English teaching interaction method and system based on artificial intelligence

Abstract

The invention relates to the technical field of English teaching interaction methods, and discloses an English teaching interaction method and system based on artificial intelligence, and the method comprises the steps of user English level evaluation, personalized teaching content generation, real-time interaction and feedback, learning progress tracking, adaptive adjustment and the like. The system comprises a user interface module, an evaluation module, a content generation module, an interaction module, a data storage module and an adaptive learning module. According to the invention, by using natural language processing (NLP), machine learning (ML), voice recognition and other technologies, intelligence and individuation of English teaching are realized, customized learning experience, real-time error correction and feedback can be provided according to the actual situation of a user, and a teaching strategy is dynamically adjusted. Compared with a traditional English teaching method, the method can significantly improve the learning efficiency, enhance the learning interestingness, reduce the teaching cost, and has better expandability and flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of English teaching interactive methods, and in particular to an English teaching interactive method and system based on artificial intelligence. Background Art

[0002] With the acceleration of globalization, the importance of English as a key tool for international communication is becoming increasingly prominent. While traditional English teaching methods, such as classroom instruction and self-study based on textbooks, are effective, they also have limitations. For example, classroom instruction often fails to meet the individual learning needs of each student, while self-study based on textbooks lacks interactivity and real-time feedback, leading to low learning efficiency.

[0003] In recent years, with the rapid development of artificial intelligence (AI) technology, its application in education has become increasingly widespread. By simulating human thinking, AI technology can achieve personalized learning, intelligent tutoring, and automated assessment, thereby improving the efficiency and effectiveness of English teaching.

[0004] However, existing AI-based English teaching systems still have some shortcomings. For example, some systems lack accurate assessment of users' English proficiency, resulting in a mismatch between teaching content and actual levels; some systems lack effective interactive mechanisms, failing to stimulate users' interest and enthusiasm in learning; and still others lack real-time monitoring and feedback on users' learning progress, making it difficult to adjust teaching strategies in a timely manner.

[0005] Therefore, how to provide a more intelligent, efficient and personalized English teaching interactive method and system has become a problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In view of the shortcomings of the existing technology, the present invention provides an interactive method and system for English teaching based on artificial intelligence, which solves the problems raised in the above background technology.

[0008] (2) Technical solution

[0009] To achieve the above objectives, the present invention provides the following technical solutions: an artificial intelligence-based English teaching interactive method and system, the interactive method comprising the following steps:

[0010] Step 1: User Information Collection and Analysis: Collect the user's basic information, English proficiency, learning goals, and learning preferences, and use machine learning algorithms to model and analyze the user data to build a user profile;

[0011] Step 2: Personalized learning path planning: Based on user profiles, combined with knowledge graphs and teaching strategy libraries, AI algorithms are used to generate personalized learning paths, including learning content recommendations, learning sequence arrangements, and learning resource matching.

[0012] Step 3: Multimodal interactive teaching: Through speech recognition, natural language processing, and computer vision technologies, multimodal input and output of text, speech, images, and videos are achieved, allowing users to interact with the system in various ways.

[0013] Step 4: Real-time feedback and intelligent assessment: During the learning process, the user's learning status and progress are monitored in real time. Through speech recognition and natural language processing technology, the user's pronunciation, grammar, vocabulary, and fluency are evaluated in real time, and personalized feedback and guidance are provided.

[0014] Step 5: Learning data recording and analysis: Record the user's learning data, including learning time, learning content, practice results, and test scores. Use data analysis technology to mine and analyze the learning data, evaluate the learning effect, and dynamically adjust the learning path and teaching strategy based on the analysis results.

[0015] Preferably, in step 2, the personalized learning path planning further includes:

[0016] Knowledge point difficulty assessment: Using knowledge graphs and machine learning algorithms, the difficulty of each knowledge point is assessed and adaptively adjusted based on the user's English proficiency.

