for classifying data in a high-dimensional space. This allows the use of a machine learning model to create an optimal learning plan for the user. A part or all of the above-mentioned processing in the plan creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan creation unit can input the user's learning history data to the generation AI and cause the generation AI to generate a learning plan.

[0084] The providing unit can provide a commentary customized according to the learning style of the user. The providing unit provides a commentary customized according to a learning style such as visual, auditory, or tactile. For example, a visual user is provided with a commentary that uses a lot of diagrams and graphs. An auditory user is provided with a commentary that uses a lot of audio commentary and podcasts. A tactile user is provided with a commentary that uses a lot of experiments and hands-on activities. This allows the user to improve their understanding by providing a commentary that matches their learning style. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's learning style data into the generating AI and cause the generating AI to generate a customized commentary.

[0085] The tracking unit can monitor the progress of the user in real time and generate an alert when a specific abnormality is detected. The tracking unit, for example, monitors the progress of learning in real time and generates an alert when an abnormality is detected. For example, it detects abnormalities such as stagnation of learning and a sudden drop in grades. This makes it possible to support the user's learning by monitoring the progress in real time and generating an alert when an abnormality is detected. Some or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the tracking unit can input the user's learning data to the generation AI and cause the generation AI to detect an abnormality.

[0086] The motivation unit can increase the user's motivation to learn using gamification technology. The motivation unit can increase the user's motivation to learn using gamification technology such as a point system, leveling up, and badges. For example, the point system is a mechanism that allows a user to earn points each time they progress in their learning. Leveling up is a mechanism that allows a user to increase their level when they earn a certain number of points. A badge is a digital badge that can be earned when a specific condition is met. As a result, the use of gamification technology can increase the user's motivation to learn. Some or all of the above-mentioned processing in the motivation unit may be performed using, for example, AI, or may be performed without using AI. For example, the motivation unit can input the user's learning progress data into the generation AI and cause the generation AI to generate gamification elements.

[0087] The motivation unit can send a customized encouraging message according to the user's learning progress. The motivation unit sends the encouraging message in the form of, for example, a text message, a voice message, a video message, or the like. For example, the text message includes words of encouragement customized according to the user's learning progress. The voice message provides a customized voice encouragement according to the user's learning progress. The video message provides a customized video encouragement according to the user's learning progress. This makes it possible to maintain the motivation of the user by sending an encouraging message according to the learning progress. A part or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI or may be performed without using AI. For example, the motivation unit can input the user's learning progress data to the generation AI and cause the generation AI to generate the encouraging message.

[0088] The plan creation unit can use the biometric data from the wearable device to analyze the user's emotional state, measure the stress level, and create a study plan based on the result. The plan creation unit acquires biometric data such as heart rate, electrodermal activity, and body temperature from the wearable device and analyzes the user's emotional state. For example, the plan creation unit analyzes the fluctuation of the heart rate to measure the user's stress level. The plan creation unit analyzes the electrodermal activity to measure the user's tension state. The plan creation unit analyzes the body temperature fluctuation to measure the user's relaxed state. In this way, by using the biometric data from the wearable device, a study plan according to the user's stress level can be created. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plan creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan creation unit can input the biometric data acquired from the wearable device to the generation AI and cause the generation AI to execute the analysis of the emotional state.

[0089] The motivation unit allows the AI ​​character to interactively provide a cheering message according to the user's learning progress. The motivation unit provides a cheering message according to the user's learning progress using an AI character having functions such as voice dialogue, facial expression change, and movement. For example, the voice dialogue function is used to provide words of encouragement according to the user's learning progress. The facial expression change function is used to cheer the user with a facial expression according to the user's learning progress. The movement function is used to cheer the user with a movement according to the user's learning progress. This allows the AI ​​character to interactively provide a cheering message, thereby improving the user's motivation. Some or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI, or may be performed without using AI. For example, the motivation unit can input the user's learning progress data to the generation AI and cause the generation AI to generate a cheering message.

[0090] The providing unit can provide a live commentary by an AI lecturer generated in real time based on the user's learning theme. The providing unit provides a live commentary by an AI lecturer in the form of, for example, real-time generation, pre-recording, interactive commentary, etc. For example, in the real-time generation, an AI generates a commentary in real time based on the user's learning theme. In the pre-recording, a commentary recorded in advance is provided based on the user's learning theme. In the interactive commentary, an AI interactively provides a commentary based on the user's learning theme. This allows the user's understanding to be deepened by providing a live commentary by an AI lecturer generated in real time. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's learning theme data to the generating AI and cause the generating AI to generate a live commentary.

