AI intelligent supervision online education system

Abstract

The invention discloses an AI intelligent supervision online education system, and relates to the technical field of online education. The AI intelligent supervision online education system comprises a student interface, the student interface is connected with a parent interface, the parent interface is connected with a teacher interface, the teacher interface is connected with data processing and analysis, and the student interface is connected with a cognitive map engine. According to the system, comprehensive and dynamic learning path planning and real-time feedback are achieved through cognitive map generation and analysis, the emotion intelligent evaluation and support module monitors the emotion state of students in real time and provides personalized psychological support, the adaptive learning engine accurately recommends learning resources, the learning mode is dynamically adjusted, and the learning efficiency is improved. The multi-modal interaction module supports multiple interaction modes and enhances the interestingness and participation degree of learning, the project system learning and community cooperation function promotes deep cooperation and practical application, and the ethical and value education module helps students to establish correct concepts, so that the education efficiency and quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of online education technology, and in particular to an AI intelligent supervision online education system. Background Art

[0002] Existing online education technologies are mainly implemented in the following ways: 1. Video teaching: Teaching videos are produced through video recording tools, and are published and played through video platforms. Students can watch videos at any time and any place for independent learning; 2. Online tests: Test questions are designed and published using online test platforms. The system automatically scores and generates score reports. This method can provide timely feedback on students' learning situation and help teachers understand students' mastery of the subject; 3. Discussion area: Through forum software or built-in discussion area functions, students and teachers can post, comment and interact in the discussion area to promote learning exchanges and cooperation; 4. Learning management system: Use a learning management system to manage course content, student progress, homework submission and grade records. LMS provides a structured and systematic learning environment; 5. Real-time interaction: Real-time interactive teaching is carried out through a live broadcast platform, supporting video conferencing, screen sharing and real-time Q&A.

[0003] Although existing technologies have made certain progress in the above aspects, existing online education systems usually rely on simple learning records and score reports to evaluate students' learning progress, lack deep cognitive map support, and cannot fully understand students' learning paths and cognitive obstacles; existing systems rarely involve emotional intelligence assessment, lack monitoring and psychological support for students' emotional states, and are prone to neglecting students' mental health; existing personalized recommendation systems are often based only on simple user behavior data, making it difficult to achieve dynamic and comprehensive learning adjustments, and the degree of personalization is limited; most existing systems support basic text and video interactions, but lack support for multimodality, and the interaction method is single, making it difficult to provide a more natural and rich interactive experience; existing systems often lack project-based learning and community collaboration functions, making it difficult to support deep cooperation and practical applications among students, and the effect of collaborative learning needs to be improved; existing systems usually only perform simple data collection and analysis, making it difficult to generate detailed multi-dimensional learning reports and improvement suggestions, and the feedback information provided is not specific and comprehensive enough. Therefore, it will greatly affect the efficiency of education and the quality of student learning. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides an AI intelligent supervision online education system, comprising a student interface, the student interface is connected to a parent interface, the parent interface is connected to a teacher interface, the teacher interface is connected to data processing and analysis, the student interface is connected to a cognitive map engine, the cognitive map engine is connected to an adaptive learning engine, and the adaptive learning engine is connected to a multimodal interaction module;

[0005] The cognitive map engine is connected to emotional assessment and support, the emotional assessment and support is connected to a collaboration module, the collaboration module is connected to an external interface and cooperation, the adaptive learning engine is connected to a feedback and incentive engine, the feedback and incentive engine is connected to an education module, the education module is connected to a user feedback mechanism, the multimodal interaction module is connected to data storage and management, the data storage and management is connected to data visualization, and the data visualization is connected to a system administrator interface.

[0006] Preferably, the student interface is used to display available courses and programs, recommend courses based on student interests and cognitive maps, provide a variety of learning resources such as videos, articles, and interactive exercises, support role-playing, situational simulation, and gamified learning interactive activities, and provide emotional intelligence mentors and mental health courses, encourage students to reflect on themselves, provide feedback tools, establish student communities, and support discussion and collaboration.

[0007] Preferably, the cognitive map engine generates a cognitive map based on students' learning behaviors and performances, analyzes students' learning paths and obstacles, and provides improvement suggestions.

[0008] Preferably, the adaptive learning engine dynamically adjusts the learning content according to the cognitive map and emotional state to generate a learning path and method suitable for students. The multimodal interaction module supports multiple interaction methods such as text, voice, image, video and gesture, and adjusts the interaction method according to the physical environment and psychological state of the students.

[0009] Preferably, the parent interface is a tool for displaying the child's learning progress and achievements, providing a record of the child's emotional state and psychological support, recommending learning activities and homework for parents to participate in, and supporting communication between parents and teachers.

[0010] Preferably, the emotional assessment and support is to assess the students' emotional state in real time through emotional intelligence technology, and provide psychological support and improvement suggestions based on the assessment results; the teacher interface is to manage course content and teaching plans, assess students' knowledge mastery, skill application and emotional attitudes, support communication tools between teachers and parents, and recommend appropriate teaching resources and activities; the feedback and incentive engine is to generate personalized feedback based on students' multi-dimensional performance, and provide personalized incentive plans, such as points and medals.

