Teaching mode based on artificial intelligence, recommendation method and system, equipment and medium
By introducing artificial intelligence into college professional course teaching, establishing knowledge graphs and adaptive learning systems, and developing interactive teaching platforms, the problem that traditional teaching models cannot provide personalized guidance is solved, the learning effect and teaching quality are improved, and students are enhanced. Practical application capability.
Patent Information
- Application Number
- CN202411224649.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-03
- Publication Date
- 2024-12-13
AI Technical Summary
The traditional college professional course teaching model cannot provide personalized guidance outside the classroom, making it difficult for students to solve learning problems in a timely manner, and the teaching content cannot be flexibly adjusted according to students' real-time situation, which affects the teaching quality and learning effect.
Adopt an interactive teaching model based on artificial intelligence, combine AI with traditional teaching models, and develop an interactive teaching platform by establishing a knowledge graph and adaptive learning system, providing personalized learning paths and real-time feedback to enhance students' learning enthusiasm and participation. Spend.
It improves students' learning effect and teaching quality, enhances students' practical application ability, reduces teachers' routine work burden, and optimizes the allocation of teaching resources.
Smart Images
Figure CN119128128A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of university course teaching model design, and specifically to an artificial intelligence-based teaching model, recommendation method and system, equipment, and medium. Background Art
[0002] At present, the teaching mode of professional courses in colleges and universities is mainly based on the traditional lecture mode, that is, the teacher is the center, helping students learn new knowledge, expand the concept system, and cultivate students' listening and thinking habits. However, this mode cannot give students explanations or answer questions outside of class time; and this mode requires teachers to spend a lot of time on routine tasks, such as preparing lessons, correcting homework, and making test questions, so they cannot focus most of their energy on the classroom.
[0003] Based on this, a new teaching model, recommendation method, system, equipment and medium based on artificial intelligence is needed. Summary of the invention
[0004] In view of this, the embodiments of this specification provide a teaching model, recommendation method and system, device, and medium based on artificial intelligence, which combines AI with traditional professional course education models online and offline, and can provide students with more personalized education, thereby improving students' learning enthusiasm and participation, improving students' learning effects and teaching quality, and enhancing students' practical application capabilities.
[0005] The embodiments of this specification provide the following technical solutions:
[0006] The embodiment of this specification provides an interactive teaching mode based on artificial intelligence, including:
[0007] Establish a knowledge graph to improve and condense teaching content and teaching methods;
[0008] Establishing an adaptive learning system based on the knowledge graph to provide personalized learning paths and monitoring feedback to adapt to the learning needs and progress of each student;
[0009] Establish an interactive teaching platform, including using artificial intelligence technology to develop interactive teaching tools.
[0010] The embodiment of this specification also provides an interactive teaching system based on artificial intelligence, including:
[0011] The knowledge graph module is used to establish a knowledge graph to improve and condense teaching content and teaching methods, including: establishing a static knowledge graph and establishing a dynamic knowledge graph;
[0012] The static knowledge graph is used to represent the relationship between knowledge points of the course itself and between the course and subject majors;
[0013] The dynamic knowledge graph is used to automatically iterate, evolve and update as the student's training situation changes, forming a knowledge capability system with one graph for each student;
[0014] An adaptive learning system module, which establishes an adaptive learning system based on the knowledge graph to provide personalized learning paths and monitoring feedback to adapt to the learning needs and progress of each student;
[0015] Interactive teaching platform module, using artificial intelligence technology to develop interactive teaching tools.
[0016] The embodiment of this specification also provides a recommendation method for an interactive teaching mode, including:
[0017] Use static knowledge graphs to represent the relationships between knowledge points in the course itself and between courses and disciplines, and build a structured framework for course content;
[0018] Using dynamic knowledge graphs, the knowledge graphs are automatically iterated and updated according to the students’ learning situation, forming a knowledge and ability system with one graph for each student.
