An education field teaching intelligent agent development system

Abstract

This invention discloses an intelligent agent development system for the educational field, comprising: a teacher teaching module for creating and publishing intelligent agent construction tasks, tracking students' progress in executing these tasks, and checking and guiding the finished intelligent agents built by students; a student practice module for receiving intelligent agent construction tasks, executing intelligent agent construction operations, and submitting finished intelligent agents; an intelligent agent construction module for providing intelligent agent development capabilities to enable teachers and students to build intelligent agents; a large model management module for providing various large models to support the intelligent agent construction module and for unified management and billing of the various large models accessed; and a user center module for unified management and identity authentication of all-dimensional subjects. This invention can solve the problems of high technical barriers, lack of alignment with teaching processes, and lack of education-specific capabilities in general intelligent agent platforms, significantly improving the universality and practicality of AI teaching.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence teaching technology, and in particular to a teaching intelligent agent development system for the education field. Background Technology

[0002] Currently, AI agent technology centered on large models is developing rapidly, and many agent development platforms for general scenarios in various industries have emerged on the market. These platforms are mainly aimed at developers with certain technical backgrounds and support model training, agent construction, and general AI application development based on large models to empower various application scenarios. These general platforms adopt a modular architecture design and have basic capabilities such as multi-model access and tool calling, which can meet the general agent development needs of different industries.

[0003] However, the aforementioned general-purpose intelligent agent development platform has significant technical shortcomings when directly applied to educational scenarios: First, it has a high technical threshold, requiring the writing of complex prompts, configuration of workflow parameters, and development of interfaces and front-end interfaces, making it impossible to achieve zero-programming and unsuitable for teachers and students without technical backgrounds; second, it only aims to generate intelligent agents capable of executing tasks, without designing dedicated processes for teachers to "teach" and students to "learn" around teaching scenarios, lacking organizational structures, permissions, and teaching control capabilities adapted to schools, and unable to support classroom-based AI intelligent agent teaching; third, it only provides general industry components, lacking specialized components for the education field such as formula editing, teaching and research knowledge graphs, classroom interaction, and lesson plan and courseware design, making it difficult to cover the AI ​​application scenarios required for teaching across all educational stages. Summary of the Invention

[0004] This invention provides a teaching intelligent agent development system for the education field, which can effectively solve the problems of high technical threshold, lack of fit with teaching process, and lack of education-specific capabilities of general intelligent agent platforms, and significantly improve the universality and operability of AI teaching.

[0005] This invention provides a teaching intelligence agent development system for the education field, comprising: The teacher teaching module is used to create and publish intelligent agent construction tasks, track students' progress in executing the intelligent agent construction tasks, and check and guide the finished intelligent agents built by students. The student hands-on module is used to receive intelligent agent construction tasks issued by teachers, perform intelligent agent construction operations, and submit the completed intelligent agent product. The intelligent agent building module provides subject-specific components, structured knowledge services, large model invocation, and prompt word optimization functions to enable teachers and students to build intelligent agents. The large model management module is used to provide various large models to the intelligent agent construction module, and to perform unified management and metering and billing of the various large models connected; The user center module is used for unified management and identity authentication of school information, class information, subject information, teacher information, student information, and account information.

[0006] Furthermore, the teacher instruction module is provided for teachers' use and includes: The AI ​​teaching task management submodule is used to create classes, maintain class student lists, publish AI training course notices, and distribute intelligent agent construction tasks. It also supports the distribution of pre-built intelligent agent templates by teachers to designated classes. The AI ​​teaching progress management submodule is used to statistically analyze the release status of intelligent agent construction tasks, the use of students' intelligent agents, and the quality of homework completion, and supports teachers to intervene in real time to view the entire process of students' intelligent agent construction. The Intelligent Agent Review and Guidance submodule is used to view, grade, provide optimization guidance, and share and publish the finished intelligent agents built by students.