[0011] Preferably, the data storage and management is to record students' learning behaviors and performances, record students' emotional states and psychological support records, and record students' community interactions and collaboration data; the collaboration module is to manage project-based learning activities in which students participate, and promote collaboration among students through intelligent matching algorithms.

[0012] Preferably, the educational module is a course that includes ethical knowledge and values, which regularly evaluates students' ethical concepts and values, and provides improvement suggestions and learning resources based on the evaluation results. The data processing and analysis is to collect data from the front-end and back-end modules, use machine learning and data analysis techniques to analyze the collected data, and generate reports and suggestions. The data visualization is to present the analysis results to users in the form of charts and reports.

[0013] The beneficial effects of the present invention are as follows: by generating cognitive maps, the system can comprehensively understand students' learning paths and cognitive structures, identify knowledge weaknesses and learning obstacles, and based on the analysis results, the system dynamically adjusts the learning content and sequence to ensure that students learn along the path that best suits them. Real-time feedback and improvement suggestions help students correct errors in a timely manner and improve learning outcomes. By using multimodal emotion recognition technology, the system monitors students' emotional fluctuations and stress levels in real time, provides personalized psychological support courses and interactive activities, and helps students maintain a positive attitude. It supports the collection and analysis of multiple emotional data such as text, voice, and facial expressions to ensure the accuracy and comprehensiveness of emotional evaluation. In combination with cognitive maps and emotional states, the system accurately recommends learning resources and courses suitable for students, not only adjusting the learning content, but also dynamically adjusting the learning methods, such as time arrangements and learning intensity, according to students' learning habits and emotional states. By continuously collecting learning data and continuously optimizing the recommendation algorithm, the accuracy and effectiveness of the recommended content are ensured, thereby increasing the quality and efficiency of education. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0015] Figure 1 It is a schematic diagram of the system framework flow of the present invention. DETAILED DESCRIPTION

[0016] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0017] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0018] like Figure 1As shown, an AI intelligent supervision online education system includes a student interface, the student interface is connected to a parent interface, the parent interface is connected to a teacher interface, the teacher interface is connected to data processing and analysis, the student interface is connected to a cognitive map engine, the cognitive map engine is connected to an adaptive learning engine, and the adaptive learning engine is connected to a multimodal interaction module;

[0019] The cognitive map engine is connected with the emotion evaluation and support, the emotion evaluation and support is connected with the collaboration module, the collaboration module is connected with the external interface and cooperation, the adaptive learning engine is connected with the feedback and incentive engine, the feedback and incentive engine is connected with the education module, the education module is connected with the user feedback mechanism, the multimodal interaction module is connected with the data storage and management, the data storage and management is connected with the data visualization, and the data visualization is connected with the system administrator interface.

[0020] The student interface is used to display available courses and projects, recommend courses based on student interests and cognitive maps, provide a variety of learning resources such as videos, articles, and interactive exercises, support role-playing, situational simulation, and gamification learning interactive activities, and provide emotional intelligence mentors and mental health courses. It encourages students to reflect on themselves, provides feedback tools, builds student communities, and supports discussion and collaboration.

[0021] The cognitive map engine generates cognitive maps based on students' learning behaviors and performances, analyzes students' learning paths and obstacles, and provides improvement suggestions. The adaptive learning engine dynamically adjusts learning content based on cognitive maps and emotional states, and generates learning paths and methods suitable for students. The multimodal interaction module supports multiple interaction methods such as text, voice, image, video, and gesture, and adjusts the interaction method according to the physical environment and psychological state of the students.

[0022] The parent interface displays the child's learning progress and grades, provides the child's emotional state and psychological support records, recommends learning activities and homework for parents to participate in, and supports communication tools between parents and teachers. Emotional assessment and support uses emotional intelligence technology to evaluate students' emotional states in real time, and provides psychological support and improvement suggestions based on the evaluation results. The teacher interface manages course content and teaching plans, evaluates students' knowledge mastery, skill application and emotional attitudes, supports communication tools between teachers and parents, and recommends appropriate teaching resources and activities. The feedback and incentive engine generates personalized feedback based on students' multi-dimensional performance and provides personalized incentive plans, such as points and medals.

[0023] Data storage and management are used to record students' learning behaviors and performances, record students' emotional states and psychological support records, and record students' community interactions and collaboration data. The collaboration module is used to manage project-based learning activities in which students participate, and to promote collaboration among students through intelligent matching algorithms. The education module is a course that includes ethical knowledge and values. It regularly evaluates students' ethical concepts and values, and provides improvement suggestions and learning resources based on the evaluation results. Data processing and analysis is used to collect data from the front-end and back-end modules, use machine learning and data analysis techniques to analyze the collected data, and generate reports and suggestions. Data visualization is used to present the analysis results to users in the form of charts and reports.

