system

Abstract

We provide the system. [Solution] A means of receiving educational information provided by the user, A means for analyzing the aforementioned educational information by comparing it with existing curriculum and external examination trend data, A means for generating a plan for the following educational activities based on the above analysis, A means of presenting the generated educational activity plan to the user, A means of evaluating learners' comprehension using emotion recognition technology and supporting the adjustment of educational activities based on this evaluation, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional educational field, there is a problem that teachers require a great deal of time and labor when formulating daily lesson plans. This process depends on individual teachers and is not efficiently standardized, which not only increases the burden on the teachers themselves but also may cause variations in the quality of the provided education. Furthermore, the implementation of education directly related to external examinations such as high school entrance examinations may not be appropriately carried out, which has become an obstacle to efficient learning for both teachers and students.

Means for Solving the Problems

[0005] This invention solves these problems by providing an analytical means that receives educational information and compares it with existing curriculum and external examination trend data. Based on this analysis, it automatically generates plans for the next educational activities, thereby reducing the burden on teachers and supporting the preparation of efficient and standardized lessons. Furthermore, the generated educational plans take into account the trends in questions asked in external examinations, enabling appropriate learning guidance. In addition, by receiving feedback from users and utilizing it in subsequent analyses, it is possible to improve plans and enhance the quality of education.

[0006] "Educational information" refers to all data related to educational activities, such as the content of lessons, students' level of understanding, and the progress of assignments.

[0007] "Means of receiving" refers to a mechanism or process for taking in information from the user, which is usually done via a digital device.

[0008] A "curriculum" refers to a set of educational content and plans based on the learning guidelines established by an educational institution.

[0009] "External exam trend data" refers to information used to predict future exam trends based on an analysis of past exam questions, their frequency, and format.

[0010] "Means of analysis" refers to methods and techniques for interpreting received data and drawing conclusions by comparing it with other data as needed.

[0011] An "educational activity plan" refers to a detailed outline of the goals, content, and methods to be achieved in a particular lesson or learning session.

[0012] "Means of presentation" refers to a system that allows users to view automatically generated plans and information, which typically utilizes displays or printed materials.

[0013] "Feedback" refers to opinions and information from users regarding the results and progress of educational activities, which are used in formulating future plans. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a support system for educational institutions and individual teachers to efficiently create lesson plans. This system is comprised of a server, terminals, and users.

[0036] Server: The server, which forms the core of the system, has the function of importing curriculum guidelines and trend data from past external examinations and storing them in a database. It receives educational information sent by users and performs processing to compare and analyze it with existing data. Based on the analysis results, it automatically generates the next lesson plan and improves the accuracy of the system by accepting feedback. It also sends the generated lesson plan to the user's terminal so that they can check it.

[0037] Terminal: The terminal is a device used by the user, providing an interface for inputting class progress information and assignments and sending them to the server. It also displays the next class plan sent from the server, allowing the user to review and edit it. Feedback can also be entered and resent to the server to help improve the plan.

[0038] User: As a teacher, the user is responsible for inputting daily lesson progress and learning content into the system. They receive the generated lesson plan and conduct lessons based on it. They also contribute to further optimization of future lesson plans by inputting feedback on the results after implementation.

[0039] As a concrete example, consider a scenario where a user inputs "Lesson progress on geometry for 2nd-year mathematics" into their terminal. The user reports to the system that they taught the congruence conditions for triangles, but the student is having difficulty understanding them. The server receives this information, identifies the points of insufficient understanding based on the curriculum guidelines, and, comparing them with data on high school entrance exam trends, creates a plan to improve the content for the next lesson. This plan includes specific explanations and practice problems, which are provided to the user via the terminal. The user can refer to the plan and use it to prepare for the next lesson.

[0040] In this way, the system aims to reduce the burden on teachers and support the implementation of more efficient and effective lessons.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] After each lesson, users input educational information such as "lesson content," "student comprehension level," and "areas for next lesson" into their devices. This input data includes specific topics and student responses.

[0044] Step 2:

[0045] The terminal sends educational information entered by the user to the server. This transmission occurs in real time or at specified intervals via the network.

[0046] Step 3:

[0047] The server temporarily stores the received educational information in a database. This database also stores past lesson data and trend data from external examinations.

[0048] Step 4:

[0049] The server uses an analysis module to compare the stored educational information with the curriculum guidelines and evaluate the extent to which the content and progress of the lessons are meeting the objectives.

[0050] Step 5:

[0051] The server compares and analyzes data from external exams to determine which topics and exercises should be given particular emphasis in the next class.

[0052] Step 6:

[0053] The server generates an automated lesson plan based on the analysis results. This plan includes specific lesson content, recommended materials, and teaching methods.

[0054] Step 7:

[0055] The generated lesson plan is sent from the server to the terminal and presented to the user. The user then uses this to prepare for the next lesson.

[0056] Step 8:

[0057] After the lesson, users enter new feedback into their terminals and send it back to the server. This feedback is used to improve the next lesson plan.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] For educational institutions and teachers to create lesson plans quickly and effectively, data collection and analysis from diverse sources are necessary. However, these tasks have traditionally been very time-consuming and labor-intensive, increasing the burden on teachers. Furthermore, creating plans that take exam trends into account is complex and highly dependent on the experience of individual teachers. In addition, it has been difficult to effectively incorporate post-lesson feedback into future plans, hindering improvements in the quality of instruction.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for receiving educational data provided by the user, means for analyzing the educational data by comparing it with existing teaching standards and test trend information, and means for automatically generating a plan for the next educational activity based on the analysis using a generative AI model. This enables educational institutions and teachers to efficiently utilize data, quickly create lesson plans that take test trends into consideration, and incorporate feedback into the next plan.

[0063] "Educational data" is a general term for information provided by users regarding lesson progress and learning content.

[0064] "Instructional standards" refer to the criteria set by educational institutions that outline the learning curriculum and learning objectives.

[0065] "Exam trend information" refers to information about past exam question patterns and important points.

[0066] A "generative AI model" is a model that uses artificial intelligence to perform data analysis and plan generation.

[0067] An "educational activity plan" refers to a plan for the progress of lessons and learning that is structured to achieve specific learning objectives.

[0068] "Feedback" refers to information provided by users regarding their evaluations and reactions after a lesson has been conducted.

[0069] This invention provides a system for educational institutions and teachers to efficiently create lesson plans. The embodiments for carrying out the invention are described below.

[0070] Server: The server is the core computer device of the system. It collects curriculum guidelines and past exam trend data and stores them in a database. It receives educational data, analyzes it using a generation AI model, and automatically generates the next lesson plan. The server also receives feedback from users and uses it to optimize the plan. Standard server hardware and data analysis software such as Python and R are used for calculations performed by the server.

[0071] Terminals: Terminals are digital devices used by users, including personal computers, tablets, and smartphones. Users input information about class progress and assignments via their terminals and send it to the server. The server displays the received next class plan and provides an interface that users can review and edit. Specifically, a web browser or dedicated application is used as the interface.

[0072] User: The user is an educator and is responsible for inputting daily lesson content into the terminal. They refer to the next lesson plan and use it to prepare for the lesson. After the lesson, they input the results as feedback to help improve the next plan.

[0073] As a concrete example, consider a scenario where a user inputs "Lesson progress on geometry for 2nd-year mathematics" into their terminal and reports to the system that they taught the congruence conditions for triangles, but the students' understanding was insufficient. The server receives this information and performs a detailed analysis based on teaching standards and exam trend information. As a result, a new lesson plan is automatically generated and provided to the user via the terminal. This plan includes supplementary explanations and additional practice problems, which the user can use to prepare for lessons.

[0074] In this way, the present invention supports the creation of efficient and effective lesson plans for educational institutions and teachers, thereby reducing the burden on teachers. An example of a prompt message would be, "Create a lesson plan for 2nd-year mathematics. Add supplementary explanations, especially regarding triangle congruence."

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

[0076] Step 1:

[0077] The server collects curriculum guidelines and exam trend information from online resources and educational institutions. It then formats this data and stores it in a database. Input is from external data sources, and output is formatted data. Specifically, the server performs data collection via APIs and CSV file analysis.

[0078] Step 2:

[0079] The terminal provides an interface for users to input progress information and assignment details for their lessons. This information is converted into a format that the system can understand and sent to the server. The input is educational data entered by the user, and the output is data converted into a server-readable format. Specifically, the form input data is converted into JSON format and sent to the server.

[0080] Step 3:

[0081] The server analyzes educational data sent from terminals by comparing it with existing data in the database. This process utilizes a generative AI model to identify particularly critical learning points. The input consists of educational data sent from terminals and instructional standards and exam trend information from the database, while the output is the analysis results. Specifically, text analysis and pattern recognition are performed by the AI ​​model.

[0082] Step 4:

[0083] The server automatically generates the next lesson plan based on the analysis results. Using a generative AI model, it creates a detailed plan including teaching content and practice problems. The input is the analysis results, and the output is the next lesson plan. Specifically, a language generation model generates the plan's text and suggests necessary materials.

[0084] Step 5:

[0085] The terminal receives lesson plans sent from the server and displays them to the user. The user can review and edit them as needed. The input is the lesson plan from the server, and the output is an interface that the user can view and edit. Specifically, the plan content is displayed as HTML on the terminal screen, and editing functions are provided.

[0086] Step 6:

[0087] Users input the results of their lessons into their terminals. Feedback is sent to the server and used for analysis and planning for future lessons. The input is user feedback, and the output is feedback data for the server. Specifically, comments and evaluation data are sent to the server via an input form.

[0088] Step 7:

[0089] The server uses the received feedback to improve the overall accuracy of the system. Machine learning algorithms are utilized to help generate the next lesson plan. The input is feedback data, and the output is an improved analytical model. Specifically, feedback is added to the dataset, and the AI ​​model is retrained.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In recent years, educational settings have required the understanding and individualization of diverse student levels. However, conventional educational support systems primarily focus on the automatic generation of curricula and lack mechanisms to evaluate students' understanding and concentration levels in real time and support flexible lesson adjustments based on that evaluation. As a result, teachers have been unable to properly grasp the understanding of each student, making effective lesson management difficult.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for receiving educational information provided by the user, means for analyzing the educational information by comparing it with existing curriculum and external examination trend data, and means for evaluating the learner's level of understanding using emotion recognition technology and supporting the adjustment of educational activities based on this evaluation. This makes it possible to grasp the student's level of understanding in real time and flexibly adjust the lesson plan.

[0095] A "user" is an entity that provides educational information and refers to the generated educational activity plans.

[0096] "Educational information" refers to information that includes data on curriculum progress and students' learning status.

[0097] "Existing curriculum" refers to the standard course content and objectives set by an educational institution.

[0098] "External exam trend data" refers to information regarding the content and frequency of questions asked in past exams.

[0099] "Emotion recognition technology" is a technology that estimates emotions from a learner's facial expressions and voice, and evaluates their level of comprehension and concentration.

[0100] The "Educational Activity Plan" will include specific guidelines regarding the progress and content of lessons.

[0101] The system implementing this invention primarily operates with a server at its core. The server receives educational information from users and analyzes this data by comparing it with existing curriculum and external exam trend data. In this process, it performs large-scale data processing using a database management system and machine learning algorithms.

[0102] The server also utilizes emotion recognition technology to evaluate learners' comprehension levels from data provided by users. This technology includes image processing software and voice analysis software for facial expressions and voice analysis. This enables the generation of educational activity plans tailored to the user, supporting more effective lesson management.

[0103] The terminal is a device used by the user and is responsible for presenting the generated educational activity plan to the user. The terminal also allows for the review and editing of the generated plan, and users can send additional feedback to the server. In this way, educational information is entered and feedback is provided in real time via the terminal.

[0104] This system includes, as a concrete example, a system in which teachers wear smart wearable devices to monitor students' understanding and concentration levels in real time. An example of a prompt message is, "Please evaluate the students' level of engagement based on their facial expressions and body movements." This can be used to generate lesson plans tailored to the students' situations, and the system can be continuously improved based on the feedback provided as needed.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server receives educational information from the terminal. This educational information includes the progress of the curriculum entered by the user and the learning status of students. The server stores this information in a database and uses it as basic data for subsequent analysis.