[0017] Dynamic recommendation of learning resources: Dynamically recommend appropriate learning resources based on the user's learning progress and feedback;

[0018] Adaptive adjustment of learning progress: The learning progress is adjusted in real time based on the user's mastery of the knowledge points. For knowledge points that are well mastered, the learning progress can be accelerated. For knowledge points that are poorly mastered, the learning progress can be slowed down and additional practice and guidance can be provided.

[0019] Preferably, the interaction method in step three includes:

[0020] Voice Conversation: Allows users to practice spoken conversations with the AI assistant. The AI assistant can understand the user's voice commands, respond with voice commands, and provide real-time feedback and guidance on the user's pronunciation, grammar, and word choice.

[0021] Text interaction: Allows users to interact with the system through text input, performing grammar practice, writing practice, and reading comprehension. The system provides real-time error correction, scoring, and feedback on user text input.

[0022] Image recognition: Using image recognition technology, we can identify English content in user-uploaded images and provide relevant translations, explanations, and learning resources.

[0023] Scenario simulation: Use virtual reality (VR) or augmented reality (AR) technology to build a virtual English learning scenario, allowing users to immersively experience the English environment and improve their language proficiency.

[0024] Preferably, in step 4, the real-time feedback and intelligent evaluation further include:

[0025] Emotion recognition and analysis: Identify the user's emotional state through voice and tone analysis and expression recognition technology, and adjust teaching strategies based on the user's emotional state;

[0026] Learning style identification: By analyzing the user's learning behavior data, the user's learning style is identified and personalized teaching services are provided based on the user's learning style;

[0027] Error type analysis and attribution: Classify and analyze the errors that occur during the learning process, identify the root causes of the errors, and provide targeted improvement suggestions.

[0028] Preferably, the English teaching interactive system includes a user information collection and analysis module, a personalized learning path planning module, an interactive teaching content generation module, a real-time speech recognition and error correction module, a natural language processing and feedback module, an intelligent evaluation and progress tracking module, a virtual reality and augmented reality module, an interactive community and collaborative learning module, a data analysis and optimization module, and a user interface and experience design module;

[0029] The information collection and analysis module collects the user's basic information, English proficiency, learning goals and learning preference data, uses machine learning algorithms to model and analyze the user data, and constructs a user profile in order to provide users with personalized learning suggestions and teaching content;

[0030] The personalized learning path planning module develops personalized learning plans and learning paths for users based on user profiles and the English teaching syllabus, ensuring that the learning content meets the user's actual needs and progress;

[0031] The interactive teaching content generation module generates multimodal teaching materials including text, audio, video, and interactive exercises based on the personalized learning path, thereby improving the fun and interactivity of learning;

[0032] The real-time speech recognition and error correction module recognizes the user's speech input in real time when the user is practicing speaking, and provides pronunciation guidance and error correction to help the user improve pronunciation accuracy;

[0033] The natural language processing and feedback module analyzes the user's language expression in writing and conversation, and provides instant feedback and suggestions on grammar, word usage, and sentence structure through natural language processing technology;

[0034] The intelligent assessment and progress tracking module intelligently assesses the user's learning outcomes and adjusts the learning path and teaching strategy based on the assessment results;

[0035] The virtual reality and augmented reality modules construct a virtual English learning scene, provide an immersive learning experience through virtual reality (VR) and augmented reality (AR) technologies, and enhance the sense of reality and participation in learning;

[0036] The interactive community and collaborative learning module provides an online community platform that allows users to interact with other learners, participate in collaborative learning projects, and improve their social and collaborative learning abilities;

[0037] The data analysis and optimization module collects and analyzes users’ learning data, including learning time, practice results, and interaction frequency, to optimize teaching methods and system performance;

[0038] The user interface and experience design module is used to design an intuitive and easy-to-use user interface, provide a smooth learning experience, and ensure that users can easily access and use the various functions of the system.