[0091] The motivation unit can provide information on the AI ​​pet that grows according to the user's learning progress. The motivation unit can, for example, improve the user's motivation to learn by using an AI pet that grows according to the learning progress. For example, the AI ​​pet grows according to the user's learning progress and has an interaction function. As the user progresses with learning, the AI ​​pet grows and acquires new functions and actions. In this way, by providing information on the growing AI pet, the user's motivation to learn can be improved. Some or all of the above-mentioned processing in the motivation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the motivation unit can input the user's learning progress data to the generation AI and cause the generation AI to execute the growth of the AI ​​pet.

[0092] The plan creation unit can analyze the genetic information of the user and create a learning plan based on the results. The plan creation unit can analyze genetic information such as DNA sequence and genetic characteristic evaluation, and create a learning plan based on the genetic characteristics of the user. For example, the plan creation unit can analyze the DNA sequence and evaluate the genetic characteristics of the user. Based on the genetic characteristics, a learning plan optimal for the user is created. In this way, a learning plan based on the genetic characteristics of the user can be created by analyzing the genetic information. A part or all of the above-mentioned processing in the plan creation unit can be performed, for example, using AI, or can be performed without using AI. For example, the plan creation unit can input the genetic information data of the user to the generation AI and cause the generation AI to generate a learning plan.

[0093] The plan creation unit can estimate the user's emotions and adjust the difficulty of the study plan based on the estimated user's emotions. The plan creation unit estimates the user's emotions using technologies such as facial expression analysis and voice analysis. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotions. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotions. This makes it possible to adjust the difficulty of the study plan based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plan creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan creation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the difficulty of the study plan.

[0094] The plan creation unit can set the progress speed of the learning plan individually based on the user's learning history. The plan creation unit, for example, analyzes data such as the user's past study time, study content, and grades, and sets the progress speed of the learning plan individually. For example, if the user has studied at a fast pace in the past, the progress speed of the learning plan is set to be fast. If the user has studied slowly in the past, the progress speed of the learning plan is set to be slow. In this way, by setting the progress speed based on the learning history, it is possible to proceed with learning at an optimal pace for the user. Part or all of the above-mentioned processing in the plan creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan creation unit can input the user's learning history data to the generation AI and cause the generation AI to set the progress speed.

[0095] The plan creation unit can customize the contents of the learning plan according to the learning style of the user. The plan creation unit customizes the contents of the learning plan according to the learning style, such as visual, auditory, and tactile. For example, a visual learner is provided with a learning plan that uses a lot of diagrams and graphs. An auditory learner is provided with a learning plan that uses a lot of audio commentary and podcasts. A tactile learner is provided with a learning plan that uses a lot of experiments and hands-on activities. This makes it possible to improve the learning effect by providing content according to the learning style. A part or all of the above-mentioned processing in the plan creation unit may be performed, for example, using AI or may be performed without using AI. For example, the plan creation unit can input the user's learning style data into the generation AI and cause the generation AI to customize the learning plan.

[0096] The plan creation unit can estimate the user's emotions and determine the priority of the learning plan based on the estimated user's emotions. The plan creation unit estimates the user's emotions using technologies such as facial expression analysis and voice analysis. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotions. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotions. This makes it possible to determine the priority of the learning plan based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plan creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan creation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the learning plan.

[0097] The plan creation unit can propose an appropriate study time period based on the user's life rhythm. The plan creation unit, for example, analyzes data such as the user's sleep pattern and activity time, and proposes an optimal study time period. For example, if the user is a morning person, the study plan is set for the morning time period. If the user is a night person, the study plan is set for the evening time period. This makes it possible to maximize the effect of learning by proposing an optimal study time period based on the user's life rhythm. Some or all of the above-mentioned processing in the plan creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan creation unit can input the user's life rhythm data to the generation AI and cause the generation AI to execute the proposal of a study time period.