[0108] Step 2:

[0109] The server analyzes the received educational information by comparing it with existing curriculum and external exam trend data. Using a database management system, it searches for relevant data and applies machine learning algorithms to process the data for creating optimal educational plans. As an output, it generates an analytical report on students' current level of understanding.

[0110] Step 3:

[0111] The server uses generative AI models and emotion recognition technology to evaluate learners' comprehension. Specifically, it utilizes facial expression analysis programs and speech recognition software to analyze learners' facial expressions and speaking style from received data, thereby determining their emotional state. The input is student voice and facial expression data, and the output is an emotion evaluation score.

[0112] Step 4:

[0113] The terminal displays a plan of educational activities sent from the server to the user. The user uses this as a guide when conducting lessons. An interface has been developed on the terminal, allowing the user to review the plan and edit the necessary parts.

[0114] Step 5:

[0115] After conducting a lesson, users input feedback into the server via their device. This feedback includes information about the effectiveness of the lesson and student responses. The server uses the received feedback for subsequent data analysis to further optimize future educational activities.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention provides a system that supports educational institutions and teachers in creating lesson plans more effectively, and in particular, by integrating an emotion engine, it enables lesson plans that take the user's emotions into account. This system, including the emotion engine, consists of three components: a server, a terminal, and a user.

[0118] Server: As a central processing unit, the server receives educational information, feedback, and user emotion data recognized by the emotion engine, and stores this information in a database. This database also stores curriculum guidelines and trend data from past external examinations. Based on the received information, the server analyzes the educational information and generates the next lesson plan while taking the user's emotions into consideration. This adjusts the plan to better suit the user.

[0119] Terminal: The terminal provides an interface for users to input educational information and interact with the server. Users input their lesson progress here, and the terminal activates an emotion engine to analyze the user's emotions at the time of input. The analysis results are sent to the server and reflected in the next lesson plan. Furthermore, the terminal displays the plan provided by the server, allowing the user to review its contents.

[0120] User: The user, who is a teacher, inputs information about the progress of the lesson and their own emotions into the terminal. For example, they identify difficulties in the lesson or emotional stress points and share them with the system via the terminal. This allows the server to provide the next lesson plan based on the emotional data.

[0121] As a concrete example, consider a scenario where a user inputs information into their terminal, such as "I felt stressed because students weren't understanding the geometry lesson in the second year," along with "stress" data detected by the emotion engine. The server analyzes this information and generates a plan that includes specific approaches to deepen students' understanding in the next lesson. This plan also takes into account effective teaching methods to reduce stress.

[0122] This system allows for more personalized educational activities and enables them to proceed in a way that also takes into account the well-being of the teachers themselves.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] After each lesson, users input information into their device about the lesson content, students' understanding, and any stress or emotions they experienced. This data entry is intended to provide a detailed record of specific events and emotions.

[0126] Step 2:

[0127] The device automatically sends the entered educational information to an emotion engine to analyze the user's emotions. This analysis infers emotions from text data and the user's actions during input.

[0128] Step 3:

[0129] The device sends the emotional data obtained as a result of the analysis to the server along with educational information. This transmitted data includes information such as "Lesson progress: Delayed" and "Emotion: Stress."

[0130] Step 4:

[0131] The server compares the received educational information and sentiment data with existing curriculum guidelines and external examination trend data to analyze the progress and areas of deficiency in educational content.

[0132] Step 5:

[0133] The server automatically generates the next lesson plan based on the analysis results. This process also takes user emotional data into consideration, ensuring the plan includes measures to alleviate the stress the user experienced.

[0134] Step 6:

[0135] The generated lesson plan is sent from the server to the terminal and presented to the user as the plan for the next lesson. The user can then review it and prepare for the lesson based on the plan.

[0136] Step 7:

[0137] After the lesson is conducted, users again input sentiment information, including feedback, and send it to the server. The server uses this information to improve future lesson plans.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] Traditional educational planning systems generated educational plans based solely on information provided by educators, thus failing to adequately consider the emotional aspects of educators. This led to increased psychological burden on educators and potentially a decline in the quality of education. Furthermore, the generated plans were rigid and unable to flexibly adapt to changing circumstances.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for receiving educational information and emotional data provided by the user, means for analyzing the educational information and emotional data by comparing them with existing educational policy and evaluation indicator data, and means for generating a plan for the next educational instruction based on the analysis. This makes it possible to generate more personalized educational plans that take into account the emotions of educators, thereby reducing the burden on educators and improving the quality of education.

[0143] "Educational information provided by users" refers to information about educators' lesson plans and lesson content.

[0144] "Emotional data" refers to data that expresses the emotional state of educators when conducting lessons as numerical values ​​or categories.

[0145] "Existing educational policies" refer to the guidelines for curricula and teaching methods established by educational institutions.

[0146] "Evaluation indicator data" refers to data such as students' learning achievement levels based on past exams and evaluations.

[0147] "Means of analysis" refers to the technologies and methods used to analyze collected educational information and emotional data and process them for use in future planning.

[0148] "Means for generating educational guidance plans" refers to systems and methods for planning the next lesson or instructional content based on input data and analysis results from educators.

[0149] A "sentiment analysis engine" refers to a software module that analyzes and quantifies or categorizes emotional states from input by educators.

[0150] This embodiment of the invention is an educational planning system for supporting educators, and consists of three components: a server, a terminal, and a user. Specifically, the server functions as a central processing unit and receives and analyzes the various types of data described below.

[0151] server:

[0152] The server receives educational information and emotional data provided by educators via their devices. During this process, an emotional analysis engine detects the educators' emotions and quantifies the results. The server stores the received data in a database and analyzes it using Python data science tools. Furthermore, it utilizes a generative AI model to generate educational guidance plans that take the educators' emotions into account. Specifically, general natural language generation tools can be used for the AI ​​model.

[0153] Terminal:

[0154] The terminal is used by educators and provides an interface for inputting educational information and emotional data. An emotional analysis engine operates on the terminal to analyze the input emotions. Furthermore, it displays educational instruction plans sent from the server, allowing educators to review these plans and provide feedback. This feedback is then incorporated into subsequent analyses.

[0155] User:

[0156] The user, an educator, inputs their own emotions along with educational information into the terminal. For example, they input self-assessments and emotional states based on students' understanding in class. As a specific example, they might input a situation such as, "Students were slow to understand in today's lesson, and I felt insecure about my explanation methods," and emotional data based on that would be generated. It is also assumed that the generating AI model would be prompted with a message such as, "In the educational system, please propose a lesson plan that takes into account the user's emotions, such as experiencing a decline in student motivation in recent lessons and feeling stressed about it."

[0157] This embodiment allows educators to receive an optimal educational plan that takes their own emotional state into account, and is expected to lead to more personalized educational activities.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] Users input educational information and emotional data through their devices. Specifically, they input text about the progress of lessons and their own feelings (e.g., "Students showed no motivation, which made me anxious"). This input data forms the basis for the next step.

[0161] Step 2:

[0162] The terminal receives the input educational information and emotional data, and uses an emotional analysis engine to analyze the emotional data. The emotional analysis engine converts the emotions from the input text into numerical values ​​and outputs "anxiety" as an emotional score as a result of the analysis. The emotional data is then sent to the server in an analyzed state.

[0163] Step 3:

[0164] The server receives educational information and quantified sentiment data transmitted from terminals. The server stores this data in a database and performs detailed data analysis by comparing it with existing educational policy and evaluation metric data. Using Python data science tools, it conducts trend and correlation analyses of the data and generates future educational guidance plans based on the analysis results.

[0165] Step 4:

[0166] The server uses a generative AI model to generate educational guidance plans based on educational information and analysis results stored on the server. Specifically, it inputs a prompt message to the generative AI model (e.g., a general natural language generation tool) such as, "In the educational system, please propose a lesson plan that takes into account the feelings of the user who recently experienced a decline in student motivation in a lesson and is feeling stressed about it," and outputs a plan that is sensitive to the educator's feelings.

[0167] Step 5:

[0168] The server sends the generated teaching plan to the terminal. This plan incorporates the previous analysis results and suggestions from the generated AI model, and is adjusted to be meaningful for educators.

[0169] Step 6:

[0170] The terminal displays the received lesson plan to the user. The user can review the plan and enter feedback into the terminal as needed. This feedback will be used to improve future lesson plans. Furthermore, the feedback information will be used as input for the next data analysis cycle, contributing to the overall improvement of the system's accuracy.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] In educational settings, teachers face the challenge of not being able to consider individual students' learning progress, understanding, and emotional state when creating lesson plans. In particular, there is a need to grasp students' responses and interests to learning in real time and adjust lesson content accordingly, but the current system has limited means of doing this efficiently.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes means for receiving educational information provided by the user, means for analyzing the educational information by comparing it with existing curriculum and external examination trend data, means for generating a plan for the next educational activity based on the analysis, and means for analyzing the user's emotional state and providing real-time feedback. This enables teachers in educational settings to create and present individually optimized lesson plans in real time based on students' emotions and level of understanding.

[0176] "Users" are entities that provide educational information using the system, and primarily refer to teachers.

[0177] "Educational information" is a general term for various data and materials related to education provided by users, and includes information such as students' learning progress and understanding.

[0178] A "curriculum" is a set of educational content and learning plans organized to achieve specific educational objectives.

[0179] "External exam trend data" refers to data on past exam questions and their question trends, and is information used to predict future exam questions.

[0180] "Analysis" is the process of comparing received educational information with existing data and interpreting and evaluating its content based on certain rules.

[0181] An "educational activity plan" is a plan that defines the specific content and methods of lessons to effectively promote students' learning.

[0182] "Emotional state" refers to the user's psychological state and emotional fluctuations, and includes information about internal reactions such as stress, anxiety, and relaxation.

[0183] A "means of providing real-time feedback" refers to a function that instantly returns information about the user's feelings and level of understanding, and provides advice and instructions that are useful for the progress of the lesson.

[0184] In implementing this invention, a specific system will be developed through the following hardware and software configuration. The main components of the system are a server, a terminal, and a user.

[0185] The server functions as a central processing unit, receiving educational information from users and analyzing it by comparing it with existing curriculum and external exam trend data. Based on this analysis, it generates a plan for the next educational activity. The generated plan is adjusted to take into account the user's emotional state. Emotional analysis will utilize an emotion recognition library. By using this library, the teacher's stress, anxiety, and relaxation levels will be identified, and this information will be reflected in the lesson plan.

[0186] The terminal provides an interface for users to input educational information and feedback. Wearable devices such as smart glasses are connected to the terminal and have the capability to collect and analyze real-time emotional states. These devices extract emotional data from the user's own recordings and facial expressions and send it to the server. As a result, users can receive immediate, emotion-based feedback.

[0187] Teachers, as users, input their emotions and lesson progress into their devices, receive feedback from the system, and use it to plan their next lessons. This allows teachers to gain concrete approaches to conducting better educational activities.

[0188] For example, if a teacher feels that students are not responding well in a math class, they can immediately input their thoughts and feelings into a terminal. Based on this information, the server can suggest alternative approaches, such as "problem-solving learning," for the next class. An example of a prompt provided by the AI ​​model could be: "Generate specific teaching methods to improve student comprehension in a 2nd-year geometry class and reduce the teacher's anxiety."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The terminal receives input from the user. This input includes information on the progress of the lesson and the user's self-reported emotional state. The terminal formats this data and preprocesses it for transmission to the server.

[0192] Step 2:

[0193] The server receives educational information and sentiment data transmitted from the terminal. The received data is analyzed using an educational information database and a sentiment recognition library. In this process, educational information is compared with existing curriculum and external exam trend data, and sentiment data is analyzed by sentiment recognition algorithms.

[0194] Step 3:

[0195] The server generates a plan for the next educational activity based on the analysis results. This plan generation uses an AI-based recommendation algorithm, and the generated plan is customized to take into account the user's emotional state. In this process, the generating AI model is used to create appropriate prompt sentences and make specific suggestions such as, "Generate specific teaching methods to improve student understanding in 2nd-year geometry lessons and reduce teacher anxiety."