[0039] Preferably, the user information collection and analysis module includes a data preprocessing component, a user portrait building component, a learning behavior tracking component, an analysis and report generation component, and a feedback and iteration component;

[0040] The data preprocessing component cleans, formats and standardizes the collected data in preparation for data analysis;

[0041] The user profile building component uses machine learning algorithms to analyze user data and build a user profile, including the user's English level, learning style, and interests;

[0042] The learning behavior tracking component records the user's learning behavior in the system for analyzing the user's learning habits;

[0043] The analysis and report generation component generates analysis reports based on user data and user portraits to provide decision support for personalized learning path planning;

[0044] The feedback and iteration component collects feedback information based on the user's learning progress and results, continuously optimizes user portraits and learning paths, and improves teaching effectiveness.

[0045] Preferably, the interactive teaching content generation module includes a content creation engine, a user portrait analyzer, an interactive element designer, an adaptive learning controller, and a feedback and evaluation module;

[0046] The content creation engine is responsible for generating and customizing teaching content, including text, audio, video, and interactive exercises. It combines materials from the teaching resource library into complete course units according to certain logic and teaching objectives.

[0047] The user portrait analyzer is used to analyze the data provided by the user information collection and analysis module and generate personalized features of the user so that the content creation engine can adjust the teaching content according to these features.

[0048] The interactive element designer is used to design and integrate various interactive elements to enhance the interactivity and fun of learning.

[0049] The adaptive learning controller dynamically adjusts the difficulty and presentation of teaching content according to the user's learning progress and performance, ensuring that the teaching content matches the user's learning ability.

[0050] The feedback and evaluation system collects user feedback and data during the interactive learning process, evaluates learning effects, and provides a basis for optimizing teaching content and methods.

[0051] Preferably, the real-time speech recognition and error correction module includes a microphone or audio input device, an audio signal preprocessing unit, a feature extractor, a speech recognition engine, a pronunciation evaluation module and a feedback generator;

[0052] The microphone or audio input device collects the user's voice data in real time;

[0053] The audio signal preprocessing unit is used to optimize the speech signal and prepare for subsequent processing;

[0054] The feature extractor extracts key features for recognition from the speech signal;

[0055] The speech recognition engine converts acoustic features into readable text;

[0056] The pronunciation evaluation module is used to score and provide feedback on the user's pronunciation;

[0057] The feedback generator provides users with instant feedback on their pronunciation and correction results.

[0058] Preferably, the experience design module includes a user interface (UI) design component, a user experience (UX) design component, a personalized recommendation engine, a dynamic learning path planner, a gamification element integration, a social interaction component, and a multimedia content manager;

[0059] The user interface (UI) design component: improves the intuitiveness and efficiency of user operations;

[0060] The User Experience (UX) design components: ensure user satisfaction and loyalty during use;

[0061] The personalized recommendation engine provides a customized learning experience and improves learning efficiency.

[0062] The dynamic learning path planner ensures that the learning content matches the user's ability and improves learning outcomes;

[0063] The integration of gamification elements: stimulates users’ learning motivation and sense of participation;

[0064] The social interaction component: establishes a learning community to promote knowledge sharing and mutual learning;

[0065] The multimedia content manager enriches learning resources and enhances the attractiveness of learning content.

[0066] Preferably, the specific steps of the English teaching interactive system are as follows:

[0067] Step 1: User registration and information collection: The user registers an account in the system and fills in personal information. The system collects the user's English level, learning goals and preferences;

[0068] Step 2: User Profile Analysis

[0069] Use the formula: X = f(a,b,c)

[0070] X is the user portrait, f is the portrait data, a is personal information, b is learning history, and c is behavioral data. The system analyzes user data and creates a personalized user portrait.

[0071] Step 3: Personalized teaching content generation: The system generates personalized teaching content based on the user portrait;

[0072] Step 4: Real-time speech recognition and error correction

[0073] Users practice voice, and the system recognizes it in real time.