[0007] Furthermore, the student practical module is provided for student use and includes: The AI ​​learning task receiving submodule is used to receive AI training courses, agent usage tasks, and agent construction assignments published by teachers. The AI ​​learning task execution submodule is used to call the intelligent agent construction module to complete the construction of the intelligent agent according to the task requirements, submit the finished intelligent agent, and optimize the intelligent agent based on the teacher's feedback and optimization suggestions.

[0008] Furthermore, the intelligent agent construction module includes: The custom interface configuration submodule provides teachers and students with visual configuration capabilities for both PC and mobile interfaces. The subject-specific component library submodule is used to register and manage subject-specific components for the education field through a standardized MCP interface; The structured knowledge service submodule is used to provide semantic retrieval, knowledge reasoning, and context enhancement services for intelligent agent construction.

[0009] Furthermore, the custom interface configuration submodule provides teachers and students with front-end interface components and UI design templates, supports drag-and-drop interface layout and optimization, and can call pre-built intelligent agents through configuration to achieve zero-code integration between the front-end interface and the back-end intelligent agents.

[0010] Furthermore, the intelligent agent construction module also includes: The access permission verification submodule is used to verify the user's identity, class and subject permissions, and determine whether the user has the permission to build intelligent agents and use components.

[0011] Furthermore, the agent building module supports the construction of chat-type agents, task-type agents, and application-type agents; The chat-type intelligent agent is used to realize knowledge Q&A, learning consultation, daily Q&A and multi-round dialogue interaction through dialogue interaction; The task-oriented intelligent agents are suitable for specific teaching scenarios, including essay correction intelligent agents, subject Q&A intelligent agents, paper optimization intelligent agents, and translation proofreading intelligent agents; The application-oriented intelligent agent is used to build complete and independent AI teaching applications, supporting custom prompts, custom knowledge bases, and workflow orchestration of complex business logic.

[0012] Furthermore, the large model management module includes: The large model access submodule is used to remotely connect to and call third-party cloud-based large model services through standardized API interfaces; The large model management submodule is used to manage large models deployed locally and privately by schools. The large model measurement submodule is used to record the usage of various large models by school and to complete the measurement and billing statistics based on the usage.

[0013] Furthermore, the user center module includes: The school management submodule is used to configure and manage basic school information, grade information, and class information. The subject management submodule is used to configure and manage information about all subjects offered by the school. The teacher management submodule is used to create teacher accounts, configure the binding relationship between teachers and subjects and classes, and set the scope of teachers' teaching permissions. The student management submodule is used to create student accounts, establish a binding relationship between students and classes, and record students' AI learning profiles and intelligent agent construction results. The account authentication submodule provides a unified identity authentication interface for the teacher teaching module, student practical module, and intelligent agent construction module to call to complete user login and identity verification.

[0014] Furthermore, the user center module is also used to interface with the academic affairs system to synchronize teacher teaching data and student learning data to the academic affairs system.

[0015] Compared with existing technologies, the educational intelligent agent development system provided by this invention has the following advantages: through the coordinated operation of five modules, it integrates organizational management, teaching process control, student practical training, zero-code intelligent agent development, and large-scale model service support, truly realizing class-based teaching, zero-code development, full-process control, and integrated support for educational scenarios. It effectively solves the problems of high technical threshold, lack of fit with teaching processes, and lack of education-specific capabilities of general intelligent agent platforms, enabling teachers and students without technical backgrounds to use it smoothly, and significantly improving the universality of AI general education and intelligent agent innovative teaching. Attached Figure Description

[0016] To more clearly illustrate the technical features of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the overall structure of an educational intelligent agent development system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the functions of each module of an educational intelligent agent development system provided in an embodiment of the present invention; Figure 3 This is a teaching organization architecture diagram of an educational intelligent agent development system provided by an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0021] This invention provides a teaching intelligence agent development system for the education field. See [link to relevant documentation]. Figure 1 This is a schematic diagram of the structure of an embodiment of an educational intelligent agent development system provided by the present invention.