[0196] Step 4:

[0197] The server sends the generated educational activity plan to the terminal. The terminal displays this plan to the user. At this time, the user can review the plan and provide additional feedback as needed. The feedback is received and processed by the server again and used for future planning.

[0198] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention provides a support system for educational institutions and individual teachers to efficiently create lesson plans. This system is comprised of a server, terminals, and users.

[0215] Server: The server, which forms the core of the system, has the function of importing curriculum guidelines and trend data from past external examinations and storing them in a database. It receives educational information sent by users and performs processing to compare and analyze it with existing data. Based on the analysis results, it automatically generates the next lesson plan and improves the accuracy of the system by accepting feedback. It also sends the generated lesson plan to the user's terminal so that they can check it.

[0216] Terminal: The terminal is a device used by the user, providing an interface for inputting class progress information and assignments and sending them to the server. It also displays the next class plan sent from the server, allowing the user to review and edit it. Feedback can also be entered and resent to the server to help improve the plan.

[0217] User: As a teacher, the user is responsible for inputting daily lesson progress and learning content into the system. They receive the generated lesson plan and conduct lessons based on it. They also contribute to further optimization of future lesson plans by inputting feedback on the results after implementation.

[0218] As a concrete example, consider a scenario where a user inputs "Lesson progress on geometry for 2nd-year mathematics" into their terminal. The user reports to the system that they taught the congruence conditions for triangles, but the student is having difficulty understanding them. The server receives this information, identifies the points of insufficient understanding based on the curriculum guidelines, and, comparing them with data on high school entrance exam trends, creates a plan to improve the content for the next lesson. This plan includes specific explanations and practice problems, which are provided to the user via the terminal. The user can refer to the plan and use it to prepare for the next lesson.

[0219] In this way, the system aims to reduce the burden on teachers and support the implementation of more efficient and effective lessons.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] After each lesson, users input educational information such as "lesson content," "student comprehension level," and "areas for next lesson" into their devices. This input data includes specific topics and student responses.

[0223] Step 2:

[0224] The terminal sends educational information entered by the user to the server. This transmission occurs in real time or at specified intervals via the network.

[0225] Step 3:

[0226] The server temporarily stores the received educational information in a database. This database also stores past lesson data and trend data from external examinations.

[0227] Step 4:

[0228] The server uses an analysis module to compare the stored educational information with the curriculum guidelines and evaluate the extent to which the content and progress of the lessons are meeting the objectives.

[0229] Step 5:

[0230] The server compares and analyzes data from external exams to determine which topics and exercises should be given particular emphasis in the next class.

[0231] Step 6:

[0232] The server generates an automated lesson plan based on the analysis results. This plan includes specific lesson content, recommended materials, and teaching methods.

[0233] Step 7:

[0234] The generated lesson plan is sent from the server to the terminal and presented to the user. The user then uses this to prepare for the next lesson.

[0235] Step 8:

[0236] After the lesson, users enter new feedback into their terminals and send it back to the server. This feedback is used to improve the next lesson plan.

[0237] (Example 1)

[0238] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0239] For educational institutions and teachers to create lesson plans quickly and effectively, data collection and analysis from diverse sources are necessary. However, these tasks have traditionally been very time-consuming and labor-intensive, increasing the burden on teachers. Furthermore, creating plans that take exam trends into account is complex and highly dependent on the experience of individual teachers. In addition, it has been difficult to effectively incorporate post-lesson feedback into future plans, hindering improvements in the quality of instruction.

[0240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0241] In this invention, the server includes means for receiving educational data provided by the user, means for analyzing the educational data by comparing it with existing teaching standards and test trend information, and means for automatically generating a plan for the next educational activity based on the analysis using a generative AI model. This enables educational institutions and teachers to efficiently utilize data, quickly create lesson plans that take test trends into consideration, and incorporate feedback into the next plan.

[0242] "Educational data" is a general term for information provided by users regarding lesson progress and learning content.

[0243] "Instructional standards" refer to the criteria set by educational institutions that outline the learning curriculum and learning objectives.

[0244] "Exam trend information" refers to information about past exam question patterns and important points.

[0245] A "generative AI model" is a model that uses artificial intelligence to perform data analysis and plan generation.

[0246] An "educational activity plan" refers to a plan for the progress of lessons and learning that is structured to achieve specific learning objectives.

[0247] "Feedback" refers to information provided by users regarding their evaluations and reactions after a lesson has been conducted.

[0248] This invention provides a system for educational institutions and teachers to efficiently create lesson plans. The embodiments for carrying out the invention are described below.

[0249] Server: The server is the core computer device of the system. It collects curriculum guidelines and past exam trend data and stores them in a database. It receives educational data, analyzes it using a generation AI model, and automatically generates the next lesson plan. The server also receives feedback from users and uses it to optimize the plan. Standard server hardware and data analysis software such as Python and R are used for calculations performed by the server.

[0250] Terminals: Terminals are digital devices used by users, including personal computers, tablets, and smartphones. Users input information about class progress and assignments via their terminals and send it to the server. The server displays the received next class plan and provides an interface that users can review and edit. Specifically, a web browser or dedicated application is used as the interface.

[0251] User: The user is an educator and is responsible for inputting daily lesson content into the terminal. They refer to the next lesson plan and use it to prepare for the lesson. After the lesson, they input the results as feedback to help improve the next plan.

[0252] As a concrete example, consider a scenario where a user inputs "Lesson progress on geometry for 2nd-year mathematics" into their terminal and reports to the system that they taught the congruence conditions for triangles, but the students' understanding was insufficient. The server receives this information and performs a detailed analysis based on teaching standards and exam trend information. As a result, a new lesson plan is automatically generated and provided to the user via the terminal. This plan includes supplementary explanations and additional practice problems, which the user can use to prepare for lessons.

[0253] In this way, the present invention supports the creation of efficient and effective lesson plans for educational institutions and teachers, thereby reducing the burden on teachers. An example of a prompt message would be, "Create a lesson plan for 2nd-year mathematics. Add supplementary explanations, especially regarding triangle congruence."

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The server collects curriculum guidelines and exam trend information from online resources and educational institutions. It then formats this data and stores it in a database. Input is from external data sources, and output is formatted data. Specifically, the server performs data collection via APIs and CSV file analysis.

[0257] Step 2:

[0258] The terminal provides an interface for users to input progress information and assignment details for their lessons. This information is converted into a format that the system can understand and sent to the server. The input is educational data entered by the user, and the output is data converted into a server-readable format. Specifically, the form input data is converted into JSON format and sent to the server.

[0259] Step 3:

[0260] The server analyzes educational data sent from terminals by comparing it with existing data in the database. This process utilizes a generative AI model to identify particularly critical learning points. The input consists of educational data sent from terminals and instructional standards and exam trend information from the database, while the output is the analysis results. Specifically, text analysis and pattern recognition are performed by the AI ​​model.

[0261] Step 4:

[0262] The server automatically generates the next lesson plan based on the analysis results. Using a generative AI model, it creates a detailed plan including teaching content and practice problems. The input is the analysis results, and the output is the next lesson plan. Specifically, a language generation model generates the plan's text and suggests necessary materials.

[0263] Step 5:

[0264] The terminal receives lesson plans sent from the server and displays them to the user. The user can review and edit them as needed. The input is the lesson plan from the server, and the output is an interface that the user can view and edit. Specifically, the plan content is displayed as HTML on the terminal screen, and editing functions are provided.

[0265] Step 6:

[0266] Users input the results of their lessons into their terminals. Feedback is sent to the server and used for analysis and planning for future lessons. The input is user feedback, and the output is feedback data for the server. Specifically, comments and evaluation data are sent to the server via an input form.

[0267] Step 7:

[0268] The server uses the received feedback to improve the overall accuracy of the system. Machine learning algorithms are utilized to help generate the next lesson plan. The input is feedback data, and the output is an improved analytical model. Specifically, feedback is added to the dataset, and the AI ​​model is retrained.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] In recent years, educational settings have required the understanding and individualization of diverse student levels. However, conventional educational support systems primarily focus on the automatic generation of curricula and lack mechanisms to evaluate students' understanding and concentration levels in real time and support flexible lesson adjustments based on that evaluation. As a result, teachers have been unable to properly grasp the understanding of each student, making effective lesson management difficult.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes means for receiving educational information provided by the user, means for analyzing the educational information by comparing it with existing curriculum and external examination trend data, and means for evaluating the learner's level of understanding using emotion recognition technology and supporting the adjustment of educational activities based on this evaluation. This makes it possible to grasp the student's level of understanding in real time and flexibly adjust the lesson plan.

[0274] A "user" is an entity that provides educational information and refers to the generated educational activity plans.

[0275] "Educational information" refers to information that includes data on curriculum progress and students' learning status.

[0276] "Existing curriculum" refers to the standard course content and objectives set by an educational institution.

[0277] "External exam trend data" refers to information regarding the content and frequency of questions asked in past exams.

[0278] "Emotion recognition technology" is a technology that estimates emotions from a learner's facial expressions and voice, and evaluates their level of comprehension and concentration.

[0279] The "Educational Activity Plan" will include specific guidelines regarding the progress and content of lessons.

[0280] The system implementing this invention primarily operates with a server at its core. The server receives educational information from users and analyzes this data by comparing it with existing curriculum and external exam trend data. In this process, it performs large-scale data processing using a database management system and machine learning algorithms.

[0281] The server also utilizes emotion recognition technology to evaluate learners' comprehension levels from data provided by users. This technology includes image processing software and voice analysis software for facial expressions and voice analysis. This enables the generation of educational activity plans tailored to the user, supporting more effective lesson management.

[0282] The terminal is a device used by the user and is responsible for presenting the generated educational activity plan to the user. The terminal also allows for the review and editing of the generated plan, and users can send additional feedback to the server. In this way, educational information is entered and feedback is provided in real time via the terminal.

[0283] As a specific example, this system includes a system where a teacher wears a smart wearable device and can observe the understanding and concentration levels of students on the spot. An example of a prompt sentence is "Please evaluate the engagement level of students based on facial expressions and body movements." Using this, a teaching plan can be generated according to the situation of the students, and the system can be continuously improved based on the necessary feedback.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The server receives educational information from the terminal. This educational information includes the progress of the curriculum input by the user and the learning status of students. The server stores this information in the database and utilizes it as the basic data for the next analysis.

[0287] Step 2:

[0288] The server analyzes the received educational information by comparing it with the existing curriculum and the trend data of external tests. Using a database management system, relevant data is retrieved, and data processing for generating an optimal educational plan is performed by applying machine learning algorithms. As an output result, an analysis report on the current understanding level of students is generated.

[0289] Step 3:

[0290] The server utilizes emotion recognition technology using a generated AI model to evaluate the understanding level of learners. Specifically, by using an expression analysis program and voice recognition software to analyze the expressions and speaking styles of learners from the received data, the emotional state of the learners is determined. The input is the voice and expression data of students, and the output is an emotion evaluation score.

[0291] Step 4:

[0292] The terminal displays a plan of educational activities sent from the server to the user. The user uses this as a guide when conducting lessons. An interface has been developed on the terminal, allowing the user to review the plan and edit the necessary parts.

[0293] Step 5:

[0294] After conducting a lesson, users input feedback into the server via their device. This feedback includes information about the effectiveness of the lesson and student responses. The server uses the received feedback for subsequent data analysis to further optimize future educational activities.

[0295] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0296] This invention provides a system that supports educational institutions and teachers in creating lesson plans more effectively, and in particular, by integrating an emotion engine, it enables lesson plans that take the user's emotions into account. This system, including the emotion engine, consists of three components: a server, a terminal, and a user.

[0297] Server: As a central processing unit, the server receives educational information, feedback, and user emotion data recognized by the emotion engine, and stores this information in a database. This database also stores curriculum guidelines and trend data from past external examinations. Based on the received information, the server analyzes the educational information and generates the next lesson plan while taking the user's emotions into consideration. This adjusts the plan to better suit the user.

[0298] Terminal: The terminal provides an interface for users to input educational information and interact with the server. Users input their lesson progress here, and the terminal activates an emotion engine to analyze the user's emotions at the time of input. The analysis results are sent to the server and reflected in the next lesson plan. Furthermore, the terminal displays the plan provided by the server, allowing the user to review its contents.