[0019] Combined with students' real-time learning data and feedback, personalized learning paths are dynamically adjusted to suit students' learning progress and comprehension ability.
[0020] The embodiment of the present specification also provides an electronic device, including:
[0021] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute: the interactive teaching mode based on artificial intelligence.
[0022] The embodiments of this specification also provide a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the interactive teaching mode based on artificial intelligence is executed.
[0023] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above technical solutions adopted in the embodiments of this specification include at least:
[0024] The teaching model of the present invention combines AI with the traditional teaching model of professional courses in colleges and universities. Through a systematic and intelligent knowledge base and teaching resources, it improves students' learning effect and teaching quality; through personalized and interactive teaching design, it improves students' learning enthusiasm and participation; through practical operations on an interactive teaching platform, it enhances students' practical application ability. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0026] Figure 1 It is a schematic diagram of the structure of the knowledge base in this application;
[0027] Figure 2 It is a schematic diagram of the framework structure of the knowledge base constructed in this application;
[0028] Figure 3 It is a flow chart of the interactive teaching mode based on artificial intelligence in this application. DETAILED DESCRIPTION
[0029] The embodiments of the present application are described in detail below with reference to the accompanying drawings.
[0030] The following describes the implementation methods of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.
[0031] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspect described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.
[0032] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.
[0033] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the examples can be practiced without these specific details.
[0034] At present, the traditional teaching mode of professional courses in colleges and universities is mainly based on the traditional lecture mode, that is, it relies on teachers' lectures to cultivate students' listening and thinking habits. Since teachers need to spend a lot of time to allocate time between various teaching tasks such as preparing lessons, correcting homework, and making test questions, it is difficult to provide continuous individual guidance to each student outside the classroom. Moreover, the time of students and teachers is not synchronized, and it is difficult to find a suitable time for additional explanations or answering questions.
[0035] In view of this, the inventor discovered through research and improvement exploration that the existing traditional teaching model is usually conducted at a fixed time and place, and the "one-size-fits-all" teaching content arrangement cannot adapt to students' learning needs outside the classroom and personalized learning differences, resulting in learning problems encountered by students in learning being difficult to be solved in a timely manner, and the teaching content cannot be flexibly adjusted according to students' real-time learning situation, thus affecting the teaching quality and learning effects.
[0036] Based on this, the embodiment of this specification proposes an interactive teaching model based on artificial intelligence. The overall idea is: to combine artificial intelligence (AI) with the traditional university professional course teaching model both online and offline, and to achieve personalized recommendation, real-time monitoring and dynamic adjustment of teaching content by establishing a complete knowledge graph of the course, developing an adaptive learning system and an interactive teaching platform, so as to improve students' learning enthusiasm, participation and practical application ability, while reducing the routine workload of teachers, optimizing the allocation of teaching resources, and promoting the overall improvement of teaching quality.
[0037] The technical solutions provided by various embodiments of the present application are described below in conjunction with the accompanying drawings.
[0038] like Figure 3 As shown, the embodiment of this specification provides a method based on an interactive teaching mode of artificial intelligence, including:
[0039] Establish a knowledge graph to improve and condense teaching content and teaching methods;
[0040] Establishing an adaptive learning system based on the knowledge graph to provide personalized learning paths and monitoring feedback to adapt to the learning needs and progress of each student;
[0041] Establish an interactive teaching platform, including using artificial intelligence technology to develop interactive teaching tools.
[0042] Specifically, taking the professional course "Principles of Automatic Control" as an example, a complete knowledge map of "Principles of Automatic Control" is established to improve and condense the teaching content and teaching methods;
[0043] Then, using the knowledge graph, we develop an intelligent recommendation algorithm to provide students with personalized learning resources and exercises, ensuring that each student can obtain appropriate learning materials based on their learning progress and comprehension ability. We also provide a feedback mechanism to track students' interactions and homework completion, adjust the learning path in a timely manner, and provide customized tutoring and support.