[0022] like Figure 1 As shown, the system includes: The teacher teaching module 11 is used to create and publish intelligent agent construction tasks, track the students' progress in executing the intelligent agent construction tasks, and check and guide the finished intelligent agents built by the students. Student practical module 12 is used to receive intelligent agent construction tasks issued by teachers, perform intelligent agent construction operations, and submit the completed intelligent agent product; The intelligent agent construction module 13 is used to provide subject-specific components, structured knowledge services, large model invocation and prompt word optimization functions, so that teachers and students can build intelligent agents; The large model management module 14 is used to provide various large models to the intelligent agent construction module, and to perform unified management and metering and billing of the various large models connected; User Center Module 15 is used for unified management and identity authentication of school information, class information, subject information, teacher information, student information and account information.

[0023] Specifically, this embodiment discloses a teaching intelligent agent development system for the education field, which is used to realize intelligent teaching for teachers and zero-code intelligent agent practical training for students in schools at all levels and of all types. The overall system consists of a teacher teaching module, a student practical module, an intelligent agent construction module, a large model management module, and a user center module. The modules work together and communicate with each other to complete the whole process of teaching services from organization and management, teaching control, practical construction to model support.

[0024] This implementation method integrates organizational management, teaching process control, student practical training, zero-code intelligent agent development, and large-scale model service support through the coordinated operation of five major modules. It truly realizes classroom-based teaching, zero-code development, full-process control, and integrated support for educational scenarios. It effectively solves the problems of high technical threshold, lack of alignment with teaching processes, and lack of education-specific capabilities of general intelligent agent platforms. It enables teachers and students without technical backgrounds to use it smoothly, significantly improving the coverage and implementation effect of AI general education and intelligent agent innovative teaching.

[0025] In one optional implementation, the teacher teaching module is for use by teachers and includes: The AI ​​teaching task management submodule is used to create classes, maintain class student lists, publish AI training course notices, and distribute intelligent agent construction tasks. It also supports the distribution of pre-built intelligent agent templates by teachers to designated classes. The AI ​​teaching progress management submodule is used to statistically analyze the release status of intelligent agent construction tasks, the use of students' intelligent agents, and the quality of homework completion, and supports teachers to intervene in real time to view the entire process of students' intelligent agent construction. The Intelligent Agent Review and Guidance submodule is used to view, grade, provide optimization guidance, and share and publish the finished intelligent agents built by students.

[0026] For details, see Figure 2 As shown, the teacher teaching module is dedicated to teachers. This module interfaces with the user center module, and login authentication is completed by the user center module. After logging in, the teacher teaching module obtains teacher information, corresponding teaching subjects, corresponding managed classes, and student lists from the user center module, so that teachers can organize and carry out AI intelligent agent teaching tasks based on the class. The teacher teaching module specifically includes an AI teaching task management sub-module, an AI teaching progress management sub-module, and an intelligent agent review guidance sub-module.

[0027] The AI ​​teaching task management submodule is used to maintain classes and distribute teaching tasks. Specifically, it includes: creating and maintaining class information, managing class student lists, publishing AI training course notices, distributing intelligent agent construction assignments, and supporting the distribution of pre-developed intelligent agent templates by teachers to designated class students.

[0028] Teachers can use this submodule to push AI knowledge learning resources to students, distribute self-developed AI agents to the class for students to use and reference, and assign AI agent construction assignments that match the subject scenarios, including but not limited to AI agent construction tasks such as homework correction, paper modification, code correction, and English optimization, so that students can complete the AI ​​agent development required by the subject based on training videos and teacher templates.

[0029] The AI ​​teaching progress management submodule is used to track and statistically analyze the entire teaching process. Specifically, it is used to: statistically analyze published course notices, the use of AI agents, and the completion of AI agent assignments; track and view the complete operation process of students building AI agents in real time; and support teachers to intervene and provide guidance in real time during the students' construction process.