[0299] User: The user, who is a teacher, inputs information about the progress of the lesson and their own emotions into the terminal. For example, they identify difficulties in the lesson or emotional stress points and share them with the system via the terminal. This allows the server to provide the next lesson plan based on the emotional data.

[0300] As a concrete example, consider a scenario where a user inputs information into their terminal, such as "I felt stressed because students weren't understanding the geometry lesson in the second year," along with "stress" data detected by the emotion engine. The server analyzes this information and generates a plan that includes specific approaches to deepen students' understanding in the next lesson. This plan also takes into account effective teaching methods to reduce stress.

[0301] This system allows for more personalized educational activities and enables them to proceed in a way that also takes into account the well-being of the teachers themselves.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] After each lesson, users input information into their device about the lesson content, students' understanding, and any stress or emotions they experienced. This data entry is intended to provide a detailed record of specific events and emotions.

[0305] Step 2:

[0306] The terminal automatically sends the input educational information to the emotion engine to analyze the user's emotion. This analysis infers the emotion from text data and the user's behavior at the time of input.

[0307] Step 3:

[0308] The terminal sends the emotion data obtained as the analysis result to the server together with the educational information. This transmitted data includes information such as "Lesson progress: behind", "Emotion: stressed".

[0309] Step 4:

[0310] The server collates the received educational information and emotion data with the existing curriculum guidelines and the trend data of external examinations, and analyzes the progress and deficiencies of the educational content.

[0311] Step 5:

[0312] Based on the analysis result, the server automatically generates the next lesson plan. Here, the user's emotion data is also considered so that the plan includes measures to reduce the stress felt by the user.

[0313] Step 6:

[0314] The generated lesson plan is sent from the server to the terminal and presented to the user as the next lesson plan. The user can confirm this and make lesson preparations based on the plan.

[0315] Step 7:

[0316] After the lesson is conducted, the user inputs emotion information including feedback again and sends it to the server. The server uses this to help improve the next or future lesson plans.

[0317] (Example 2)

[0318] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0319] Traditional educational planning systems generated educational plans based solely on information provided by educators, thus failing to adequately consider the emotional aspects of educators. This led to increased psychological burden on educators and potentially a decline in the quality of education. Furthermore, the generated plans were rigid and unable to flexibly adapt to changing circumstances.

[0320] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0321] In this invention, the server includes means for receiving educational information and emotional data provided by the user, means for analyzing the educational information and emotional data by comparing them with existing educational policy and evaluation indicator data, and means for generating a plan for the next educational instruction based on the analysis. This makes it possible to generate more personalized educational plans that take into account the emotions of educators, thereby reducing the burden on educators and improving the quality of education.

[0322] "Educational information provided by users" refers to information about educators' lesson plans and lesson content.

[0323] "Emotional data" refers to data that expresses the emotional state of educators when conducting lessons as numerical values ​​or categories.

[0324] "Existing educational policies" refer to the guidelines for curricula and teaching methods established by educational institutions.

[0325] "Evaluation indicator data" refers to data such as students' learning achievement levels based on past exams and evaluations.

[0326] "Means of analysis" refers to the technologies and methods used to analyze collected educational information and emotional data and process them for use in future planning.

[0327] "Means for generating educational guidance plans" refers to systems and methods for planning the next lesson or instructional content based on input data and analysis results from educators.

[0328] A "sentiment analysis engine" refers to a software module that analyzes and quantifies or categorizes emotional states from input by educators.

[0329] This embodiment of the invention is an educational planning system for supporting educators, and consists of three components: a server, a terminal, and a user. Specifically, the server functions as a central processing unit and receives and analyzes the various types of data described below.

[0330] server:

[0331] The server receives educational information and emotional data provided by educators via their devices. During this process, an emotional analysis engine detects the educators' emotions and quantifies the results. The server stores the received data in a database and analyzes it using Python data science tools. Furthermore, it utilizes a generative AI model to generate educational guidance plans that take the educators' emotions into account. Specifically, general natural language generation tools can be used for the AI ​​model.

[0332] Terminal:

[0333] The terminal is used by educators and provides an interface for inputting educational information and emotional data. An emotional analysis engine operates on the terminal to analyze the input emotions. Furthermore, it displays educational instruction plans sent from the server, allowing educators to review these plans and provide feedback. This feedback is then incorporated into subsequent analyses.

[0334] User:

[0335] The user, an educator, inputs their own emotions along with educational information into the terminal. For example, they input self-assessments and emotional states based on students' understanding in class. As a specific example, they might input a situation such as, "Students were slow to understand in today's lesson, and I felt insecure about my explanation methods," and emotional data based on that would be generated. It is also assumed that the generating AI model would be prompted with a message such as, "In the educational system, please propose a lesson plan that takes into account the user's emotions, such as experiencing a decline in student motivation in recent lessons and feeling stressed about it."

[0336] This embodiment allows educators to receive an optimal educational plan that takes their own emotional state into account, and is expected to lead to more personalized educational activities.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] Users input educational information and emotional data through their devices. Specifically, they input text about the progress of lessons and their own feelings (e.g., "Students showed no motivation, which made me anxious"). This input data forms the basis for the next step.

[0340] Step 2:

[0341] The terminal receives the input educational information and emotional data, and uses an emotional analysis engine to analyze the emotional data. The emotional analysis engine converts the emotions from the input text into numerical values ​​and outputs "anxiety" as an emotional score as a result of the analysis. The emotional data is then sent to the server in an analyzed state.

[0342] Step 3:

[0343] The server receives educational information and quantified sentiment data transmitted from terminals. The server stores this data in a database and performs detailed data analysis by comparing it with existing educational policy and evaluation metric data. Using Python data science tools, it conducts trend and correlation analyses of the data and generates future educational guidance plans based on the analysis results.

[0344] Step 4:

[0345] The server uses a generative AI model to generate educational guidance plans based on educational information and analysis results stored on the server. Specifically, it inputs a prompt message to the generative AI model (e.g., a general natural language generation tool) such as, "In the educational system, please propose a lesson plan that takes into account the feelings of the user who recently experienced a decline in student motivation in a lesson and is feeling stressed about it," and outputs a plan that is sensitive to the educator's feelings.

[0346] Step 5:

[0347] The server sends the generated teaching plan to the terminal. This plan incorporates the previous analysis results and suggestions from the generated AI model, and is adjusted to be meaningful for educators.

[0348] Step 6:

[0349] The terminal displays the received lesson plan to the user. The user can review the plan and enter feedback into the terminal as needed. This feedback will be used to improve future lesson plans. Furthermore, the feedback information will be used as input for the next data analysis cycle, contributing to the overall improvement of the system's accuracy.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0352] In educational settings, teachers face the challenge of not being able to consider individual students' learning progress, understanding, and emotional state when creating lesson plans. In particular, there is a need to grasp students' responses and interests to learning in real time and adjust lesson content accordingly, but the current system has limited means of doing this efficiently.

[0353] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0354] In this invention, the server includes means for receiving educational information provided by the user, means for analyzing the educational information by comparing it with existing curriculum and external examination trend data, means for generating a plan for the next educational activity based on the analysis, and means for analyzing the user's emotional state and providing real-time feedback. This enables teachers in educational settings to create and present individually optimized lesson plans in real time based on students' emotions and level of understanding.

[0355] "Users" are entities that provide educational information using the system, and primarily refer to teachers.

[0356] "Educational information" is a general term for various data and materials related to education provided by users, and includes information such as students' learning progress and understanding.

[0357] A "curriculum" is a set of educational content and learning plans organized to achieve specific educational objectives.

[0358] "External exam trend data" refers to data on past exam questions and their question trends, and is information used to predict future exam questions.

[0359] "Analysis" is the process of comparing received educational information with existing data and interpreting and evaluating its content based on certain rules.

[0360] An "educational activity plan" is a plan that defines the specific content and methods of lessons to effectively promote students' learning.

[0361] "Emotional state" refers to the user's psychological state and emotional fluctuations, and includes information about internal reactions such as stress, anxiety, and relaxation.

[0362] A "means of providing real-time feedback" refers to a function that instantly returns information about the user's feelings and level of understanding, and provides advice and instructions that are useful for the progress of the lesson.

[0363] In implementing this invention, a specific system will be developed through the following hardware and software configuration. The main components of the system are a server, a terminal, and a user.

[0364] The server functions as a central processing unit, receiving educational information from users and analyzing it by comparing it with existing curriculum and external exam trend data. Based on this analysis, it generates a plan for the next educational activity. The generated plan is adjusted to take into account the user's emotional state. Emotional analysis will utilize an emotion recognition library. By using this library, the teacher's stress, anxiety, and relaxation levels will be identified, and this information will be reflected in the lesson plan.

[0365] The terminal provides an interface for users to input educational information and feedback. Wearable devices such as smart glasses are connected to the terminal and have the capability to collect and analyze real-time emotional states. These devices extract emotional data from the user's own recordings and facial expressions and send it to the server. As a result, users can receive immediate, emotion-based feedback.

[0366] Teachers, as users, input their emotions and lesson progress into their devices, receive feedback from the system, and use it to plan their next lessons. This allows teachers to gain concrete approaches to conducting better educational activities.

[0367] For example, if a teacher feels that students are not responding well in a math class, they can immediately input their thoughts and feelings into a terminal. Based on this information, the server can suggest alternative approaches, such as "problem-solving learning," for the next class. An example of a prompt provided by the AI ​​model could be: "Generate specific teaching methods to improve student comprehension in a 2nd-year geometry class and reduce the teacher's anxiety."

[0368] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0369] Step 1:

[0370] The terminal receives input from the user. This input includes information on the progress of the lesson and the user's self-reported emotional state. The terminal formats this data and preprocesses it for transmission to the server.

[0371] Step 2:

[0372] The server receives educational information and sentiment data transmitted from the terminal. The received data is analyzed using an educational information database and a sentiment recognition library. In this process, educational information is compared with existing curriculum and external exam trend data, and sentiment data is analyzed by sentiment recognition algorithms.

[0373] Step 3:

[0374] The server generates a plan for the next educational activity based on the analysis results. This plan generation uses an AI-based recommendation algorithm, and the generated plan is customized to take into account the user's emotional state. In this process, the generating AI model is used to create appropriate prompt sentences and make specific suggestions such as, "Generate specific teaching methods to improve student understanding in 2nd-year geometry lessons and reduce teacher anxiety."

[0375] Step 4:

[0376] The server sends the generated educational activity plan to the terminal. The terminal displays this plan to the user. At this time, the user can review the plan and provide additional feedback as needed. The feedback is received and processed by the server again and used for future planning.

[0377] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0378] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0383] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0384] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0385] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0386] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0387] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0388] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0389] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0391] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0392] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0393] This invention provides a support system for educational institutions and individual teachers to efficiently create lesson plans. This system is comprised of a server, terminals, and users.

[0394] Server: The server, which forms the core of the system, has the function of importing curriculum guidelines and trend data from past external examinations and storing them in a database. It receives educational information sent by users and performs processing to compare and analyze it with existing data. Based on the analysis results, it automatically generates the next lesson plan and improves the accuracy of the system by accepting feedback. It also sends the generated lesson plan to the user's terminal so that they can check it.

[0395] Terminal: The terminal is a device used by the user, providing an interface for inputting class progress information and assignments and sending them to the server. It also displays the next class plan sent from the server, allowing the user to review and edit it. Feedback can also be entered and resent to the server to help improve the plan.

[0396] User: As a teacher, the user is responsible for inputting daily lesson progress and learning content into the system. They receive the generated lesson plan and conduct lessons based on it. They also contribute to further optimization of future lesson plans by inputting feedback on the results after implementation.

[0397] As a concrete example, consider a scenario where a user inputs "Lesson progress on geometry for 2nd-year mathematics" into their terminal. The user reports to the system that they taught the congruence conditions for triangles, but the student is having difficulty understanding them. The server receives this information, identifies the points of insufficient understanding based on the curriculum guidelines, and, comparing them with data on high school entrance exam trends, creates a plan to improve the content for the next lesson. This plan includes specific explanations and practice problems, which are provided to the user via the terminal. The user can refer to the plan and use it to prepare for the next lesson.