[0024] External interfaces and cooperation provide API interfaces for data exchange and function integration with external platforms. User feedback mechanisms collect user feedback regularly. System administrator interfaces monitor the system's operating status and performance, perform system maintenance and troubleshooting, and regularly update system functions and content based on user feedback and data analysis results.

[0025] In the present invention, the front-end framework uses React or Vue, the state management uses Redux or Vuex, and the UI component library uses Ant Design or Element UI. Generate cognitive maps Based on the learning behavior and performance of students, use the graph database Neo4j to generate and update cognitive maps. Analyze the learning process through machine learning algorithm decision tree and neural network to analyze students' cognitive paths, identify learning obstacles and difficulties, and generate improvement suggestions. Evaluate emotional state Evaluate students' emotional state through natural language processing technology. Users can submit emotional states through text or voice. Support and recommend corresponding psychological support courses and activities based on the results of emotional evaluation. According to cognitive maps and emotional states, use recommendation algorithms to dynamically adjust learning content. Recommended content can be provided to the front end through an API. Evaluate students' emotional states through natural language processing technology. Users can submit emotional states through text, voice, etc. Recommend corresponding psychological support courses and activities based on the results of emotional evaluation. Support users to interact with emotional intelligence tutors. Generate personalized feedback based on students' multi-dimensional performance (knowledge mastery, skill application, emotional attitude, etc.). Provide personalized incentive programs, such as points and medals. Use machine learning algorithms such as decision trees, random forests, and neural networks to analyze the collected data and generate learning reports and improvement suggestions. For example, you can use Scikit-learn for feature extraction and model training.

[0026] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An AI intelligent supervision online education system, including a student interface, characterized in that: The student interface is connected to the parent interface, the parent interface is connected to the teacher interface, the teacher interface is connected to data processing and analysis, the student interface is connected to the cognitive map engine, the cognitive map engine is connected to the adaptive learning engine, and the adaptive learning engine is connected to the multimodal interaction module; The cognitive map engine is connected to emotional assessment and support, the emotional assessment and support is connected to a collaboration module, the collaboration module is connected to an external interface and cooperation, the adaptive learning engine is connected to a feedback and incentive engine, the feedback and incentive engine is connected to an education module, the education module is connected to a user feedback mechanism, the multimodal interaction module is connected to data storage and management, the data storage and management is connected to data visualization, and the data visualization is connected to a system administrator interface.

2. The AI ​​intelligent supervision online education system according to claim 1, characterized in that: The student interface is used to display available courses and projects, recommend courses based on student interests and cognitive maps, provide a variety of learning resources such as videos, articles, and interactive exercises, support role-playing, situational simulation, and gamification learning interactive activities, and provide emotional intelligence mentors and mental health courses. It encourages students to reflect on themselves, provides feedback tools, builds a student community, and supports discussion and collaboration.

3. The AI ​​intelligent supervision online education system according to claim 1, characterized in that: The cognitive map engine generates a cognitive map based on students' learning behaviors and performances, analyzes students' learning paths and obstacles, and provides improvement suggestions.

4. The AI ​​intelligent supervision online education system according to claim 1, characterized in that: The adaptive learning engine dynamically adjusts the learning content according to the cognitive map and emotional state, and generates a learning path and method suitable for students. The multimodal interaction module supports multiple interaction methods such as text, voice, image, video and gesture, and adjusts the interaction method according to the physical environment and psychological state of the students.

5. The AI ​​intelligent supervision online education system according to claim 1, characterized in that: The parent interface is used to display the child's learning progress and achievements, provide records of the child's emotional state and psychological support, recommend learning activities and homework for parents to participate in, and support communication tools between parents and teachers.

6. The AI ​​intelligent supervision online education system according to claim 1, characterized in that: The emotional assessment and support is to evaluate the students' emotional state in real time through emotional intelligence technology, and provide psychological support and improvement suggestions based on the assessment results. The teacher interface is to manage course content and teaching plans, evaluate students' knowledge mastery, skill application and emotional attitudes, support communication tools between teachers and parents, and recommend appropriate teaching resources and activities. The feedback and incentive engine generates personalized feedback based on students' multi-dimensional performance and provides personalized incentive plans, such as points and medals.

7. The AI ​​intelligent supervision online education system according to claim 1, characterized in that: The data storage and management is to record students' learning behaviors and performances, record students' emotional states and psychological support records, and record students' community interactions and collaboration data. The collaboration module is to manage project-based learning activities in which students participate, and promote collaboration among students through intelligent matching algorithms.

8. The AI ​​intelligent supervision online education system according to claim 1, characterized in that: The education module is a course that includes ethical knowledge and values. It regularly evaluates students' ethical concepts and values, and provides improvement suggestions and learning resources based on the evaluation results. The data processing and analysis collects data from the front-end and back-end modules, uses machine learning and data analysis techniques to analyze the collected data, and generates reports and suggestions. The data visualization presents the analysis results to users in the form of charts and reports.

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