[0030] Teachers can use this submodule to grasp the overall teaching progress of the class, monitor the development status of students' intelligent agents, adjust and optimize the students' construction process, and realize full-process control and teaching intervention for the construction of students' intelligent agents.

[0031] The Intelligent Agent Review and Guidance submodule is used to review, guide, and manage the finished intelligent agents submitted by students. Specifically, it is used to: view the finished intelligent agents submitted by students, correct and optimize the intelligent agents, and share and publish outstanding intelligent agent works to the whole class.

[0032] Teachers can use this submodule to evaluate and provide feedback on students' AI agent creations, demonstrate and promote high-quality works, and improve students' AI agent construction capabilities and innovation levels.

[0033] This embodiment, through three sub-modules—teaching task management, teaching progress management, and AI agent review guidance—and deeply integrated with the user center, realizes full-process management of AI agent teaching at the class level. It supports teachers in completing a complete teaching loop from class organization, task assignment, process tracking to outcome review, significantly reducing the operational threshold for teachers to conduct AI agent teaching. This enables teachers without technical backgrounds to efficiently organize classroom teaching and provide real-time guidance to students in building AI agents, effectively improving teaching efficiency and quality.

[0034] In one optional implementation, the student hands-on module is for student use and includes: The AI ​​learning task receiving submodule is used to receive AI training courses, agent usage tasks, and agent construction assignments published by teachers. The AI ​​learning task execution submodule is used to call the intelligent agent construction module to complete the construction of the intelligent agent according to the task requirements, submit the finished intelligent agent, and optimize the intelligent agent based on the teacher's feedback and optimization suggestions.

[0035] For details, see Figure 2 As shown, the student practice module is dedicated to student use and interfaces with the user center module, teacher teaching module, and intelligent agent construction module. After completing the identity authentication in the user center module, students enter this module to receive and execute various AI learning tasks issued by the teacher, realizing the learning, use, construction, optimization, and sharing of intelligent agents. The student practice module specifically includes an AI learning task receiving submodule and an AI learning task execution submodule.

[0036] The AI ​​learning task receiving submodule is used by students to obtain various teaching-related content issued by teachers. Specifically, it includes receiving AI training courses issued by teachers, viewing AI agents built or recommended by teachers, receiving AI agent usage tasks and AI agent construction assignments, so that students can clearly obtain learning objectives, usage requirements and construction specifications, providing a basis for subsequent AI agent practice.

[0037] The AI ​​learning task execution submodule is used by students to complete the entire process of hands-on practice according to task requirements. Specifically, it includes: using the intelligent agent construction module to build an intelligent agent in a zero-code manner according to the template, training content and assignment requirements issued by the teacher; submitting the finished intelligent agent to the teacher after construction; receiving the teacher's feedback and optimization suggestions; and iterating and optimizing the intelligent agent accordingly. Students can also share their personally developed intelligent agents to the class according to their own wishes, and communicate and discuss with classmates to learn and improve together.

[0038] This embodiment provides students with an integrated execution channel for task reception, hands-on construction, submission and optimization, and result sharing. This enables students without technical backgrounds to smoothly complete the entire learning process from learning AI knowledge and using mature intelligent agents to independently building and optimizing intelligent agents. It effectively lowers the operational threshold for students to participate in intelligent agent development, enhances students' AI practice ability and innovative awareness, and supports result sharing and interactive communication within the class, giving full play to the application value of large models and intelligent agent technology in education and teaching.

[0039] In one optional implementation, the agent building module includes: The custom interface configuration submodule provides teachers and students with visual configuration capabilities for both PC and mobile interfaces. The subject-specific component library submodule is used to register and manage subject-specific components for the education field through a standardized MCP interface; The structured knowledge service submodule is used to provide semantic retrieval, knowledge reasoning, and context enhancement services for intelligent agent construction.