[0398] In this way, the system aims to reduce the burden on teachers and support the implementation of more efficient and effective lessons.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] After each lesson, users input educational information such as "lesson content," "student comprehension level," and "areas for next lesson" into their devices. This input data includes specific topics and student responses.

[0402] Step 2:

[0403] The terminal sends educational information entered by the user to the server. This transmission occurs in real time or at specified intervals via the network.

[0404] Step 3:

[0405] The server temporarily stores the received educational information in a database. This database also stores past lesson data and trend data from external examinations.

[0406] Step 4:

[0407] The server uses an analysis module to compare the stored educational information with the curriculum guidelines and evaluate the extent to which the content and progress of the lessons are meeting the objectives.

[0408] Step 5:

[0409] The server compares and analyzes data from external exams to determine which topics and exercises should be given particular emphasis in the next class.

[0410] Step 6:

[0411] The server generates an automated lesson plan based on the analysis results. This plan includes specific lesson content, recommended materials, and teaching methods.

[0412] Step 7:

[0413] The generated lesson plan is sent from the server to the terminal and presented to the user. The user then uses this to prepare for the next lesson.

[0414] Step 8:

[0415] After the lesson, users enter new feedback into their terminals and send it back to the server. This feedback is used to improve the next lesson plan.

[0416] (Example 1)

[0417] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0418] For educational institutions and teachers to create lesson plans quickly and effectively, data collection and analysis from diverse sources are necessary. However, these tasks have traditionally been very time-consuming and labor-intensive, increasing the burden on teachers. Furthermore, creating plans that take exam trends into account is complex and highly dependent on the experience of individual teachers. In addition, it has been difficult to effectively incorporate post-lesson feedback into future plans, hindering improvements in the quality of instruction.

[0419] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0420] In this invention, the server includes means for receiving educational data provided by the user, means for analyzing the educational data by comparing it with existing teaching standards and test trend information, and means for automatically generating a plan for the next educational activity based on the analysis using a generative AI model. This enables educational institutions and teachers to efficiently utilize data, quickly create lesson plans that take test trends into consideration, and incorporate feedback into the next plan.

[0421] "Educational data" is a general term for information provided by users regarding lesson progress and learning content.

[0422] "Instructional standards" refer to the criteria set by educational institutions that outline the learning curriculum and learning objectives.

[0423] "Exam trend information" refers to information about past exam question patterns and important points.

[0424] A "generative AI model" is a model that uses artificial intelligence to perform data analysis and plan generation.

[0425] An "educational activity plan" refers to a plan for the progress of lessons and learning that is structured to achieve specific learning objectives.

[0426] "Feedback" refers to information provided by users regarding their evaluations and reactions after a lesson has been conducted.

[0427] This invention provides a system for educational institutions and teachers to efficiently create lesson plans. The embodiments for carrying out the invention are described below.

[0428] Server: The server is the core computer device of the system. It collects curriculum guidelines and past exam trend data and stores them in a database. It receives educational data, analyzes it using a generation AI model, and automatically generates the next lesson plan. The server also receives feedback from users and uses it to optimize the plan. Standard server hardware and data analysis software such as Python and R are used for calculations performed by the server.

[0429] Terminals: Terminals are digital devices used by users, including personal computers, tablets, and smartphones. Users input information about class progress and assignments via their terminals and send it to the server. The server displays the received next class plan and provides an interface that users can review and edit. Specifically, a web browser or dedicated application is used as the interface.

[0430] User: The user is an educator and is responsible for inputting daily lesson content into the terminal. They refer to the next lesson plan and use it to prepare for the lesson. After the lesson, they input the results as feedback to help improve the next plan.

[0431] As a concrete example, consider a scenario where a user inputs "Lesson progress on geometry for 2nd-year mathematics" into their terminal and reports to the system that they taught the congruence conditions for triangles, but the students' understanding was insufficient. The server receives this information and performs a detailed analysis based on teaching standards and exam trend information. As a result, a new lesson plan is automatically generated and provided to the user via the terminal. This plan includes supplementary explanations and additional practice problems, which the user can use to prepare for lessons.

[0432] In this way, the present invention supports the creation of efficient and effective lesson plans for educational institutions and teachers, thereby reducing the burden on teachers. An example of a prompt message would be, "Create a lesson plan for 2nd-year mathematics. Add supplementary explanations, especially regarding triangle congruence."

[0433] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0434] Step 1:

[0435] The server collects curriculum guidelines and exam trend information from online resources and educational institutions. It then formats this data and stores it in a database. Input is from external data sources, and output is formatted data. Specifically, the server performs data collection via APIs and CSV file analysis.

[0436] Step 2:

[0437] The terminal provides an interface for users to input progress information and assignment details for their lessons. This information is converted into a format that the system can understand and sent to the server. The input is educational data entered by the user, and the output is data converted into a server-readable format. Specifically, the form input data is converted into JSON format and sent to the server.

[0438] Step 3:

[0439] The server analyzes educational data sent from terminals by comparing it with existing data in the database. This process utilizes a generative AI model to identify particularly critical learning points. The input consists of educational data sent from terminals and instructional standards and exam trend information from the database, while the output is the analysis results. Specifically, text analysis and pattern recognition are performed by the AI ​​model.

[0440] Step 4:

[0441] The server automatically generates the next lesson plan based on the analysis results. Using a generative AI model, it creates a detailed plan including teaching content and practice problems. The input is the analysis results, and the output is the next lesson plan. Specifically, a language generation model generates the plan's text and suggests necessary materials.

[0442] Step 5:

[0443] The terminal receives lesson plans sent from the server and displays them to the user. The user can review and edit them as needed. The input is the lesson plan from the server, and the output is an interface that the user can view and edit. Specifically, the plan content is displayed as HTML on the terminal screen, and editing functions are provided.

[0444] Step 6:

[0445] Users input the results of their lessons into their terminals. Feedback is sent to the server and used for analysis and planning for future lessons. The input is user feedback, and the output is feedback data for the server. Specifically, comments and evaluation data are sent to the server via an input form.

[0446] Step 7:

[0447] The server uses the received feedback to improve the overall accuracy of the system. Machine learning algorithms are utilized to help generate the next lesson plan. The input is feedback data, and the output is an improved analytical model. Specifically, feedback is added to the dataset, and the AI ​​model is retrained.

[0448] (Application Example 1)

[0449] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0450] In recent years, educational settings have required the understanding and individualization of diverse student levels. However, conventional educational support systems primarily focus on the automatic generation of curricula and lack mechanisms to evaluate students' understanding and concentration levels in real time and support flexible lesson adjustments based on that evaluation. As a result, teachers have been unable to properly grasp the understanding of each student, making effective lesson management difficult.

[0451] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0452] In this invention, the server includes means for receiving educational information provided by the user, means for analyzing the educational information by comparing it with existing curriculum and external examination trend data, and means for evaluating the learner's level of understanding using emotion recognition technology and supporting the adjustment of educational activities based on this evaluation. This makes it possible to grasp the student's level of understanding in real time and flexibly adjust the lesson plan.

[0453] A "user" is an entity that provides educational information and refers to the generated educational activity plans.

[0454] "Educational information" refers to information that includes data on curriculum progress and students' learning status.

[0455] "Existing curriculum" refers to the standard course content and objectives set by an educational institution.

[0456] "External exam trend data" refers to information regarding the content and frequency of questions asked in past exams.

[0457] "Emotion recognition technology" is a technology that estimates emotions from a learner's facial expressions and voice, and evaluates their level of comprehension and concentration.

[0458] The "Educational Activity Plan" will include specific guidelines regarding the progress and content of lessons.

[0459] The system implementing this invention primarily operates with a server at its core. The server receives educational information from users and analyzes this data by comparing it with existing curriculum and external exam trend data. In this process, it performs large-scale data processing using a database management system and machine learning algorithms.

[0460] The server also utilizes emotion recognition technology to evaluate learners' comprehension levels from data provided by users. This technology includes image processing software and voice analysis software for facial expressions and voice analysis. This enables the generation of educational activity plans tailored to the user, supporting more effective lesson management.

[0461] The terminal is a device used by the user and is responsible for presenting the generated educational activity plan to the user. The terminal also allows for the review and editing of the generated plan, and users can send additional feedback to the server. In this way, educational information is entered and feedback is provided in real time via the terminal.

[0462] This system includes, as a concrete example, a system in which teachers wear smart wearable devices to monitor students' understanding and concentration levels in real time. An example of a prompt message is, "Please evaluate the students' level of engagement based on their facial expressions and body movements." This can be used to generate lesson plans tailored to the students' situations, and the system can be continuously improved based on the feedback provided as needed.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The server receives educational information from the terminal. This educational information includes the progress of the curriculum entered by the user and the learning status of students. The server stores this information in a database and uses it as basic data for subsequent analysis.

[0466] Step 2:

[0467] The server analyzes the received educational information by comparing it with existing curriculum and external exam trend data. Using a database management system, it searches for relevant data and applies machine learning algorithms to process the data for creating optimal educational plans. As an output, it generates an analytical report on students' current level of understanding.

[0468] Step 3:

[0469] The server uses generative AI models and emotion recognition technology to evaluate learners' comprehension. Specifically, it utilizes facial expression analysis programs and speech recognition software to analyze learners' facial expressions and speaking style from received data, thereby determining their emotional state. The input is student voice and facial expression data, and the output is an emotion evaluation score.

[0470] Step 4:

[0471] The terminal displays a plan of educational activities sent from the server to the user. The user uses this as a guide when conducting lessons. An interface has been developed on the terminal, allowing the user to review the plan and edit the necessary parts.

[0472] Step 5:

[0473] After conducting a lesson, users input feedback into the server via their device. This feedback includes information about the effectiveness of the lesson and student responses. The server uses the received feedback for subsequent data analysis to further optimize future educational activities.

[0474] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0475] This invention provides a system that supports educational institutions and teachers in creating lesson plans more effectively, and in particular, by integrating an emotion engine, it enables lesson plans that take the user's emotions into account. This system, including the emotion engine, consists of three components: a server, a terminal, and a user.

[0476] Server: As a central processing unit, the server receives educational information, feedback, and user emotion data recognized by the emotion engine, and stores this information in a database. This database also stores curriculum guidelines and trend data from past external examinations. Based on the received information, the server analyzes the educational information and generates the next lesson plan while taking the user's emotions into consideration. This adjusts the plan to better suit the user.

[0477] Terminal: The terminal provides an interface for users to input educational information and interact with the server. Users input their lesson progress here, and the terminal activates an emotion engine to analyze the user's emotions at the time of input. The analysis results are sent to the server and reflected in the next lesson plan. Furthermore, the terminal displays the plan provided by the server, allowing the user to review its contents.

[0478] User: The user, who is a teacher, inputs information about the progress of the lesson and their own emotions into the terminal. For example, they identify difficulties in the lesson or emotional stress points and share them with the system via the terminal. This allows the server to provide the next lesson plan based on the emotional data.

[0479] As a concrete example, consider a scenario where a user inputs information into their terminal, such as "I felt stressed because students weren't understanding the geometry lesson in the second year," along with "stress" data detected by the emotion engine. The server analyzes this information and generates a plan that includes specific approaches to deepen students' understanding in the next lesson. This plan also takes into account effective teaching methods to reduce stress.

[0480] This system allows for more personalized educational activities and enables them to proceed in a way that also takes into account the well-being of the teachers themselves.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] After each lesson, users input information into their device about the lesson content, students' understanding, and any stress or emotions they experienced. This data entry is intended to provide a detailed record of specific events and emotions.

[0484] Step 2:

[0485] The device automatically sends the entered educational information to an emotion engine to analyze the user's emotions. This analysis infers emotions from text data and the user's actions during input.

[0486] Step 3:

[0487] The device sends the emotional data obtained as a result of the analysis to the server along with educational information. This transmitted data includes information such as "Lesson progress: Delayed" and "Emotion: Stress."

[0488] Step 4:

[0489] The server compares the received educational information and sentiment data with existing curriculum guidelines and external examination trend data to analyze the progress and areas of deficiency in educational content.