[0044] And create an interactive teaching platform, integrating AI teaching tools, such as intelligent question and answer, virtual laboratories, etc., to enhance students' practical operation and theoretical application capabilities, thereby realizing the personalization of teaching content, the intelligence of learning paths and the effectiveness of teaching interaction.
[0045] By combining an adaptive learning system and an interactive teaching platform, we provide students with a customized learning experience, ensuring that each student can learn at a pace that suits them while receiving the necessary guidance and resources.
[0046] The embodiments of this specification combine artificial intelligence (AI) with the traditional professional course education model online and offline. AI can serve as a private tutor for students to learn this course. When students encounter knowledge points or difficult problems that they do not understand, it will not only give explanations or answers, but also break down each problem into easy-to-understand steps. When students cannot understand the questions, it can give more understandable explanations based on the students' actual situation and give students some guidance appropriately. When students learn this question, it will also generate a series of targeted exercises to help students better absorb this type of knowledge. In addition, AI is also equivalent to a teacher's assistant, which can help teachers handle routine tasks, thereby improving the efficiency of teachers, allowing teachers to focus more on, for example, encouraging students, establishing good teacher-student relationships, and ensuring that everyone feels cared for and supported, especially those students who need extra help.
[0047] In some implementations, the establishing of the knowledge graph includes: establishing a static knowledge graph and a dynamic knowledge graph;
[0048] The static knowledge graph is used to represent the relationship between knowledge points in the course itself and between the course and subject majors;
[0049] The dynamic knowledge graph is used to automatically iterate, evolve and update as the students' training situations change, forming a knowledge capability system with one graph for each student.
[0050] Specifically, taking the professional course "Principles of Automatic Control" as an example, a static knowledge graph is constructed, such as Figure 1 As shown in the figure, the definition, attributes and logical relationship of each knowledge point in the "Principles of Automatic Control" course are clarified to ensure that students can understand the basic structure and core concepts of the course.
[0051] Build a dynamic knowledge graph that can be updated in real time based on students' learning behaviors and outcomes, reflecting students' mastery of knowledge points and adjusting teaching content and difficulty accordingly.
[0052] In some implementations, FRAGFlow is used in combination with an online large-scale language model through deep document understanding technology to establish the static knowledge graph.
[0053] Specifically, Figure 2 As shown in Figure 2, the knowledge base is constructed using the FRAGFlow+online LLM framework through deep document understanding technology; Figure 1 As shown, by constructing the "Principles of Automatic Control" knowledge base, students can quickly master the basic concepts, basic theories, basic analysis methods, correction and comprehensive design ideas of automatic control systems, and use the time domain method, frequency domain method and root locus method to analyze and synthesize control systems.
[0054] In some embodiments, based on the framework of LLaMA-Factory technical principles, establishing the dynamic knowledge graph includes:
[0055] By collating, cleaning, feeding and parameter tuning of teaching-related data;
[0056] Train a dedicated large language model to understand and generate knowledge associated with the teaching content;
[0057] After training a dedicated large language model, the model is fine-tuned on a specific dataset using the technical principles of LLaMA-Factory to adapt to teaching content and needs.
[0058] Specifically, by organizing, cleaning, feeding and tuning parameters of relevant data (including textbooks, handouts, academic papers, exercises, etc.), we train a dedicated large language model to ensure that the content of the knowledge base is not only extensive but also at the forefront of the industry. This includes collecting relevant data for organization and cleaning, and training, tuning and testing the large language model based on the organized data set to ensure that the model can accurately understand and generate relevant content. The fine-tuning framework adopts the technical principles of LLaMA-Factory.
[0059] After parameter tuning, the model is fine-tuned using the technical principles of LLaMA-Factory. The fine-tuning is performed on a specific dataset and requires multiple iterations until the model's performance reaches the expected teaching effect, so that it can more accurately handle and respond to various situations in teaching.