[0040] For details, see Figure 2 As shown, the agent building module provides teachers and students with zero-code, visual agent development capabilities. It works in conjunction with the teacher teaching module, student practice module, user center module, and large model management module. It supports the development of agents and workflows through drag-and-drop interface and parameter configuration. The agent building module specifically includes a custom interface configuration submodule, a subject-specific component library submodule, and a structured knowledge service submodule. It also has the capabilities of prompt word optimization, large model invocation, agent type encapsulation, and application publishing.

[0041] The subject-specific component library submodule is used to register and manage education-specific components through a standardized MCP interface. Component types include commonly used teaching components such as mathematical formula editing, document revision, code editing, translation proofreading, philosophical reflection, and historical review. All components provide drag-and-drop access capabilities in a visual manner. When teachers and students build intelligent agent workflows, they can directly drag and drop the required components and incorporate them into the execution process without the need for additional interface development, enabling faster and more accurate generation of intelligent agents that meet learning needs.

[0042] The structured knowledge service submodule provides semantic retrieval, knowledge reasoning, and context enhancement services for agent construction. This submodule pre-constructs general knowledge files for schools at all levels and types, covering commonly used educational corpora, school policy datasets, and professional terminology sets, and supports teachers and students to upload knowledge files independently. The system automatically segments, vectorizes, and indexes the files, providing an external knowledge base service for large models. Teachers and students can generate personalized knowledge capabilities based on this knowledge service and combine it with the agent construction process to generate domain-specific teaching and learning agents.

[0043] This embodiment enables zero-code intelligent agent development throughout the entire process, significantly reducing the technical threshold for teachers and students to use large models and build intelligent agents, significantly improving the efficiency and professionalism of intelligent agent development in teaching scenarios, while supporting rapid application release and sharing, fully meeting the AI ​​teaching and practical needs of all grade levels and disciplines.

[0044] In one optional implementation, the custom interface configuration submodule provides teachers and students with front-end interface components and UI design templates, supports drag-and-drop interface layout and optimization, and can call pre-built intelligent agents through configuration to achieve zero-code integration between the front-end interface and the back-end intelligent agents.

[0045] Specifically, the custom interface configuration submodule provides teachers and students with visual configuration functions for both PC and mobile interfaces. It includes built-in front-end interface components such as text boxes, drop-down lists, buttons, and tabs, and offers multiple UI design templates for selection. Users can edit and optimize the interface layout, style, and interaction logic through drag-and-drop, quickly generating custom interfaces for AI applications. This submodule can directly call the developed intelligent agents through configuration, achieving zero-code integration of the front-end interface and the back-end intelligent agent logic. This allows the completed intelligent agents to form directly runnable AI applications. These AI applications can be published to the teaching module for teachers and students to experience, and excellent applications can be further published as formal applications to provide services to the public.

[0046] In one optional implementation, the agent building module further includes: The access permission verification submodule is used to verify the user's identity, class and subject permissions, and determine whether the user has the permission to build intelligent agents and use components.

[0047] Specifically, the access permission verification submodule is used to uniformly verify the user's identity, class, and corresponding subject permissions. Before a user enters the intelligent agent building environment, creates / accesses an intelligent agent project, or calls educational components or knowledge services, it determines whether the current user has the corresponding operation permissions. Only when the permission verification passes can the user be allowed to perform operations such as intelligent agent building, component usage, and knowledge service calls. If the verification fails, execution is refused and an insufficient permission prompt is returned, thereby achieving fine-grained permission control over the intelligent agent building process and teaching resources.

[0048] In one alternative implementation, the agent building module supports the construction of chat-type agents, task-type agents, and application-type agents; The chat-type intelligent agent is used to realize knowledge Q&A, learning consultation, daily Q&A and multi-round dialogue interaction through dialogue interaction; The task-oriented intelligent agents are suitable for specific teaching scenarios, including essay correction intelligent agents, subject Q&A intelligent agents, paper optimization intelligent agents, and translation proofreading intelligent agents; The application-oriented intelligent agent is used to build complete and independent AI teaching applications, supporting custom prompts, custom knowledge bases, and workflow orchestration of complex business logic.