[0074] Use the formula: S = (a / b) * 100%

[0075] S is the recognition accuracy, a is the number of correctly recognized words, b is the total number of words, and it corrects errors and provides feedback for the system;

[0076] Step 5: Interactive Learning and Practice

[0077] Allow users to participate in interactive exercises, and the system adjusts the difficulty based on user performance;

[0078] Step 6: Learning progress tracking and evaluation

[0079] Enable the system to track users' learning progress and achievements;

[0080] Use the formula:

[0081] Where M is the learning progress, p i is the i-th element in the user feature vector, q i is the i-th element in the teaching content feature vector, m is the feature vector dimension;

[0082] Step 7: Feedback and Optimization

[0083] Provide users with learning experience feedback, and the system optimizes teaching content and user experience based on the feedback;

[0084] Step 8: Data Analysis and Reporting

[0085] Enable the system to analyze learning data and generate reports;

[0086] Step 9: System Update and Maintenance

[0087] The system regularly updates teaching content and technical components.

[0088] (3) Beneficial effects

[0089] Compared with the existing technology, the present invention provides an interactive method and system for English teaching based on artificial intelligence, which has the following beneficial effects:

[0090] 1. Personalized Learning Experience: This system generates personalized teaching content and exercises based on the user's English proficiency, learning history, and preferences, thereby improving the relevance and effectiveness of learning. Compared to the traditional "one-size-fits-all" teaching model, this system better meets the unique needs of each user and improves learning efficiency.

[0091] 2. Real-time Interaction and Feedback: This system utilizes real-time speech recognition and error correction technology to provide immediate evaluation and feedback on users' oral practice, helping them correct pronunciation errors and improve their oral expression. This interactive learning method enhances user engagement and motivation.

[0092] 3. Intelligent Assessment and Progress Tracking: This system dynamically assesses a user's English proficiency and tracks their learning progress and effectiveness. Through data analysis, the system identifies their strengths and weaknesses and adjusts teaching strategies accordingly, helping them learn more effectively.

[0093] 4. Improve learning efficiency: Through adaptive learning algorithms, the present invention can automatically adjust teaching content and difficulty according to the user's learning situation, ensuring that the user is always in the "zone of proximal development", thereby improving learning efficiency and shortening the learning cycle.

[0094] 5. Enhance learning interest: The present invention makes the learning process more interesting and engaging by introducing gamification elements, virtual character interactions, etc., which helps to improve users' learning enthusiasm and persistence.

[0095] 6. Scalability and flexibility: The present invention is based on a modular design and is easy to expand and maintain. New teaching content, functional modules, and language support can be added as needed to adapt to changing teaching needs and technological developments.

[0096] 7. Reduce teaching costs: By automating teaching and assessment, the present invention can reduce dependence on human teachers, thereby reducing teaching costs and enabling more people to enjoy high-quality English education resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 This is a schematic diagram of the overall system architecture of the present invention;

[0098] Figure 2 This is a schematic diagram of the user information collection and analysis module architecture of the present invention;

[0099] Figure 3 A schematic diagram of the interactive teaching content generation module architecture of the present invention;

[0100] Figure 4 This is a schematic diagram of the real-time speech recognition and error correction module architecture of the present invention. DETAILED DESCRIPTION

[0101] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0102] This is an artificial intelligence-based English teaching interactive method and system, the English teaching interactive system includes a user information collection and analysis module, a personalized learning path planning module, an interactive teaching content generation module, a real-time speech recognition and error correction module, a natural language processing and feedback module, an intelligent evaluation and progress tracking module, a virtual reality and augmented reality module, an interactive community and collaborative learning module, a data analysis and optimization module, and a user interface and experience design module;

[0103] The information collection and analysis module collects the user's basic information, English proficiency, learning goals and learning preference data, uses machine learning algorithms to model and analyze the user data, and constructs a user profile in order to provide users with personalized learning suggestions and teaching content;

[0104] The personalized learning path planning module develops personalized learning plans and learning paths for users based on user profiles and the English teaching syllabus, ensuring that the learning content meets the user's actual needs and progress;