[0098] The plan creation unit can provide additional learning resources related to the learning plan based on the user's interests. The plan creation unit analyzes data such as the user's hobbies, past learning history, and survey results, and provides additional learning resources related to the learning plan. For example, articles and videos related to topics in which the user is interested are added to the learning plan. Lectures by experts in the field in which the user is interested are added to the learning plan. This can maximize the effectiveness of learning by providing additional learning resources based on the user's interests. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's interest data to the generation AI and cause the generation AI to provide additional learning resources.

[0099] The identification unit can estimate the user's emotion and adjust the feedback method for the problem or weakness based on the estimated user's emotion. The identification unit estimates the user's emotion using techniques such as facial expression analysis and voice analysis. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotion. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotion. This makes it possible to adjust the feedback method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the feedback method.

[0100] The identification unit can analyze the answer pattern of the user and evaluate the level of understanding of a specific problem. The identification unit can analyze data such as the user's correct answer rate, answer time, and answer consistency to evaluate the level of understanding of a specific problem. For example, if the user consistently answers a specific problem correctly, the level of understanding of the problem is evaluated as high. If the user consistently answers a specific problem incorrectly, the level of understanding of the problem is evaluated as low. In this way, the user's level of understanding can be accurately evaluated by analyzing the answer pattern. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's answer data to the generation AI and cause the generation AI to execute the evaluation of the level of understanding.

[0101] The identification unit can refer to the user's learning history and track the improvement status of past problems and weaknesses. The identification unit, for example, refers to data such as the user's past study time, learning content, and grades, and tracks the improvement status of past problems and weaknesses. For example, it tracks changes in the correct answer rate for questions that the user had difficulty with in the past. It tracks the improvement status of weaknesses that the user was pointed out in the past. In this way, by referring to the learning history, it is possible to accurately grasp the improvement status of past problems and weaknesses. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's learning history data to the generation AI and cause the generation AI to track the improvement status.

[0102] The identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user's emotion. The identification unit estimates the user's emotion using technologies such as facial expression analysis and voice analysis. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotion. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotion. This makes it possible to adjust the display method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0103] The identification unit can change the method of presenting the identification result according to the user's learning environment. The identification unit analyzes data such as the physical environment (lighting, sound) and the digital environment (devices used, software) and changes the method of presenting the identification result. For example, in a quiet place, detailed feedback is provided in text. In a noisy place, brief feedback is provided in audio. This makes it possible to effectively convey the identification result by changing the presentation method according to the learning environment. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or may be performed without using AI. For example, the identification unit can input the user's learning environment data to the generation AI and cause the generation AI to change the presentation method.

[0104] The identification unit can determine the priority of the identification results based on the user's learning progress. The identification unit, for example, analyzes data such as the user's learning progress, achievement level, and time management, and determines the priority of the identification results. For example, if the user's progress is delayed, important identification results are displayed with priority. If the user's progress is good, detailed identification results are displayed. In this way, important identification results can be conveyed with priority by determining the priority based on the learning progress. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's learning progress data to the generation AI and cause the generation AI to determine the priority.

[0105] The providing unit can estimate the user's emotion and adjust the tone and style of the commentary based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, facial expression analysis, voice analysis, and other technologies. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotion. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotion. This makes it possible to adjust the tone and style of the commentary based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the tone and style of the commentary.

[0106] The providing unit can dynamically change the level of detail of the commentary according to the user's level of understanding. The providing unit analyzes data such as test results, self-assessment, and teacher evaluation, and dynamically changes the level of detail of the commentary according to the user's level of understanding. For example, if the user has a high level of understanding, a concise explanation is provided. If the user has a low level of understanding, a detailed explanation is provided. This makes it possible to support effective learning by changing the level of detail of the commentary according to the level of understanding. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's level of understanding data to the generating AI and cause the generating AI to change the level of detail of the commentary.

[0107] The providing unit can provide relevant examples and case studies based on the user's learning theme. The providing unit, for example, analyzes data such as actual case studies, success stories, and failure stories, and provides relevant examples and case studies based on the user's learning theme. For example, it provides examples related to the theme the user is studying. It provides case studies related to the theme the user is studying. This allows the user to improve their understanding by providing examples and case studies based on the learning theme. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's learning theme data into the generating AI and cause the generating AI to provide examples and case studies.

[0108] The providing unit can estimate the user's emotion and adjust the difficulty of the mock test based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, facial expression analysis, voice analysis, and other technologies. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotion. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotion. This allows the difficulty of the mock test to be adjusted based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the difficulty of the mock test.