[0049] Specifically, the intelligent agent construction module provides tiered intelligent agent construction capabilities for teaching scenarios, supporting teachers and students to build and use chat-type intelligent agents, task-type intelligent agents, and application-type intelligent agents in a step-by-step manner from easy to difficult. Through the tiered design of the three types of intelligent agents, it helps teachers and students without technical backgrounds to gradually master the use of prompt words, large model calls, and the entire process of intelligent agent development.

[0050] Among them, the chat-type intelligent agent is used to realize knowledge question answering, learning consultation, daily Q&A and multi-round continuous dialogue interaction with natural language dialogue interaction as the core form. It can be quickly created without complex configuration and serves as an introductory intelligent agent to help students understand the interaction logic of intelligent agents.

[0051] Task-oriented intelligent agents are suitable for various specific teaching scenarios and have the ability to perform clear and single teaching tasks. They mainly include essay correction agents, subject Q&A agents, paper optimization agents, and translation proofreading agents, which can directly serve the needs of subject learning. Students can quickly complete the configuration and use based on templates.

[0052] Application-oriented intelligent agents are used to build fully functional, independently deployable AI teaching applications. They support user-defined prompts and custom knowledge bases, and can perform visualized workflow orchestration of complex business logic, meeting the needs of advanced teaching innovation, interdisciplinary integrated applications, and complete AI product development.

[0053] This embodiment adopts a tiered construction mechanism of chat-type, task-type, and application-type intelligent agents, which conforms to students' cognitive patterns and the logic of teaching progression. It achieves full coverage from simple dialogues to complex applications, which can reduce the difficulty for beginners and support advanced innovative development. This enables teachers and students of different grades and with different backgrounds to quickly master the technology of building large models and intelligent agents, significantly improving the universality and practicality of AI teaching.

[0054] In one optional implementation, the large model management module includes: The large model access submodule is used to remotely connect to and call third-party cloud-based large model services through standardized API interfaces; The large model management submodule is used to manage large models deployed locally and privately by schools. The large model measurement submodule is used to record the usage of various large models by school and to complete the measurement and billing statistics based on the usage.

[0055] For details, see Figure 2 As shown, the large model management module provides unified, stable, and scalable large model support services for the intelligent agent construction module, realizes unified scheduling and management of cloud-based and private large models, and has the ability to measure and charge for model usage, meeting the diverse model needs in school teaching, training and innovation scenarios. Specifically, it includes a large model access submodule, a large model management submodule, and a large model metering submodule.

[0056] The large model access submodule is used to remotely connect to and call third-party cloud large model services through standardized API interfaces, including open-source large models and commercial large models provided by operators and Internet companies. It can quickly provide basic model capabilities such as general language understanding, multimodal processing, and tool calling for intelligent agent building modules, reducing the deployment cost and technical threshold for schools to use large models.

[0057] The large model management module is used to manage large models deployed locally and privately in schools. It especially supports large models in vertical fields that have been professionally trained in the education field, including but not limited to specialized large models in disciplines such as transportation, engineering, and computer science. Through local private access, it ensures data security and response efficiency, enabling teachers and students to carry out more in-depth and complex intelligent agent development and innovation and entrepreneurship practices based on professional vertical models.

[0058] The large model measurement submodule is used to uniformly record the usage of various large models from the perspective of the school as a whole. Specifically, it includes data such as the number of input tokens, the number of output tokens, and the frequency of calls. Based on the model usage, it completes the measurement statistics and billing accounting, providing a basis for the school to carry out model resource management, cost control, and service settlement.

[0059] This embodiment achieves centralized management and flexible scheduling of large model resources in educational scenarios through a three-layer architecture: unified access to large cloud models, unified management of private vertical large models, and unified metering and billing for model usage.