[0490] Step 5:

[0491] The server automatically generates the next lesson plan based on the analysis results. This process also takes user emotional data into consideration, ensuring the plan includes measures to alleviate the stress the user experienced.

[0492] Step 6:

[0493] The generated lesson plan is sent from the server to the terminal and presented to the user as the plan for the next lesson. The user can then review it and prepare for the lesson based on the plan.

[0494] Step 7:

[0495] After the lesson is conducted, users again input sentiment information, including feedback, and send it to the server. The server uses this information to improve future lesson plans.

[0496] (Example 2)

[0497] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] Traditional educational planning systems generated educational plans based solely on information provided by educators, thus failing to adequately consider the emotional aspects of educators. This led to increased psychological burden on educators and potentially a decline in the quality of education. Furthermore, the generated plans were rigid and unable to flexibly adapt to changing circumstances.

[0499] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0500] In this invention, the server includes means for receiving educational information and emotional data provided by the user, means for analyzing the educational information and emotional data by comparing them with existing educational policy and evaluation indicator data, and means for generating a plan for the next educational instruction based on the analysis. This makes it possible to generate more personalized educational plans that take into account the emotions of educators, thereby reducing the burden on educators and improving the quality of education.

[0501] "Educational information provided by users" refers to information about educators' lesson plans and lesson content.

[0502] "Emotional data" refers to data that expresses the emotional state of educators when conducting lessons as numerical values ​​or categories.

[0503] "Existing educational policies" refer to the guidelines for curricula and teaching methods established by educational institutions.

[0504] "Evaluation indicator data" refers to data such as students' learning achievement levels based on past exams and evaluations.

[0505] "Means of analysis" refers to the technologies and methods used to analyze collected educational information and emotional data and process them for use in future planning.

[0506] "Means for generating educational guidance plans" refers to systems and methods for planning the next lesson or instructional content based on input data and analysis results from educators.

[0507] A "sentiment analysis engine" refers to a software module that analyzes and quantifies or categorizes emotional states from input by educators.

[0508] This embodiment of the invention is an educational planning system for supporting educators, and consists of three components: a server, a terminal, and a user. Specifically, the server functions as a central processing unit and receives and analyzes the various types of data described below.

[0509] server:

[0510] The server receives educational information and emotional data provided by educators via their devices. During this process, an emotional analysis engine detects the educators' emotions and quantifies the results. The server stores the received data in a database and analyzes it using Python data science tools. Furthermore, it utilizes a generative AI model to generate educational guidance plans that take the educators' emotions into account. Specifically, general natural language generation tools can be used for the AI ​​model.

[0511] Terminal:

[0512] The terminal is used by educators and provides an interface for inputting educational information and emotional data. An emotional analysis engine operates on the terminal to analyze the input emotions. Furthermore, it displays educational instruction plans sent from the server, allowing educators to review these plans and provide feedback. This feedback is then incorporated into subsequent analyses.

[0513] User:

[0514] The user, an educator, inputs their own emotions along with educational information into the terminal. For example, they input self-assessments and emotional states based on students' understanding in class. As a specific example, they might input a situation such as, "Students were slow to understand in today's lesson, and I felt insecure about my explanation methods," and emotional data based on that would be generated. It is also assumed that the generating AI model would be prompted with a message such as, "In the educational system, please propose a lesson plan that takes into account the user's emotions, such as experiencing a decline in student motivation in recent lessons and feeling stressed about it."

[0515] This embodiment allows educators to receive an optimal educational plan that takes their own emotional state into account, and is expected to lead to more personalized educational activities.

[0516] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0517] Step 1:

[0518] Users input educational information and emotional data through their devices. Specifically, they input text about the progress of lessons and their own feelings (e.g., "Students showed no motivation, which made me anxious"). This input data forms the basis for the next step.

[0519] Step 2:

[0520] The terminal receives the input educational information and emotional data, and uses an emotional analysis engine to analyze the emotional data. The emotional analysis engine converts the emotions from the input text into numerical values ​​and outputs "anxiety" as an emotional score as a result of the analysis. The emotional data is then sent to the server in an analyzed state.

[0521] Step 3:

[0522] The server receives educational information and quantified sentiment data transmitted from terminals. The server stores this data in a database and performs detailed data analysis by comparing it with existing educational policy and evaluation metric data. Using Python data science tools, it conducts trend and correlation analyses of the data and generates future educational guidance plans based on the analysis results.

[0523] Step 4:

[0524] The server uses a generative AI model to generate educational guidance plans based on educational information and analysis results stored on the server. Specifically, it inputs a prompt message to the generative AI model (e.g., a general natural language generation tool) such as, "In the educational system, please propose a lesson plan that takes into account the feelings of the user who recently experienced a decline in student motivation in a lesson and is feeling stressed about it," and outputs a plan that is sensitive to the educator's feelings.

[0525] Step 5:

[0526] The server sends the generated teaching plan to the terminal. This plan incorporates the previous analysis results and suggestions from the generated AI model, and is adjusted to be meaningful for educators.

[0527] Step 6:

[0528] The terminal displays the received lesson plan to the user. The user can review the plan and enter feedback into the terminal as needed. This feedback will be used to improve future lesson plans. Furthermore, the feedback information will be used as input for the next data analysis cycle, contributing to the overall improvement of the system's accuracy.

[0529] (Application Example 2)

[0530] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0531] In educational settings, teachers face the challenge of not being able to consider individual students' learning progress, understanding, and emotional state when creating lesson plans. In particular, there is a need to grasp students' responses and interests to learning in real time and adjust lesson content accordingly, but the current system has limited means of doing this efficiently.

[0532] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0533] In this invention, the server includes means for receiving educational information provided by the user, means for analyzing the educational information by comparing it with existing curriculum and external examination trend data, means for generating a plan for the next educational activity based on the analysis, and means for analyzing the user's emotional state and providing real-time feedback. This enables teachers in educational settings to create and present individually optimized lesson plans in real time based on students' emotions and level of understanding.

[0534] "Users" are entities that provide educational information using the system, and primarily refer to teachers.

[0535] "Educational information" is a general term for various data and materials related to education provided by users, and includes information such as students' learning progress and understanding.

[0536] A "curriculum" is a set of educational content and learning plans organized to achieve specific educational objectives.

[0537] "External exam trend data" refers to data on past exam questions and their question trends, and is information used to predict future exam questions.

[0538] "Analysis" is the process of comparing received educational information with existing data and interpreting and evaluating its content based on certain rules.

[0539] An "educational activity plan" is a plan that defines the specific content and methods of lessons to effectively promote students' learning.

[0540] "Emotional state" refers to the user's psychological state and emotional fluctuations, and includes information about internal reactions such as stress, anxiety, and relaxation.

[0541] A "means of providing real-time feedback" refers to a function that instantly returns information about the user's feelings and level of understanding, and provides advice and instructions that are useful for the progress of the lesson.

[0542] In implementing this invention, a specific system will be developed through the following hardware and software configuration. The main components of the system are a server, a terminal, and a user.

[0543] The server functions as a central processing unit, receiving educational information from users and analyzing it by comparing it with existing curriculum and external exam trend data. Based on this analysis, it generates a plan for the next educational activity. The generated plan is adjusted to take into account the user's emotional state. Emotional analysis will utilize an emotion recognition library. By using this library, the teacher's stress, anxiety, and relaxation levels will be identified, and this information will be reflected in the lesson plan.

[0544] The terminal provides an interface for users to input educational information and feedback. Wearable devices such as smart glasses are connected to the terminal and have the capability to collect and analyze real-time emotional states. These devices extract emotional data from the user's own recordings and facial expressions and send it to the server. As a result, users can receive immediate, emotion-based feedback.

[0545] Teachers, as users, input their emotions and lesson progress into their devices, receive feedback from the system, and use it to plan their next lessons. This allows teachers to gain concrete approaches to conducting better educational activities.

[0546] For example, if a teacher feels that students are not responding well in a math class, they can immediately input their thoughts and feelings into a terminal. Based on this information, the server can suggest alternative approaches, such as "problem-solving learning," for the next class. An example of a prompt provided by the AI ​​model could be: "Generate specific teaching methods to improve student comprehension in a 2nd-year geometry class and reduce the teacher's anxiety."

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The terminal receives input from the user. This input includes information on the progress of the lesson and the user's self-reported emotional state. The terminal formats this data and preprocesses it for transmission to the server.

[0550] Step 2:

[0551] The server receives educational information and sentiment data transmitted from the terminal. The received data is analyzed using an educational information database and a sentiment recognition library. In this process, educational information is compared with existing curriculum and external exam trend data, and sentiment data is analyzed by sentiment recognition algorithms.

[0552] Step 3:

[0553] The server generates a plan for the next educational activity based on the analysis results. This plan generation uses an AI-based recommendation algorithm, and the generated plan is customized to take into account the user's emotional state. In this process, the generating AI model is used to create appropriate prompt sentences and make specific suggestions such as, "Generate specific teaching methods to improve student understanding in 2nd-year geometry lessons and reduce teacher anxiety."

[0554] Step 4:

[0555] The server sends the generated educational activity plan to the terminal. The terminal displays this plan to the user. At this time, the user can review the plan and provide additional feedback as needed. The feedback is received and processed by the server again and used for future planning.

[0556] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0557] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0558] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0559] [Fourth Embodiment]

[0560] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0561] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0562] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0563] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0564] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0565] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0566] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0567] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0568] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0569] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0570] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0571] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0572] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0573] This invention provides a support system for educational institutions and individual teachers to efficiently create lesson plans. This system is comprised of a server, terminals, and users.

[0574] Server: The server, which forms the core of the system, has the function of importing curriculum guidelines and trend data from past external examinations and storing them in a database. It receives educational information sent by users and performs processing to compare and analyze it with existing data. Based on the analysis results, it automatically generates the next lesson plan and improves the accuracy of the system by accepting feedback. It also sends the generated lesson plan to the user's terminal so that they can check it.

[0575] Terminal: The terminal is a device used by the user, providing an interface for inputting class progress information and assignments and sending them to the server. It also displays the next class plan sent from the server, allowing the user to review and edit it. Feedback can also be entered and resent to the server to help improve the plan.

[0576] User: As a teacher, the user is responsible for inputting daily lesson progress and learning content into the system. They receive the generated lesson plan and conduct lessons based on it. They also contribute to further optimization of future lesson plans by inputting feedback on the results after implementation.

[0577] As a concrete example, consider a scenario where a user inputs "Lesson progress on geometry for 2nd-year mathematics" into their terminal. The user reports to the system that they taught the congruence conditions for triangles, but the student is having difficulty understanding them. The server receives this information, identifies the points of insufficient understanding based on the curriculum guidelines, and, comparing them with data on high school entrance exam trends, creates a plan to improve the content for the next lesson. This plan includes specific explanations and practice problems, which are provided to the user via the terminal. The user can refer to the plan and use it to prepare for the next lesson.

[0578] In this way, the system aims to reduce the burden on teachers and support the implementation of more efficient and effective lessons.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] After each lesson, users input educational information such as "lesson content," "student comprehension level," and "areas for next lesson" into their devices. This input data includes specific topics and student responses.

[0582] Step 2:

[0583] The terminal sends educational information entered by the user to the server. This transmission occurs in real time or at specified intervals via the network.

[0584] Step 3:

[0585] The server temporarily stores the received educational information in a database. This database also stores past lesson data and trend data from external examinations.

[0586] Step 4:

[0587] The server uses an analysis module to compare the stored educational information with the curriculum guidelines and evaluate the extent to which the content and progress of the lessons are meeting the objectives.

[0588] Step 5:

[0589] The server compares and analyzes data from external exams to determine which topics and exercises should be given particular emphasis in the next class.

[0590] Step 6:

[0591] The server generates an automated lesson plan based on the analysis results. This plan includes specific lesson content, recommended materials, and teaching methods.

[0592] Step 7:

[0593] The generated lesson plan is sent from the server to the terminal and presented to the user. The user then uses this to prepare for the next lesson.

[0594] Step 8:

[0595] After the lesson, users enter new feedback into their terminals and send it back to the server. This feedback is used to improve the next lesson plan.