[0109] The providing unit can re-ask questions that the user got wrong in the past based on the user's learning history. The providing unit, for example, analyzes data such as the user's past study time, study content, and grades, and re-asks questions that the user got wrong in the past. For example, the providing unit re-asks questions that the user got wrong in the past. A specific question is re-asked from the user's learning history. This allows the user to improve their understanding by re-asking questions that the user got wrong in the past based on the learning history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's learning history data into the generating AI and cause the generating AI to select questions to be re-asked.

[0110] The providing unit can adjust the frequency of the explanations and mock tests according to the user's learning progress. The providing unit, for example, analyzes data such as the user's learning progress, achievement level, and time management, and adjusts the frequency of the explanations and mock tests. For example, if the user's learning progress is delayed, the frequency of the explanations and mock tests is increased. If the user's learning progress is good, the frequency of the explanations and mock tests is decreased. This makes it possible to support effective learning by adjusting the frequency of the explanations and mock tests according to the learning progress. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's learning progress data to the generating AI and cause the generating AI to adjust the frequency of the explanations and mock tests.

[0111] The tracking unit can estimate the user's emotion and adjust the progress feedback method based on the estimated user's emotion. The tracking unit estimates the user's emotion using technologies such as facial expression analysis and voice analysis. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotion. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotion. This makes it possible to adjust the progress feedback method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the feedback method.

[0112] The tracking unit can analyze the user's learning data in real time and generate an alert when an abnormality is detected. For example, the tracking unit analyzes the user's learning data in real time and generates an alert when progress is delayed. The tracking unit analyzes the user's learning data in real time and generates an alert when the correct answer rate for a specific question is low. This allows for rapid response by analyzing the learning data in real time and detecting an abnormality. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's learning data to the generation AI and cause the generation AI to detect an abnormality.

[0113] The tracking unit can refer to the user's learning history and analyze the progress trend. The tracking unit can refer to data such as the user's past study time, learning content, and grades, and analyze the progress trend. For example, the tracking unit can refer to the user's learning history and display the progress trend in a graph. The tracking unit can refer to the user's learning history and analyze the progress trend for a specific subject or topic. This allows effective progress management by analyzing the progress trend with reference to the learning history. Some or all of the above-mentioned processing in the tracking unit can be performed, for example, using AI, or can be performed without using AI. For example, the tracking unit can input the user's learning history data to the generation AI and cause the generation AI to perform an analysis of the progress trend.

[0114] The tracking unit can estimate the user's emotion and adjust the progress display method based on the estimated user's emotion. The tracking unit estimates the user's emotion using, for example, facial expression analysis, voice analysis, and other technologies. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotion. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotion. This makes it possible to adjust the display method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tracking unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0115] The tracking unit can change the progress feedback method according to the user's learning environment. The tracking unit analyzes data such as the physical environment (lighting, sound) and the digital environment (devices used, software) and changes the progress feedback method. For example, in a quiet place, detailed feedback is provided in text. In a noisy place, brief feedback is provided by voice. This enables effective progress management by changing the feedback method according to the learning environment. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's learning environment data to the generation AI and cause the generation AI to change the feedback method.

[0116] The tracking unit can determine the priority of progress based on the user's learning goals. The tracking unit, for example, analyzes data such as the user's short-term goals, long-term goals, and achievement criteria, and determines the priority of progress. For example, important progress is displayed with priority based on the user's learning goals. Detailed progress is displayed based on the user's learning goals. This enables effective progress management by determining the priority based on the learning goals. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's learning goal data to the generation AI and cause the generation AI to determine the priority.

[0117] The motivation unit can estimate the user's emotion and customize the motivation improvement measures based on the estimated user's emotion. The motivation unit estimates the user's emotion using technologies such as facial expression analysis and voice analysis. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotion. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotion. This makes it possible to customize the motivation improvement measures based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the motivation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the motivation unit can input the user's emotion data to the generation AI and cause the generation AI to execute customization of the motivation improvement measures.