[0105] The interactive teaching content generation module generates multimodal teaching materials including text, audio, video, and interactive exercises based on the personalized learning path, thereby improving the fun and interactivity of learning;

[0106] The real-time speech recognition and error correction module recognizes the user's speech input in real time when the user is practicing speaking, and provides pronunciation guidance and error correction to help the user improve pronunciation accuracy;

[0107] The natural language processing and feedback module analyzes the user's language expression in writing and conversation, and provides instant feedback and suggestions on grammar, word usage, and sentence structure through natural language processing technology;

[0108] The intelligent assessment and progress tracking module intelligently assesses the user's learning outcomes and adjusts the learning path and teaching strategy based on the assessment results;

[0109] The virtual reality and augmented reality modules construct a virtual English learning scene, provide an immersive learning experience through virtual reality (VR) and augmented reality (AR) technologies, and enhance the sense of reality and participation in learning;

[0110] The interactive community and collaborative learning module provides an online community platform that allows users to interact with other learners, participate in collaborative learning projects, and improve their social and collaborative learning abilities;

[0111] The data analysis and optimization module collects and analyzes users’ learning data, including learning time, practice results, and interaction frequency, to optimize teaching methods and system performance;

[0112] The user interface and experience design module is used to design an intuitive and easy-to-use user interface, provide a smooth learning experience, and ensure that users can easily access and use the various functions of the system.

[0113] Example 1: English teaching interactive system based on artificial intelligence

[0114] This embodiment describes an artificial intelligence-based English teaching interactive system, which includes the following modules:

[0115] User information collection and analysis module: This module is used to collect and analyze users' basic information, English learning background, learning goals and other data to provide a basis for subsequent personalized teaching.

[0116] English proficiency assessment module: This module assesses the user's English proficiency through a series of tests (for example, vocabulary test, grammar test, oral test, etc.), and divides the user into different levels according to the assessment results.

[0117] Personalized teaching content generation module: This module uses artificial intelligence algorithms (for example, recommendation algorithms, knowledge graphs, etc.) to generate personalized learning content based on the user's English level, learning goals, and interests and hobbies, including course recommendations, exercises, learning materials, etc.

[0118] Real-time speech recognition and error correction module: This module uses speech recognition technology to recognize the user's spoken English in real time, compares it with the standard pronunciation, points out the user's pronunciation errors and provides correction suggestions.

[0119] Real-time interaction and feedback module: This module interacts with users in real time through chatbots, virtual teachers, etc., answers users' questions, provides learning guidance, and adjusts teaching content and strategies based on user feedback.

[0120] Learning progress tracking and adaptive adjustment module: This module tracks the user's learning progress, records the user's learning behavior and performance, and dynamically adjusts the teaching content and difficulty based on this data to adapt to the user's learning rhythm and needs.

[0121] Data storage and management module: This module is used to store and manage all data in the system, including user information, learning records, teaching content, etc., and ensure the security and privacy of the data.

[0122] The operation process of this embodiment

[0123] 1. The user registers and logs into the system through the user interface module and enters personal information and learning goals.

[0124] 2. The user information collection and analysis module collects and analyzes user information.

[0125] 3. The English proficiency assessment module conducts an English proficiency test on the user and sends the test results to the personalized teaching content generation module.

[0126] 4. The personalized teaching content generation module generates personalized learning content based on user information and English proficiency test results, and presents it to users through the user interface module.

[0127] 5. The user learns through the user interface module, for example, practicing speaking.

[0128] 6. The real-time speech recognition and error correction module recognizes the user's spoken English in real time and provides pronunciation correction suggestions.

[0129] 7. Real-time interaction and feedback module interacts with users in real time, answers users' questions, and adjusts teaching content based on users' feedback.

[0130] 8. The learning progress tracking and adaptive adjustment module tracks the user's learning progress and dynamically adjusts the teaching content and difficulty based on the learning records and grades.

[0131] 9. The data storage and management module stores and manages all data in the system.