[0060] In some implementations, establishing an adaptive learning system based on the knowledge graph includes: establishing an intelligent recommendation system and a real-time feedback system;
[0061] The intelligent recommendation system is used to intelligently recommend learning resources and exercises based on students’ learning progress and mastery, so as to provide personalized learning paths and improve learning efficiency;
[0062] The real-time feedback system is used to timely monitor and provide feedback on students' learning situation, provide improvement suggestions, and help students improve their learning outcomes.
[0063] In some embodiments, the interactive teaching tool is an AIGC teaching tool based on a course language model, and includes any one or more of the following: an automatic text generation tool, a data analysis tool, a content visualization tool, a literature reading tool, and a video generation tool.
[0064] Specifically, the artificial intelligence generated content (AIGC) teaching tools based on the language model of the "Principles of Automatic Control" course include automatic copywriting generation tools, data analysis tools, content visualization tools, literature reading tools, video generation, etc.; the system adopts the LLMOps platform, which has the advantages of visual orchestration, out-of-the-box application, and the ability to provide services with back-end as a service API. It covers a complete set of practices and processes for the development, deployment, maintenance and optimization of large-scale language models, ensuring the efficient, scalable and secure use of these powerful AI models to build and run practical applications; these tools are designed to improve teachers' teaching efficiency and students' learning experience through AIGC technology.
[0065] Through these tools, students can conduct experiments in a simulated environment, test control strategies, and obtain instant feedback, thereby deepening their understanding and application of automatic control principles, enhancing teacher-student interaction, and improving teaching effectiveness.
[0066] This manual only takes "Principles of Automatic Control" as an example, but it is also applicable to other professional courses.
[0067] Based on the same inventive concept, the present application also provides a recommendation method for an interactive teaching mode, comprising:
[0068] Use static knowledge graphs to represent the relationships between knowledge points in the course itself and between courses and disciplines, and build a structured framework for course content;
[0069] Using dynamic knowledge graphs, the knowledge graphs are automatically iterated and updated according to the students’ learning situation, forming a knowledge and ability system with one graph for each student.
[0070] Combined with students' real-time learning data and feedback, personalized learning paths are dynamically adjusted to suit students' learning progress and comprehension ability.
[0071] Based on the same inventive concept, the present application also provides an interactive teaching system based on artificial intelligence corresponding to the aforementioned interactive teaching mode example, including:
[0072] The knowledge graph module is used to establish a knowledge graph to improve and condense teaching content and teaching methods, including: establishing a static knowledge graph and establishing a dynamic knowledge graph;
[0073] The static knowledge graph is used to represent the relationship between knowledge points of the course itself and between the course and subject majors;
[0074] The dynamic knowledge graph is used to automatically iterate, evolve and update as the student's training situation changes, forming a knowledge capability system with one graph for each student;
[0075] An adaptive learning system module, which establishes an adaptive learning system based on the knowledge graph to provide personalized learning paths and monitoring feedback to adapt to the learning needs and progress of each student;
[0076] Interactive teaching platform module, using artificial intelligence technology to develop interactive teaching tools.
[0077] Based on the same inventive concept, the present application also provides an electronic device based on an interactive teaching mode of artificial intelligence, the device comprising: a processor, a memory and a computer program; wherein
[0078] The memory is used to store the computer program, and the memory may also be a flash memory. The computer program is, for example, an application program, a functional module, etc. for implementing the above method.
[0079] The processor is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.
[0080] Optionally, the memory can be independent or integrated with the processor.
[0081] When the memory is a device independent of the processor, the device may further include:
[0082] A bus is used to connect the memory and the processor.
[0083] Based on the same inventive concept, the present application also provides a computer storage medium, which stores computer executable instructions. When the computer executable instructions are executed by a processor, they execute: an interactive teaching mode based on artificial intelligence as described in any embodiment of the present application.