[0060] In one optional implementation, the user center module includes: The school management submodule is used to configure and manage basic school information, grade information, and class information. The subject management submodule is used to configure and manage information about all subjects offered by the school. The teacher management submodule is used to create teacher accounts, configure the binding relationship between teachers and subjects and classes, and set the scope of teachers' teaching permissions. The student management submodule is used to create student accounts, establish a binding relationship between students and classes, and record students' AI learning profiles and intelligent agent construction results. The account authentication submodule provides a unified identity authentication interface for the teacher teaching module, student practical module, and intelligent agent construction module to call to complete user login and identity verification.

[0061] For details, see Figure 2 As shown, the user center module serves as the core of the system's organizational structure and identity management. It is used to uniformly manage information on all dimensions of entities in the education scenario, such as schools, grades, classes, subjects, teachers, and students. It provides identity authentication, permission allocation, relationship binding, and integration capabilities with the academic affairs system, and provides the organizational structure and permission foundation for the teacher teaching module, student practical module, and intelligent agent construction module.

[0062] The user center module, based on the organizational structure of the education and teaching scenario, establishes hierarchical relationships and multi-dimensional correspondences between teachers, schools, classes, students, and subjects. The relationship logic is as follows: Figure 3 As shown, a school corresponds to multiple teachers and multiple classes, a class corresponds to multiple students, a subject corresponds to multiple teachers, a subject can correspond to multiple students studying that subject, and a student can study multiple subjects at the same time. This module realizes precise binding and refined control of teachers, students, classes, and subjects.

[0063] The school management submodule is used to configure and manage basic school information, grade information, and class information, maintain the overall organizational structure of the school, and provide basic structural support for subsequent class teaching and subject teaching.

[0064] The subject management submodule is used to configure and manage all subject information offered by the school, establish a standardized subject catalog, and support teachers and students to carry out intelligent body teaching and practical activities by subject.

[0065] The teacher management submodule is used to create teacher accounts, establish and maintain the binding relationship between teachers and subjects and classes, and configure the scope of teaching permissions for teachers, such as teaching management, task assignment, progress viewing, and AI subject evaluation, so that teachers can carry out systematic AI subject teaching in classes.

[0066] The student management submodule is used to create student accounts, establish and maintain the binding relationship between students and classes, record students' AI learning process data, course completion status and intelligent agent construction results, and form a complete AI learning profile.

[0067] The account authentication submodule provides a unified identity authentication interface for use by the teacher teaching module, student practical module, and intelligent agent construction module to complete user login, identity verification, and permission authentication, ensuring system access security.

[0068] This embodiment ensures standardized teaching processes and refined access control through comprehensive organizational structure management, identity authentication, and relationship binding, providing stable and reliable underlying support for AI-powered intelligent body teaching.

[0069] In one optional implementation, the user center module is also used to interface with the academic affairs system to synchronize teacher teaching data and student learning data to the academic affairs system.

[0070] Specifically, the user center module can connect with the external school's academic affairs system to synchronize the latest data on schools, teachers, students, classes, and subjects from the academic affairs system, and send back data such as teachers' teaching status, students' completion of AI intelligent agent courses, and learning outcomes to the academic affairs system, so that AI intelligent agent teaching tasks can be incorporated into the school's unified teaching management system.

[0071] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. It should be noted that, for those skilled in the art, several equivalent obvious modifications and / or equivalent substitutions can be made without departing from the technical principles of the present invention, and these obvious modifications and / or equivalent substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A teaching intelligent agent development system for the education field, characterized in that, include: The teacher teaching module is used to create and publish intelligent agent construction tasks, track students' progress in executing the intelligent agent construction tasks, and check and guide the finished intelligent agents built by students. The student hands-on module is used to receive intelligent agent construction tasks issued by teachers, perform intelligent agent construction operations, and submit the completed intelligent agent product. The intelligent agent building module provides subject-specific components, structured knowledge services, large model invocation, and prompt word optimization functions to enable teachers and students to build intelligent agents. The large model management module is used to provide various large models to the intelligent agent construction module, and to perform unified management and metering and billing of the various large models connected; The user center module is used for unified management and identity authentication of school information, class information, subject information, teacher information, student information, and account information.