[0118] The motivation unit can propose individual motivation improvement measures based on the user's learning history. The motivation unit, for example, analyzes data such as the user's past study time, learning content, and grades, and proposes individual motivation improvement measures. For example, the motivation unit refers to the user's learning history and proposes motivation improvement measures that have been effective in the past. The motivation unit refers to the user's learning history and proposes motivation improvement measures for specific subjects or topics. This enables effective motivation management by proposing individual motivation improvement measures based on the learning history. Some or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI, or may be performed without using AI. For example, the motivation unit can input the user's learning history data to the generation AI and have the generation AI execute the proposal of motivation improvement measures.

[0119] The motivation unit can evaluate the effectiveness of the motivation improvement measures according to the user's learning progress. The motivation unit, for example, analyzes data such as the user's learning progress, achievement level, and time management to evaluate the effectiveness of the motivation improvement measures. For example, the motivation unit refers to the user's learning progress and quantifies and evaluates the effectiveness of the motivation improvement measures. The motivation unit refers to the user's learning progress and evaluates the effectiveness of a specific motivation improvement measure. This enables effective motivation management by evaluating the effectiveness of the motivation improvement measures according to the learning progress. Some or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI or may be performed without using AI. For example, the motivation unit can input the user's learning progress data into the generation AI and cause the generation AI to execute an evaluation of the effectiveness of the motivation improvement measures.

[0120] The motivation unit can estimate the user's emotion and determine the priority order of the motivation improvement measures based on the estimated user's emotion. The motivation unit estimates the user's emotion using, for example, facial expression analysis, voice analysis, and other techniques. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotion. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotion. This allows the priority order of the motivation improvement measures to be determined based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the motivation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the motivation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of the motivation improvement measures.

[0121] The motivation unit can change the motivation improvement measures according to the user's learning environment. The motivation unit analyzes data such as the physical environment (lighting, sound) and the digital environment (devices used, software) and changes the motivation improvement measures. For example, in a quiet place, it provides a motivation improvement measure that allows the user to relax. In a noisy place, it provides a motivation improvement measure that allows the user to concentrate. This enables effective motivation management by changing the motivation improvement measures according to the learning environment. Some or all of the above-mentioned processing in the motivation unit may be performed using, for example, AI, or may be performed without using AI. For example, the motivation unit can input the user's learning environment data to the generation AI and cause the generation AI to execute the change of the motivation improvement measures.

[0122] The motivation unit can customize the content of the motivation improvement measures based on the user's learning goals. The motivation unit, for example, analyzes data such as the user's short-term goals, long-term goals, and achievement standards, and customizes the content of the motivation improvement measures. For example, a specific motivation improvement measure is provided based on the user's learning goals. A challenging motivation improvement measure is provided based on the user's learning goals. This enables effective motivation management by customizing the content of the motivation improvement measures based on the learning goals. A part or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI, or may be performed without using AI. For example, the motivation unit can input the user's learning goal data into the generation AI and cause the generation AI to customize the motivation improvement measures.

[0123] The motivation unit can evaluate the effectiveness of the motivation improvement measures according to the user's learning progress. The motivation unit, for example, analyzes data such as the user's learning progress, achievement level, and time management to evaluate the effectiveness of the motivation improvement measures. For example, the motivation unit refers to the user's learning progress and quantifies and evaluates the effectiveness of the motivation improvement measures. The motivation unit refers to the user's learning progress and evaluates the effectiveness of a specific motivation improvement measure. This enables effective motivation management by evaluating the effectiveness of the motivation improvement measures according to the learning progress. Some or all of the above-mentioned processing in the motivation unit may be performed, for example, using AI or may be performed without using AI. For example, the motivation unit can input the user's learning progress data into the generation AI and cause the generation AI to execute an evaluation of the effectiveness of the motivation improvement measures.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The plan creation unit can analyze the user's learning history, as well as the user's life rhythm and daily activity data, to suggest optimal study times. For example, taking into account the user's sleep patterns and exercise habits, study times are set during times when concentration is high. This makes it possible to provide a study plan that matches the user's life rhythm and maximize the learning effect. In addition, by avoiding times when fatigue is high based on the user's activity data, it is possible to support efficient study. Furthermore, it is possible to suggest break times and timings for refreshing based on the user's life rhythm.