[0132] 10. The AI algorithm and model library provides necessary algorithm and model support for each module, such as recommendation algorithms, speech recognition models, natural language processing models, etc.

[0133] Example 2: Artificial Intelligence-Based English Speaking Practice Method

[0134] This embodiment describes an artificial intelligence-based spoken English practice method, which includes the following steps:

[0135] 1. The user selects the speaking practice mode through the user interface module.

[0136] 2. The personalized teaching content generation module generates corresponding oral practice questions based on the user's English level and learning goals.

[0137] 3. The user practices speaking through the user interface module and inputs the speech into the real-time speech recognition and error correction module.

[0138] 4. The real-time speech recognition and error correction module recognizes the user's spoken English in real time, compares it with the standard pronunciation, points out the user's pronunciation errors and provides correction suggestions.

[0139] 5. The real-time interaction and feedback module provides real-time feedback and guidance based on the user's oral practice.

[0140] 6. The learning progress tracking and adaptive adjustment module records the user's oral practice data and dynamically adjusts the difficulty and content of oral practice based on the practice data.

[0141] Example 3: English writing assistance method based on artificial intelligence

[0142] This embodiment describes an artificial intelligence-based English writing assistance method, which includes the following steps:

[0143] 1. The user selects the writing practice mode through the user interface module and enters the writing topic.

[0144] 2. The personalized teaching content generation module provides relevant writing guidance and materials based on the user's English level and writing topic.

[0145] 3. Users practice writing through the user interface module and input their compositions into the system.

[0146] 4. The real-time interaction and feedback module uses natural language processing technology to check grammar, recommend vocabulary, and polish the style of users' essays, and provide modification suggestions.

[0147] 5. The learning progress tracking and adaptive adjustment module records the user's writing practice data and dynamically adjusts the difficulty and content of the writing practice based on the practice data.

[0148] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0149] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An interactive method for English teaching based on artificial intelligence, characterized by: The interaction method comprises the following steps: Step 1: User Information Collection and Analysis: Collect the user's basic information, English proficiency, learning goals, and learning preferences, and use machine learning algorithms to model and analyze the user data to build a user profile; Step 2: Personalized learning path planning: Based on user profiles, combined with knowledge graphs and teaching strategy libraries, AI algorithms are used to generate personalized learning paths, including learning content recommendations, learning sequence arrangements, and learning resource matching. Step 3: Multimodal interactive teaching: Through speech recognition, natural language processing, and computer vision technologies, multimodal input and output of text, speech, images, and videos are achieved, allowing users to interact with the system in various ways. Step 4: Real-time feedback and intelligent assessment: During the learning process, the user's learning status and progress are monitored in real time. Through speech recognition and natural language processing technology, the user's pronunciation, grammar, vocabulary, and fluency are evaluated in real time, and personalized feedback and guidance are provided. Step 5: Learning data recording and analysis: Record the user's learning data, including learning time, learning content, practice results, and test scores. Use data analysis technology to mine and analyze the learning data, evaluate the learning effect, and dynamically adjust the learning path and teaching strategy based on the analysis results.

2. The interactive method for English teaching based on artificial intelligence according to claim 1, characterized in that: In step 2, the personalized learning path planning also includes: Knowledge point difficulty assessment: Using knowledge graphs and machine learning algorithms, the difficulty of each knowledge point is assessed and adaptively adjusted based on the user's English proficiency. Dynamic recommendation of learning resources: Dynamically recommend appropriate learning resources based on the user's learning progress and feedback; Adaptive adjustment of learning progress: The learning progress is adjusted in real time based on the user's mastery of the knowledge points. For knowledge points that are well mastered, the learning progress can be accelerated. For knowledge points that are poorly mastered, the learning progress can be slowed down and additional practice and guidance can be provided.