[0596] (Example 1)

[0597] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0598] For educational institutions and teachers to create lesson plans quickly and effectively, data collection and analysis from diverse sources are necessary. However, these tasks have traditionally been very time-consuming and labor-intensive, increasing the burden on teachers. Furthermore, creating plans that take exam trends into account is complex and highly dependent on the experience of individual teachers. In addition, it has been difficult to effectively incorporate post-lesson feedback into future plans, hindering improvements in the quality of instruction.

[0599] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0600] In this invention, the server includes means for receiving educational data provided by the user, means for analyzing the educational data by comparing it with existing teaching standards and test trend information, and means for automatically generating a plan for the next educational activity based on the analysis using a generative AI model. This enables educational institutions and teachers to efficiently utilize data, quickly create lesson plans that take test trends into consideration, and incorporate feedback into the next plan.

[0601] "Educational data" is a general term for information provided by users regarding lesson progress and learning content.

[0602] "Instructional standards" refer to the criteria set by educational institutions that outline the learning curriculum and learning objectives.

[0603] "Exam trend information" refers to information about past exam question patterns and important points.

[0604] A "generative AI model" is a model that uses artificial intelligence to perform data analysis and plan generation.

[0605] An "educational activity plan" refers to a plan for the progress of lessons and learning that is structured to achieve specific learning objectives.

[0606] "Feedback" refers to information provided by users regarding their evaluations and reactions after a lesson has been conducted.

[0607] This invention provides a system for educational institutions and teachers to efficiently create lesson plans. The embodiments for carrying out the invention are described below.

[0608] Server: The server is the core computer device of the system. It collects curriculum guidelines and past exam trend data and stores them in a database. It receives educational data, analyzes it using a generation AI model, and automatically generates the next lesson plan. The server also receives feedback from users and uses it to optimize the plan. Standard server hardware and data analysis software such as Python and R are used for calculations performed by the server.

[0609] Terminals: Terminals are digital devices used by users, including personal computers, tablets, and smartphones. Users input information about class progress and assignments via their terminals and send it to the server. The server displays the received next class plan and provides an interface that users can review and edit. Specifically, a web browser or dedicated application is used as the interface.

[0610] User: The user is an educator and is responsible for inputting daily lesson content into the terminal. They refer to the next lesson plan and use it to prepare for the lesson. After the lesson, they input the results as feedback to help improve the next plan.

[0611] As a concrete example, consider a scenario where a user inputs "Lesson progress on geometry for 2nd-year mathematics" into their terminal and reports to the system that they taught the congruence conditions for triangles, but the students' understanding was insufficient. The server receives this information and performs a detailed analysis based on teaching standards and exam trend information. As a result, a new lesson plan is automatically generated and provided to the user via the terminal. This plan includes supplementary explanations and additional practice problems, which the user can use to prepare for lessons.

[0612] In this way, the present invention supports the creation of efficient and effective lesson plans for educational institutions and teachers, thereby reducing the burden on teachers. An example of a prompt message would be, "Create a lesson plan for 2nd-year mathematics. Add supplementary explanations, especially regarding triangle congruence."

[0613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0614] Step 1:

[0615] The server collects curriculum guidelines and exam trend information from online resources and educational institutions. It then formats this data and stores it in a database. Input is from external data sources, and output is formatted data. Specifically, the server performs data collection via APIs and CSV file analysis.

[0616] Step 2:

[0617] The terminal provides an interface for users to input progress information and assignment details for their lessons. This information is converted into a format that the system can understand and sent to the server. The input is educational data entered by the user, and the output is data converted into a server-readable format. Specifically, the form input data is converted into JSON format and sent to the server.

[0618] Step 3:

[0619] The server analyzes educational data sent from terminals by comparing it with existing data in the database. This process utilizes a generative AI model to identify particularly critical learning points. The input consists of educational data sent from terminals and instructional standards and exam trend information from the database, while the output is the analysis results. Specifically, text analysis and pattern recognition are performed by the AI ​​model.

[0620] Step 4:

[0621] The server automatically generates the next lesson plan based on the analysis results. Using a generative AI model, it creates a detailed plan including teaching content and practice problems. The input is the analysis results, and the output is the next lesson plan. Specifically, a language generation model generates the plan's text and suggests necessary materials.

[0622] Step 5:

[0623] The terminal receives lesson plans sent from the server and displays them to the user. The user can review and edit them as needed. The input is the lesson plan from the server, and the output is an interface that the user can view and edit. Specifically, the plan content is displayed as HTML on the terminal screen, and editing functions are provided.

[0624] Step 6:

[0625] Users input the results of their lessons into their terminals. Feedback is sent to the server and used for analysis and planning for future lessons. The input is user feedback, and the output is feedback data for the server. Specifically, comments and evaluation data are sent to the server via an input form.

[0626] Step 7:

[0627] The server uses the received feedback to improve the overall accuracy of the system. Machine learning algorithms are utilized to help generate the next lesson plan. The input is feedback data, and the output is an improved analytical model. Specifically, feedback is added to the dataset, and the AI ​​model is retrained.

[0628] (Application Example 1)

[0629] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] In recent years, educational settings have required the understanding and individualization of diverse student levels. However, conventional educational support systems primarily focus on the automatic generation of curricula and lack mechanisms to evaluate students' understanding and concentration levels in real time and support flexible lesson adjustments based on that evaluation. As a result, teachers have been unable to properly grasp the understanding of each student, making effective lesson management difficult.

[0631] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0632] In this invention, the server includes means for receiving educational information provided by the user, means for analyzing the educational information by comparing it with existing curriculum and external examination trend data, and means for evaluating the learner's level of understanding using emotion recognition technology and supporting the adjustment of educational activities based on this evaluation. This makes it possible to grasp the student's level of understanding in real time and flexibly adjust the lesson plan.

[0633] A "user" is an entity that provides educational information and refers to the generated educational activity plans.

[0634] "Educational information" refers to information that includes data on curriculum progress and students' learning status.

[0635] "Existing curriculum" refers to the standard course content and objectives set by an educational institution.

[0636] "External exam trend data" refers to information regarding the content and frequency of questions asked in past exams.

[0637] "Emotion recognition technology" is a technology that estimates emotions from a learner's facial expressions and voice, and evaluates their level of comprehension and concentration.

[0638] The "Educational Activity Plan" will include specific guidelines regarding the progress and content of lessons.

[0639] The system implementing this invention primarily operates with a server at its core. The server receives educational information from users and analyzes this data by comparing it with existing curriculum and external exam trend data. In this process, it performs large-scale data processing using a database management system and machine learning algorithms.

[0640] The server also utilizes emotion recognition technology to evaluate learners' comprehension levels from data provided by users. This technology includes image processing software and voice analysis software for facial expressions and voice analysis. This enables the generation of educational activity plans tailored to the user, supporting more effective lesson management.

[0641] The terminal is a device used by the user and is responsible for presenting the generated educational activity plan to the user. The terminal also allows for the review and editing of the generated plan, and users can send additional feedback to the server. In this way, educational information is entered and feedback is provided in real time via the terminal.

[0642] This system includes, as a concrete example, a system in which teachers wear smart wearable devices to monitor students' understanding and concentration levels in real time. An example of a prompt message is, "Please evaluate the students' level of engagement based on their facial expressions and body movements." This can be used to generate lesson plans tailored to the students' situations, and the system can be continuously improved based on the feedback provided as needed.

[0643] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0644] Step 1:

[0645] The server receives educational information from the terminal. This educational information includes the progress of the curriculum entered by the user and the learning status of students. The server stores this information in a database and uses it as basic data for subsequent analysis.

[0646] Step 2:

[0647] The server analyzes the received educational information by comparing it with existing curriculum and external exam trend data. Using a database management system, it searches for relevant data and applies machine learning algorithms to process the data for creating optimal educational plans. As an output, it generates an analytical report on students' current level of understanding.

[0648] Step 3:

[0649] The server uses generative AI models and emotion recognition technology to evaluate learners' comprehension. Specifically, it utilizes facial expression analysis programs and speech recognition software to analyze learners' facial expressions and speaking style from received data, thereby determining their emotional state. The input is student voice and facial expression data, and the output is an emotion evaluation score.

[0650] Step 4:

[0651] The terminal displays a plan of educational activities sent from the server to the user. The user uses this as a guide when conducting lessons. An interface has been developed on the terminal, allowing the user to review the plan and edit the necessary parts.

[0652] Step 5:

[0653] After conducting a lesson, users input feedback into the server via their device. This feedback includes information about the effectiveness of the lesson and student responses. The server uses the received feedback for subsequent data analysis to further optimize future educational activities.

[0654] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0655] This invention provides a system that supports educational institutions and teachers in creating lesson plans more effectively, and in particular, by integrating an emotion engine, it enables lesson plans that take the user's emotions into account. This system, including the emotion engine, consists of three components: a server, a terminal, and a user.

[0656] Server: As a central processing unit, the server receives educational information, feedback, and user emotion data recognized by the emotion engine, and stores this information in a database. This database also stores curriculum guidelines and trend data from past external examinations. Based on the received information, the server analyzes the educational information and generates the next lesson plan while taking the user's emotions into consideration. This adjusts the plan to better suit the user.

[0657] Terminal: The terminal provides an interface for users to input educational information and interact with the server. Users input their lesson progress here, and the terminal activates an emotion engine to analyze the user's emotions at the time of input. The analysis results are sent to the server and reflected in the next lesson plan. Furthermore, the terminal displays the plan provided by the server, allowing the user to review its contents.

[0658] User: The user, who is a teacher, inputs information about the progress of the lesson and their own emotions into the terminal. For example, they identify difficulties in the lesson or emotional stress points and share them with the system via the terminal. This allows the server to provide the next lesson plan based on the emotional data.

[0659] As a concrete example, consider a scenario where a user inputs information into their terminal, such as "I felt stressed because students weren't understanding the geometry lesson in the second year," along with "stress" data detected by the emotion engine. The server analyzes this information and generates a plan that includes specific approaches to deepen students' understanding in the next lesson. This plan also takes into account effective teaching methods to reduce stress.

[0660] This system allows for more personalized educational activities and enables them to proceed in a way that also takes into account the well-being of the teachers themselves.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] After each lesson, users input information into their device about the lesson content, students' understanding, and any stress or emotions they experienced. This data entry is intended to provide a detailed record of specific events and emotions.

[0664] Step 2:

[0665] The device automatically sends the entered educational information to an emotion engine to analyze the user's emotions. This analysis infers emotions from text data and the user's actions during input.

[0666] Step 3:

[0667] The device sends the emotional data obtained as a result of the analysis to the server along with educational information. This transmitted data includes information such as "Lesson progress: Delayed" and "Emotion: Stress."

[0668] Step 4:

[0669] The server compares the received educational information and sentiment data with existing curriculum guidelines and external examination trend data to analyze the progress and areas of deficiency in educational content.

[0670] Step 5:

[0671] The server automatically generates the next lesson plan based on the analysis results. This process also takes user emotional data into consideration, ensuring the plan includes measures to alleviate the stress the user experienced.

[0672] Step 6:

[0673] The generated lesson plan is sent from the server to the terminal and presented to the user as the plan for the next lesson. The user can then review it and prepare for the lesson based on the plan.

[0674] Step 7:

[0675] After the lesson is conducted, users again input sentiment information, including feedback, and send it to the server. The server uses this information to improve future lesson plans.

[0676] (Example 2)

[0677] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0678] Traditional educational planning systems generated educational plans based solely on information provided by educators, thus failing to adequately consider the emotional aspects of educators. This led to increased psychological burden on educators and potentially a decline in the quality of education. Furthermore, the generated plans were rigid and unable to flexibly adapt to changing circumstances.

[0679] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0680] In this invention, the server includes means for receiving educational information and emotional data provided by the user, means for analyzing the educational information and emotional data by comparing them with existing educational policy and evaluation indicator data, and means for generating a plan for the next educational instruction based on the analysis. This makes it possible to generate more personalized educational plans that take into account the emotions of educators, thereby reducing the burden on educators and improving the quality of education.

[0681] "Educational information provided by users" refers to information about educators' lesson plans and lesson content.