[0126] The identification unit can identify problems and weaknesses based on the user's learning history as well as the user's interests and hobbies. For example, if the user is interested in a particular topic, many questions related to that topic can be presented to deepen the user's understanding. In addition, by incorporating content related to the user's hobbies into the study plan, the user's motivation to study can be improved. This allows for individualized learning support based on the user's interests. Furthermore, by providing examples and case studies related to the user's hobbies and interests, the user can deepen their understanding of the study content.

[0127] The providing unit can provide an explanation taking into consideration the user's emotional state in addition to the user's learning style. For example, if the user is feeling stressed, the explanation is given in a tone that relaxes the user. Conversely, if the user is concentrating, a detailed explanation is provided to deepen the user's understanding. The explanation format can also be changed according to the user's emotional state. For example, if the user is tired, a short video explanation is provided to maintain concentration. This enables flexible learning support according to the user's emotional state.

[0128] The tracking unit can monitor not only the user's learning progress, but also the user's health condition in real time, generating an alert if an abnormality is detected. For example, it can monitor fluctuations in the user's heart rate and body temperature, and generate an alert encouraging the user to take a break if an abnormality is detected. This makes it possible to provide learning support that takes the user's health condition into consideration. It can also adjust the learning plan based on the user's health data. For example, if fatigue is accumulating, a plan to reduce the learning load is proposed.

[0129] The motivation unit can provide an AI character with an interactive cheering message according to the user's learning progress. For example, an AI character having functions such as voice dialogue, facial expression changes, and movements is used to provide a cheering message according to the user's learning progress. For example, the voice dialogue function is used to provide encouraging words according to the user's learning progress. The facial expression change function is used to cheer the user with a facial expression according to the user's learning progress. The movement function is used to cheer the user with a movement according to the user's learning progress. In this way, the AI ​​character can provide an interactive cheering message, thereby improving the user's motivation.

[0130] The plan creation unit can set the progress speed of the learning plan individually based on the user's learning history. For example, data such as the user's past study time, study content, and grades is analyzed, and the progress speed of the learning plan is individually set. For example, if the user has studied at a fast pace in the past, the progress speed of the learning plan is set faster. If the user has studied slowly in the past, the progress speed of the learning plan is set slower. In this way, by setting the progress speed based on the learning history, it is possible to proceed with learning at an optimal pace for the user.

[0131] The plan creation unit can estimate the user's emotions and determine the priority of the learning plan based on the estimated user's emotions. For example, the user's emotions are estimated using techniques such as facial expression analysis and voice analysis. For example, the facial expression analysis captures the user's facial expression with a camera and estimates the emotions. The voice analysis analyzes the tone and speed of the user's voice and estimates the emotions. This makes it possible to determine the priority of the learning plan based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plan creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the plan creation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the learning plan.

[0132] The identification unit can refer to the user's learning history and track the progress of improvements in past problems and weak points. For example, the identification unit refers to data such as the user's past study time, study content, and grades, and tracks the progress of improvements in past problems and weak points. For example, the identification unit tracks changes in the rate of correct answers to questions that the user had difficulty with in the past. The identification unit tracks the progress of improvements in weak points that were pointed out to the user in the past. In this way, by referring to the learning history, the identification unit can accurately grasp the progress of improvements in past problems and weak points.

[0133] The providing unit can provide a live commentary by an AI instructor generated in real time based on the user's learning theme. For example, the live commentary by the AI ​​instructor is provided in the form of real-time generation, pre-recording, interactive commentary, etc. For example, in the real-time generation, an AI generates a commentary in real time based on the user's learning theme. In the pre-recording, a commentary that is pre-recorded based on the user's learning theme is provided. In the interactive commentary, an AI interactively provides a commentary based on the user's learning theme. In this way, the user's understanding can be deepened by providing a live commentary by an AI instructor generated in real time.

[0134] The motivation unit can provide information about an AI pet that grows according to the user's learning progress. For example, the AI ​​pet that grows according to the user's learning progress can be used to improve the user's motivation to learn. For example, the AI ​​pet grows according to the user's learning progress and has an interaction function. As the user progresses with their learning, the AI ​​pet grows and acquires new functions and actions. In this way, by providing information about the growing AI pet, the user's motivation to learn can be improved.

[0135] The process flow of the second embodiment will be briefly described below.