3. The interactive method for English teaching based on artificial intelligence according to claim 1, characterized in that: The interaction method in step three includes: Voice Conversation: Allows users to practice spoken conversations with the AI assistant. The AI assistant can understand the user's voice commands, respond with voice commands, and provide real-time feedback and guidance on the user's pronunciation, grammar, and word choice. Text interaction: Allows users to interact with the system through text input, performing grammar practice, writing practice, and reading comprehension. The system provides real-time error correction, scoring, and feedback on user text input. Image recognition: Using image recognition technology, we can identify English content in user-uploaded images and provide relevant translations, explanations, and learning resources. Scenario simulation: Use virtual reality (VR) or augmented reality (AR) technology to build a virtual English learning scenario, allowing users to immersively experience the English environment and improve their language proficiency.

4. The interactive method for English teaching based on artificial intelligence according to claim 1, characterized in that: In step 4, the real-time feedback and intelligent evaluation also include: Emotion recognition and analysis: Identify the user's emotional state through voice and tone analysis and expression recognition technology, and adjust teaching strategies based on the user's emotional state; Learning style identification: By analyzing the user's learning behavior data, the user's learning style is identified and personalized teaching services are provided based on the user's learning style; Error type analysis and attribution: Classify and analyze the errors that occur during the learning process, identify the root causes of the errors, and provide targeted improvement suggestions.

5. An artificial intelligence-based English teaching interactive system, characterized by: The English teaching interactive system includes a user information collection and analysis module, a personalized learning path planning module, an interactive teaching content generation module, a real-time speech recognition and error correction module, a natural language processing and feedback module, an intelligent evaluation and progress tracking module, a virtual reality and augmented reality module, an interactive community and collaborative learning module, a data analysis and optimization module, and a user interface and experience design module. The information collection and analysis module collects the user's basic information, English proficiency, learning goals and learning preference data, uses machine learning algorithms to model and analyze the user data, and constructs a user profile in order to provide users with personalized learning suggestions and teaching content; The personalized learning path planning module develops personalized learning plans and learning paths for users based on user profiles and the English teaching syllabus, ensuring that the learning content meets the user's actual needs and progress; The interactive teaching content generation module generates multimodal teaching materials including text, audio, video, and interactive exercises based on the personalized learning path, thereby improving the fun and interactivity of learning; The real-time speech recognition and error correction module recognizes the user's speech input in real time when the user is practicing speaking, and provides pronunciation guidance and error correction to help the user improve pronunciation accuracy; The natural language processing and feedback module analyzes the user's language expression in writing and conversation, and provides instant feedback and suggestions on grammar, word usage, and sentence structure through natural language processing technology; The intelligent assessment and progress tracking module intelligently assesses the user's learning outcomes and adjusts the learning path and teaching strategy based on the assessment results; The virtual reality and augmented reality modules construct a virtual English learning scene, provide an immersive learning experience through virtual reality (VR) and augmented reality (AR) technologies, and enhance the sense of reality and participation in learning; The interactive community and collaborative learning module provides an online community platform that allows users to interact with other learners, participate in collaborative learning projects, and improve their social and collaborative learning abilities; The data analysis and optimization module collects and analyzes users’ learning data, including learning time, practice results, and interaction frequency, to optimize teaching methods and system performance; The user interface and experience design module is used to design an intuitive and easy-to-use user interface, provide a smooth learning experience, and ensure that users can easily access and use the various functions of the system.

6. The artificial intelligence-based English teaching interactive system according to claim 5, characterized in that: The user information collection and analysis module includes a data preprocessing component, a user portrait building component, a learning behavior tracking component, an analysis and report generation component, and a feedback and iteration component; The data preprocessing component cleans, formats and standardizes the collected data in preparation for data analysis; The user profile building component uses machine learning algorithms to analyze user data and build a user profile, including the user's English level, learning style, and interests; The learning behavior tracking component records the user's learning behavior in the system for analyzing the user's learning habits; The analysis and report generation component generates analysis reports based on user data and user portraits to provide decision support for personalized learning path planning; The feedback and iteration component collects feedback information based on the user's learning progress and results, continuously optimizes user portraits and learning paths, and improves teaching effectiveness.