[0084] It should be noted that the computer storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination thereof.
[0085] In a possible implementation, the present invention may also provide a form of a program product that implements data processing, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute several steps of the method described in any of the aforementioned embodiments.
[0086] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.
[0087] In this specification, the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments described later, the description is relatively simple, and the relevant parts can be referred to the partial description of the previous embodiments.
[0088] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. An interactive teaching mode based on artificial intelligence, characterized in that: include: Establish a knowledge graph to improve and condense teaching content and teaching methods; Establishing an adaptive learning system based on the knowledge graph to provide personalized learning paths and monitoring feedback to adapt to the learning needs and progress of each student; Establish an interactive teaching platform, including using artificial intelligence technology to develop interactive teaching tools.
2. The interactive teaching mode based on artificial intelligence according to claim 1 is characterized in that: The establishing of the knowledge graph includes: establishing a static knowledge graph and a dynamic knowledge graph; The static knowledge graph is used to represent the relationship between knowledge points in the course itself and between the course and subject majors; The dynamic knowledge graph is used to automatically iterate, evolve and update as the students' training situations change, forming a knowledge capability system with one graph for each student.
3. The interactive teaching mode based on artificial intelligence according to claim 1 is characterized in that: The static knowledge graph is established by using FRAGFlow, combined with an online large-scale language model through deep document understanding technology.
4. The interactive teaching mode based on artificial intelligence according to claim 1 is characterized in that: Based on the framework of LLaMA-Factory technical principles, the dynamic knowledge graph is established including: By collating, cleaning, feeding and parameter tuning of teaching-related data; Train a dedicated large language model to understand and generate knowledge associated with the teaching content; After training a dedicated large language model, the model is fine-tuned on a specific dataset using the technical principles of LLaMA-Factory to adapt to teaching content and needs.
5. The interactive teaching mode based on artificial intelligence according to claim 1 is characterized in that: The establishing of the adaptive learning system according to the knowledge graph includes: establishing an intelligent recommendation system and a real-time feedback system; The intelligent recommendation system is used to intelligently recommend learning resources and exercises based on the student's learning progress and mastery to provide a personalized learning path; The real-time feedback system is used to monitor and provide feedback on students' learning status in a timely manner.
6. The interactive teaching mode based on artificial intelligence according to claim 1 is characterized in that: The interactive teaching tool is an AIGC teaching tool based on the course language model, including any one or more of the following: automatic copywriting generation tool, data analysis tool, content visualization tool, literature reading tool and video generation tool.
7. A recommendation method for an interactive teaching mode, characterized in that: include: Use static knowledge graphs to represent the relationships between knowledge points in the course itself and between courses and disciplines, and build a structured framework for course content; Using dynamic knowledge graphs, the knowledge graphs are automatically iterated and updated according to the students’ learning situation, forming a knowledge and ability system with one graph for each student. Combined with students' real-time learning data and feedback, personalized learning paths are dynamically adjusted to suit students' learning progress and comprehension ability.
8. An interactive teaching system based on artificial intelligence, characterized in that: include: The knowledge graph module is used to establish a knowledge graph to improve and condense teaching content and teaching methods, including: establishing a static knowledge graph and establishing a dynamic knowledge graph; The static knowledge graph is used to represent the relationship between knowledge points of the course itself and between the course and subject majors; The dynamic knowledge graph is used to automatically iterate, evolve and update as the student's training situation changes, forming a knowledge capability system with one graph for each student; An adaptive learning system module, which establishes an adaptive learning system based on the knowledge graph to provide personalized learning paths and monitoring feedback to adapt to the learning needs and progress of each student; Interactive teaching platform module, using artificial intelligence technology to develop interactive teaching tools.
9. An electronic device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute: the interactive teaching mode based on artificial intelligence as described in any one of claims 1-6.
10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by the processor, they perform: an interactive teaching mode based on artificial intelligence as described in any one of claims 1-6.