[0136] Step 1: The plan creation unit analyzes the user's learning history and goals, and creates a learning plan tailored to the user. The user's learning history includes past study time, study content, grades, etc. The plan creation unit collects data such as past test results, study time, and study content, and the AI ​​analyzes it. This makes it possible to understand the user's strengths and weaknesses and create an individual learning plan. Step 2: The identification unit identifies the user's problems and weaknesses in the learning process using the learning plan created by the plan creation unit. For example, if the correct answer rate for a particular problem is low, the identification unit identifies that problem as the user's weakness. Step 3: The providing unit provides the user with explanations and mock exams based on the problems and weaknesses identified by the identifying unit. For example, the providing unit provides detailed explanations for problems that the user is weak at, deepening the user's understanding. The mock exams also enable the user to prepare for the actual exam. Step 4: The tracking unit tracks the user's progress in the learning plan created by the plan creation unit. For example, the tracking unit monitors the learning progress in real time and generates an alert if an abnormality is detected. This makes it easier for the user to grasp the learning progress. Step 5: The motivation unit improves the motivation based on the progress tracked by the tracking unit. The motivation unit improves the user's motivation to learn, for example, by using gamification technology. In addition, the motivation unit can maintain the user's motivation by sending encouraging messages customized according to the learning progress.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0139] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] For example, the plan creation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the identification unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the tracking unit is realized by the specific processing unit 290 of the data processing device 12. For example, the motivation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] For example, the plan creation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the identification unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the tracking unit is realized by the specific processing unit 290 of the data processing device 12. For example, the motivation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0172] For example, the plan creation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the identification unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the tracking unit is realized by the specific processing unit 290 of the data processing device 12. For example, the motivation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] 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.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0184] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] For example, the plan creation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the identification unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the tracking unit is realized by the specific processing unit 290 of the data processing device 12. For example, the motivation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0191] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0195] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] In addition, 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 upon request from the data processing device 12.