7. The artificial intelligence-based English teaching interactive system according to claim 5, characterized in that: The interactive teaching content generation module includes a content creation engine, a user portrait analyzer, an interactive element designer, an adaptive learning controller, and a feedback and evaluation module; The content creation engine is responsible for generating and customizing teaching content, including text, audio, video, and interactive exercises. It combines materials in the teaching resource library into complete course units according to certain logic and teaching objectives. The user portrait analyzer is used to analyze the data provided by the user information collection and analysis module to generate user personalized features so that the content creation engine can adjust the teaching content according to these features; The interactive element designer is used to design and integrate various interactive elements to enhance the interactivity and fun of learning; The adaptive learning controller dynamically adjusts the difficulty and presentation of teaching content based on the user's learning progress and performance, ensuring that the teaching content matches the user's learning ability; The feedback and evaluation system collects user feedback and data during the interactive learning process, evaluates learning effects, and provides a basis for optimizing teaching content and methods.

8. The artificial intelligence-based English teaching interactive system according to claim 5, characterized in that: The real-time speech recognition and error correction module includes a microphone or audio input device, an audio signal preprocessing unit, a feature extractor, a speech recognition engine, a pronunciation evaluation module and a feedback generator; The microphone or audio input device collects the user's voice data in real time; The audio signal preprocessing unit is used to optimize the speech signal and prepare for subsequent processing; The feature extractor extracts key features for recognition from the speech signal; The speech recognition engine converts acoustic features into readable text; The pronunciation evaluation module is used to score and provide feedback on the user's pronunciation; The feedback generator provides users with instant feedback on their pronunciation and correction results.

9. The artificial intelligence-based English teaching interactive system according to claim 5, characterized in that: The experience design module includes a user interface (UI) design component, a user experience (UX) design component, a personalized recommendation engine, a dynamic learning path planner, gamification element integration, a social interaction component, and a multimedia content manager; The user interface (UI) design component: improves the intuitiveness and efficiency of user operations; The User Experience (UX) design components: ensure user satisfaction and loyalty during use; The personalized recommendation engine provides a customized learning experience and improves learning efficiency. The dynamic learning path planner ensures that the learning content matches the user's ability and improves learning outcomes; The integration of gamification elements: stimulates users’ learning motivation and sense of participation; The social interaction component: establishes a learning community to promote knowledge sharing and mutual learning; The multimedia content manager enriches learning resources and enhances the attractiveness of learning content.

10. The artificial intelligence-based English teaching interactive system according to claim 5, characterized in that: The specific steps of the English teaching interactive system are as follows: Step 1: User registration and information collection: The user registers an account in the system and fills in personal information. The system collects the user's English level, learning goals and preferences; Step 2: User portrait analysis: Use the formula: X = f(a, b, c); X is the user portrait, f is the portrait data, a is personal information, b is learning history, and c is behavioral data. The system analyzes user data and creates a personalized user portrait. Step 3: Personalized teaching content generation: The system generates personalized teaching content based on the user portrait; Step 4: Real-time speech recognition and error correction: Users practice voice, and the system recognizes it in real time. Use the formula: S = (a / b) * 100%; S is the recognition accuracy, a is the number of correctly recognized words, b is the total number of words, and it corrects errors and provides feedback for the system; Step 5: Interactive Learning and Practice: Allow users to participate in interactive exercises, and the system adjusts the difficulty based on user performance; Step 6: Learning progress tracking and evaluation: Enable the system to track users' learning progress and achievements; Use the formula: Among them, M is the learning progress, p i is the i-th element in the user feature vector, q i is the i-th element in the teaching content feature vector, m is the feature vector dimension; Step 7: Feedback and Optimization: Provide users with learning experience feedback, and the system optimizes teaching content and user experience based on the feedback; Step 8: Data Analysis and Reporting: Enable the system to analyze learning data and generate reports; Step 9: System Update and Maintenance: The system regularly updates teaching content and technical components.

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