[0682] "Emotional data" refers to data that expresses the emotional state of educators when conducting lessons as numerical values ​​or categories.

[0683] "Existing educational policies" refer to the guidelines for curricula and teaching methods established by educational institutions.

[0684] "Evaluation indicator data" refers to data such as students' learning achievement levels based on past exams and evaluations.

[0685] "Means of analysis" refers to the technologies and methods used to analyze collected educational information and emotional data and process them for use in future planning.

[0686] "Means for generating educational guidance plans" refers to systems and methods for planning the next lesson or instructional content based on input data and analysis results from educators.

[0687] A "sentiment analysis engine" refers to a software module that analyzes and quantifies or categorizes emotional states from input by educators.

[0688] This embodiment of the invention is an educational planning system for supporting educators, and consists of three components: a server, a terminal, and a user. Specifically, the server functions as a central processing unit and receives and analyzes the various types of data described below.

[0689] server:

[0690] The server receives educational information and emotional data provided by educators via their devices. During this process, an emotional analysis engine detects the educators' emotions and quantifies the results. The server stores the received data in a database and analyzes it using Python data science tools. Furthermore, it utilizes a generative AI model to generate educational guidance plans that take the educators' emotions into account. Specifically, general natural language generation tools can be used for the AI ​​model.

[0691] Terminal:

[0692] The terminal is used by educators and provides an interface for inputting educational information and emotional data. An emotional analysis engine operates on the terminal to analyze the input emotions. Furthermore, it displays educational instruction plans sent from the server, allowing educators to review these plans and provide feedback. This feedback is then incorporated into subsequent analyses.

[0693] User:

[0694] The user, an educator, inputs their own emotions along with educational information into the terminal. For example, they input self-assessments and emotional states based on students' understanding in class. As a specific example, they might input a situation such as, "Students were slow to understand in today's lesson, and I felt insecure about my explanation methods," and emotional data based on that would be generated. It is also assumed that the generating AI model would be prompted with a message such as, "In the educational system, please propose a lesson plan that takes into account the user's emotions, such as experiencing a decline in student motivation in recent lessons and feeling stressed about it."

[0695] This embodiment allows educators to receive an optimal educational plan that takes their own emotional state into account, and is expected to lead to more personalized educational activities.

[0696] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0697] Step 1:

[0698] Users input educational information and emotional data through their devices. Specifically, they input text about the progress of lessons and their own feelings (e.g., "Students showed no motivation, which made me anxious"). This input data forms the basis for the next step.

[0699] Step 2:

[0700] The terminal receives the input educational information and emotional data, and uses an emotional analysis engine to analyze the emotional data. The emotional analysis engine converts the emotions from the input text into numerical values ​​and outputs "anxiety" as an emotional score as a result of the analysis. The emotional data is then sent to the server in an analyzed state.

[0701] Step 3:

[0702] The server receives educational information and quantified sentiment data transmitted from terminals. The server stores this data in a database and performs detailed data analysis by comparing it with existing educational policy and evaluation metric data. Using Python data science tools, it conducts trend and correlation analyses of the data and generates future educational guidance plans based on the analysis results.

[0703] Step 4:

[0704] The server uses a generative AI model to generate educational guidance plans based on educational information and analysis results stored on the server. Specifically, it inputs a prompt message to the generative AI model (e.g., a general natural language generation tool) such as, "In the educational system, please propose a lesson plan that takes into account the feelings of the user who recently experienced a decline in student motivation in a lesson and is feeling stressed about it," and outputs a plan that is sensitive to the educator's feelings.

[0705] Step 5:

[0706] The server sends the generated teaching plan to the terminal. This plan incorporates the previous analysis results and suggestions from the generated AI model, and is adjusted to be meaningful for educators.

[0707] Step 6:

[0708] The terminal displays the received lesson plan to the user. The user can review the plan and enter feedback into the terminal as needed. This feedback will be used to improve future lesson plans. Furthermore, the feedback information will be used as input for the next data analysis cycle, contributing to the overall improvement of the system's accuracy.

[0709] (Application Example 2)

[0710] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0711] In educational settings, teachers face the challenge of not being able to consider individual students' learning progress, understanding, and emotional state when creating lesson plans. In particular, there is a need to grasp students' responses and interests to learning in real time and adjust lesson content accordingly, but the current system has limited means of doing this efficiently.

[0712] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0713] In this invention, the server includes means for receiving educational information provided by the user, means for analyzing the educational information by comparing it with existing curriculum and external examination trend data, means for generating a plan for the next educational activity based on the analysis, and means for analyzing the user's emotional state and providing real-time feedback. This enables teachers in educational settings to create and present individually optimized lesson plans in real time based on students' emotions and level of understanding.

[0714] "Users" are entities that provide educational information using the system, and primarily refer to teachers.

[0715] "Educational information" is a general term for various data and materials related to education provided by users, and includes information such as students' learning progress and understanding.

[0716] A "curriculum" is a set of educational content and learning plans organized to achieve specific educational objectives.

[0717] "External exam trend data" refers to data on past exam questions and their question trends, and is information used to predict future exam questions.

[0718] "Analysis" is the process of comparing received educational information with existing data and interpreting and evaluating its content based on certain rules.

[0719] An "educational activity plan" is a plan that defines the specific content and methods of lessons to effectively promote students' learning.

[0720] "Emotional state" refers to the user's psychological state and emotional fluctuations, and includes information about internal reactions such as stress, anxiety, and relaxation.

[0721] A "means of providing real-time feedback" refers to a function that instantly returns information about the user's feelings and level of understanding, and provides advice and instructions that are useful for the progress of the lesson.

[0722] In implementing this invention, a specific system will be developed through the following hardware and software configuration. The main components of the system are a server, a terminal, and a user.

[0723] The server functions as a central processing unit, receiving educational information from users and analyzing it by comparing it with existing curriculum and external exam trend data. Based on this analysis, it generates a plan for the next educational activity. The generated plan is adjusted to take into account the user's emotional state. Emotional analysis will utilize an emotion recognition library. By using this library, the teacher's stress, anxiety, and relaxation levels will be identified, and this information will be reflected in the lesson plan.

[0724] The terminal provides an interface for users to input educational information and feedback. Wearable devices such as smart glasses are connected to the terminal and have the capability to collect and analyze real-time emotional states. These devices extract emotional data from the user's own recordings and facial expressions and send it to the server. As a result, users can receive immediate, emotion-based feedback.

[0725] Teachers, as users, input their emotions and lesson progress into their devices, receive feedback from the system, and use it to plan their next lessons. This allows teachers to gain concrete approaches to conducting better educational activities.

[0726] For example, if a teacher feels that students are not responding well in a math class, they can immediately input their thoughts and feelings into a terminal. Based on this information, the server can suggest alternative approaches, such as "problem-solving learning," for the next class. An example of a prompt provided by the AI ​​model could be: "Generate specific teaching methods to improve student comprehension in a 2nd-year geometry class and reduce the teacher's anxiety."

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The terminal receives input from the user. This input includes information on the progress of the lesson and the user's self-reported emotional state. The terminal formats this data and preprocesses it for transmission to the server.

[0730] Step 2:

[0731] The server receives educational information and sentiment data transmitted from the terminal. The received data is analyzed using an educational information database and a sentiment recognition library. In this process, educational information is compared with existing curriculum and external exam trend data, and sentiment data is analyzed by sentiment recognition algorithms.

[0732] Step 3:

[0733] The server generates a plan for the next educational activity based on the analysis results. This plan generation uses an AI-based recommendation algorithm, and the generated plan is customized to take into account the user's emotional state. In this process, the generating AI model is used to create appropriate prompt sentences and make specific suggestions such as, "Generate specific teaching methods to improve student understanding in 2nd-year geometry lessons and reduce teacher anxiety."

[0734] Step 4:

[0735] The server sends the generated educational activity plan to the terminal. The terminal displays this plan to the user. At this time, the user can review the plan and provide additional feedback as needed. The feedback is received and processed by the server again and used for future planning.

[0736] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0737] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0738] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0739] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0740] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0741] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0742] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0743] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0744] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0745] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0746] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0747] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0748] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0749] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0750] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0751] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0752] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0753] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0754] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0755] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0756] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0757] The following is further disclosed regarding the embodiments described above.

[0758] (Claim 1)

[0759] A means of receiving educational information provided by the user,

[0760] A means for analyzing the aforementioned educational information by comparing it with existing curriculum and external examination trend data,

[0761] A means for generating a plan for the following educational activities based on the above analysis,

[0762] A means of presenting the generated educational activity plan to the user,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, characterized in that the generated educational activity plan takes into account the trends in questions asked in external examinations.

[0766] (Claim 3)

[0767] The system according to claim 1, further comprising means for receiving feedback from the user and utilizing the feedback for subsequent analysis.

[0768] "Example 1"

[0769] (Claim 1)

[0770] Means for receiving educational data provided by the user,

[0771] A means for analyzing the aforementioned educational data by comparing it with existing teaching standards and examination trend information,

[0772] A means for automatically generating a plan for the next educational activity based on the analysis of the above using a generative AI model,

[0773] A means of presenting the generated educational activity plan to the user and allowing the user to edit it,

[0774] A means of presenting the generated educational activity plan to the user,

[0775] A means for receiving feedback from users and utilizing said feedback for optimization,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, characterized in that the generated educational activity plan takes into account information on the trends in exam questions.

[0779] (Claim 3)

[0780] The system according to claim 1, further comprising means for utilizing user feedback in the analysis of subsequent educational data using machine learning.

[0781] "Application Example 1"

[0782] (Claim 1)

[0783] A means of receiving educational information provided by the user,

[0784] A means for analyzing the aforementioned educational information by comparing it with existing curriculum and external examination trend data,

[0785] A means for generating a plan for the following educational activities based on the above analysis,

[0786] A means of presenting the generated educational activity plan to the user,

[0787] A means of evaluating learners' comprehension using emotion recognition technology and supporting the adjustment of educational activities based on this evaluation,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, characterized in that the generated educational activity plan takes into account the trends in questions asked in external examinations.

[0791] (Claim 3)

[0792] The system according to claim 1, further comprising means for receiving feedback from the user and utilizing the feedback for subsequent analysis.

[0793] "Example 2 of combining an emotion engine"

[0794] (Claim 1)

[0795] A means of receiving educational information and emotional data provided by the user,

[0796] A means for analyzing the aforementioned educational information and emotional data by comparing them with existing educational policy and evaluation indicator data,

[0797] A means for generating the following educational guidance plan based on the above analysis,

[0798] A means for presenting the generated educational guidance plan to the user and receiving feedback from the user,

[0799] A means for utilizing the user's feedback and emotional data in the following analysis,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, characterized in that the generated educational guidance plan takes evaluation indicator data into consideration.

[0803] (Claim 3)

[0804] The system according to claim 1, characterized in that the aforementioned emotional data is generated by an emotional analysis engine.

[0805] "Application example 2 when combining with an emotional engine"

[0806] (Claim 1)

[0807] A means of receiving educational information provided by the user,

[0808] A means for analyzing the aforementioned educational information by comparing it with existing curriculum and external examination trend data,

[0809] A means for generating a plan for the following educational activities based on the above analysis,

[0810] A means of presenting the generated educational activity plan to the user,

[0811] A means of analyzing the user's emotional state and providing real-time feedback,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, characterized in that the generated educational activity plan takes into account the trends in questions asked in external examinations.

[0815] (Claim 3)

[0816] The system according to claim 1, further comprising means for receiving feedback and emotional information from the user and utilizing the feedback and emotional information for subsequent analysis. [Explanation of symbols]

[0817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving educational information provided by the user, A means for analyzing the aforementioned educational information by comparing it with existing curriculum and external examination trend data, A means for generating a plan for the following educational activities based on the above analysis, A means of presenting the generated educational activity plan to the user, A means of evaluating learners' comprehension using emotion recognition technology and supporting the adjustment of educational activities based on this evaluation, A system that includes this.

2. The system according to claim 1, characterized in that the generated educational activity plan takes into account the trends in questions asked in external examinations.

3. The system according to claim 1, further comprising means for receiving feedback from the user and utilizing the feedback for subsequent analysis.

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