[0200] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0202] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0206] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0208] (Appendix 1) A plan creation unit that analyzes a user's learning history and goals and creates a learning plan according to the user; an identification unit for identifying problems and weaknesses of the user in the course of learning according to the learning plan created by the plan creation unit; a providing unit that provides explanations and practice tests to the user based on the problems and weaknesses identified by the identifying unit; a tracking unit that tracks the user's progress in the learning plan created by the plan creation unit; A motivation unit that improves motivation based on the progress tracked by the tracking unit. A system characterized by: (Appendix 2) The plan creation unit, Use machine learning models to create personalized learning plans for users 2. The system of claim 1, (Appendix 3) The providing unit is Provides customized explanations based on the user's learning style 2. The system of claim 1, (Appendix 4) The tracking unit includes: Monitor user progress in real time and generate alerts if certain anomalies are detected 2. The system of claim 1, (Appendix 5) The motivation unit includes: Using gamification techniques to increase users' motivation to learn 2. The system of claim 1, (Appendix 6) The motivation unit includes: Sending customized encouragement messages based on user progress 2. The system of claim 1, (Appendix 7) The plan creation unit, It uses biometric data from wearable devices to analyze the user's emotional state, measure stress levels and create study plans based on the results. 2. The system of claim 1, (Appendix 8) The motivation unit includes: An AI character interactively provides encouraging messages based on the user's learning progress. 2. The system of claim 1, (Appendix 9) The providing unit is Provides live commentary by AI instructors generated in real time based on the user's learning topic 2. The system of claim 1, (Appendix 10) The motivation unit includes: Provide information about AI pets that grow according to the user's learning progress 2. The system of claim 1, (Appendix 11) The plan creation unit, Analyzes the user's genetic information and creates a learning plan based on the results 2. The system of claim 1, (Appendix 12) The plan creation unit, Inferring user emotions and adjusting the difficulty of the study plan based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The plan creation unit, Personalize the pace of progress through a learning plan based on a user's learning history 2. The system of claim 1, (Appendix 14) The plan creation unit, Customize the content of your learning plan based on your learning style 2. The system of claim 1, (Appendix 15) The plan creation unit, Infer user emotions and prioritize learning plans based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The plan creation unit, Suggesting suitable study times based on the user's daily routine 2. The system of claim 1, (Appendix 17) The plan creation unit, Providing additional learning resources related to your learning plan based on your interests 2. The system of claim 1, (Appendix 18) The identification unit is Infer user emotions and adjust feedback methods for problems and weaknesses based on the inferred user emotions 2. The system of claim 1, (Appendix 19) The identification unit is Analyze the user's answer patterns and evaluate their understanding of a particular question 2. The system of claim 1, (Appendix 20) The identification unit is View users' learning history and track past issues and improvements to weaknesses 2. The system of claim 1, (Appendix 21) The identification unit is Estimating a user's emotion and adjusting the display method of the classification result based on the estimated user's emotion 2. The system of claim 1, (Appendix 22) The identification unit is Change the way the classification results are presented depending on the user's learning environment 2. The system of claim 1, (Appendix 23) The identification unit is Prioritize the classification results based on the user's learning progress 2. The system of claim 1, (Appendix 24) The providing unit is Infer user emotions and adjust the tone and style of commentary based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The providing unit is Dynamically change the level of detail of the explanation according to the user's level of understanding 2. The system of claim 1, (Appendix 26) The providing unit is Providing relevant examples and case studies based on the user's learning topic 2. The system of claim 1, (Appendix 27) The providing unit is To estimate a user's emotion and adjust the difficulty of a mock test based on the estimated user's emotion. 2. The system of claim 1, (Appendix 28) The providing unit is Based on the user's learning history, questions that were previously answered incorrectly are re-asked 2. The system of claim 1, (Appendix 29) The providing unit is Adjust the frequency of explanations and practice tests according to the user's learning progress 2. The system of claim 1, (Appendix 30) The tracking unit includes: Inferring a user's emotions and adjusting the progress feedback method based on the estimated user's emotions 2. The system of claim 1, (Appendix 31) The tracking unit includes: Analyzes user learning data in real time and generates an alert if an anomaly is detected 2. The system of claim 1, (Appendix 32) The tracking unit includes: Refer to the user's learning history and analyze progress trends 2. The system of claim 1, (Appendix 33) The tracking unit includes: Inferring a user's emotions and adjusting how progress is displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 34) The tracking unit includes: Change the way you provide progress feedback depending on your learning environment 2. The system of claim 1, (Appendix 35) The tracking unit includes: Prioritize progress based on user learning goals 2. The system of claim 1, (Appendix 36) The motivation unit includes: Inferring user emotions and customizing motivational measures based on the inferred user emotions 2. The system of claim 1, (Appendix 37) The motivation unit includes: Suggesting individual motivational measures based on the user's learning history 2. The system of claim 1, (Appendix 38) The motivation unit includes: Evaluate the effectiveness of motivational measures according to the user's learning progress 2. The system of claim 1, (Appendix 39) The motivation unit includes: Estimate the user's emotions and prioritize motivation improvement measures based on the estimated user emotions. 2. The system of claim 1, (Appendix 40) The motivation unit includes: Change motivational strategies according to the user's learning environment 2. The system of claim 1, (Appendix 41) The motivation unit includes: Customize motivational content based on the user's learning goals 2. The system of claim 1, (Appendix 42) The motivation unit includes: Evaluate the effectiveness of motivational measures according to the user's learning progress 2. The system of claim 1, [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A plan creation unit that analyzes a user's learning history and goals and creates a learning plan according to the user; an identification unit for identifying problems and weaknesses of the user in the course of learning according to the learning plan created by the plan creation unit; a providing unit that provides explanations and practice tests to the user based on the problems and weaknesses identified by the identifying unit; a tracking unit that tracks the user's progress in the learning plan created by the plan creation unit; A motivation unit that has a specific method for improving motivation based on the progress tracked by the tracking unit. A system characterized by:

2. The plan creation unit, Using machine learning models to create a personalized learning plan for the user 2. The system of claim 1 .

3. The providing unit is Providing customized explanations according to the user's learning style 2. The system of claim 1 .

4. The tracking unit includes: Monitor the user's progress in real time and generate alerts if certain anomalies are detected 2. The system of claim 1 .

5. The motivation unit includes: Using gamification techniques to increase the user's motivation to learn 2. The system of claim 1 .

6. The motivation unit includes: Sending customized encouraging messages according to the user's learning progress 2. The system of claim 1 .

7. The plan creation unit, Using biometric data from the wearable device to analyze the user's emotional state, measure stress levels, and create a learning plan based on the results.

2. The system of claim 1 .

8. The motivation unit includes: The AI ​​character interactively provides encouraging messages according to the user's learning progress.

2. The system of claim 1 .

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