2. The educational intelligent agent development system as described in claim 1, characterized in that, The teacher instruction module is for teachers' use and includes: The AI ​​teaching task management submodule is used to create classes, maintain class student lists, publish AI training course notices, and distribute intelligent agent construction tasks. It also supports the distribution of pre-built intelligent agent templates by teachers to designated classes. The AI ​​teaching progress management submodule is used to statistically analyze the release status of intelligent agent construction tasks, the use of students' intelligent agents, and the quality of homework completion, and supports teachers to intervene in real time to view the entire process of students' intelligent agent construction. The Intelligent Agent Review and Guidance submodule is used to view, grade, provide optimization guidance, and share and publish the finished intelligent agents built by students.

3. The educational intelligent agent development system as described in claim 1, characterized in that, The student practical module is for student use and includes: The AI ​​learning task receiving submodule is used to receive AI training courses, agent usage tasks, and agent construction assignments published by teachers. The AI ​​learning task execution submodule is used to call the intelligent agent construction module to complete the construction of the intelligent agent according to the task requirements, submit the finished intelligent agent, and optimize the intelligent agent based on the teacher's feedback and optimization suggestions.

4. The educational intelligent agent development system as described in claim 1, characterized in that, The intelligent agent construction module includes: The custom interface configuration submodule provides teachers and students with visual configuration capabilities for both PC and mobile interfaces. The subject-specific component library submodule is used to register and manage subject-specific components for the education field through a standardized MCP interface; The structured knowledge service submodule is used to provide semantic retrieval, knowledge reasoning, and context enhancement services for intelligent agent construction.

5. The educational intelligent agent development system as described in claim 4, characterized in that, The custom interface configuration submodule provides teachers and students with front-end interface components and UI design templates, supports drag-and-drop interface layout and optimization, and can call pre-built intelligent agents through configuration to achieve zero-code integration between the front-end interface and the back-end intelligent agents.

6. The educational intelligent agent development system as described in claim 4, characterized in that, The intelligent agent construction module also includes: The access permission verification submodule is used to verify the user's identity, class and subject permissions, and determine whether the user has the permission to build intelligent agents and use components.

7. The educational intelligent agent development system as described in claim 1, characterized in that, The agent building module supports the construction of chat-type agents, task-type agents, and application-type agents; The chat-type intelligent agent is used to realize knowledge Q&A, learning consultation, daily Q&A and multi-round dialogue interaction through dialogue interaction; The task-oriented intelligent agents are suitable for specific teaching scenarios, including essay correction intelligent agents, subject Q&A intelligent agents, paper optimization intelligent agents, and translation proofreading intelligent agents; The application-oriented intelligent agent is used to build complete and independent AI teaching applications, supporting custom prompts, custom knowledge bases, and workflow orchestration of complex business logic.

8. The educational intelligent agent development system as described in claim 1, characterized in that, The large model management module includes: The large model access submodule is used to remotely connect to and call third-party cloud-based large model services through standardized API interfaces; The large model management submodule is used to manage large models deployed locally and privately by schools. The large model measurement submodule is used to record the usage of various large models by school and to complete the measurement and billing statistics based on the usage.

9. The educational intelligent agent development system as described in claim 1, characterized in that, The user center module includes: The school management submodule is used to configure and manage basic school information, grade information, and class information. The subject management submodule is used to configure and manage information about all subjects offered by the school. The teacher management submodule is used to create teacher accounts, configure the binding relationship between teachers and subjects and classes, and set the scope of teachers' teaching permissions. The student management submodule is used to create student accounts, establish a binding relationship between students and classes, and record students' AI learning profiles and intelligent agent construction results. The account authentication submodule provides a unified identity authentication interface for the teacher teaching module, student practical module, and intelligent agent construction module to call to complete user login and identity verification.

10. The educational intelligent agent development system as described in claim 1, characterized in that, The user center module is also used to interface with the academic affairs system to synchronize teacher teaching data and student learning data to the academic affairs system.

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