Interactive English teaching method and system

Abstract

The invention discloses an interactive English teaching method, which comprises the following steps: S1, constructing a virtual learning environment by using a virtual reality technology, S2, comprehensively evaluating the English levels of students through an artificial intelligence algorithm, and generating a personalized learning path for each student according to an evaluation result; s3, constructing interactive learning activities and feeding back the interactive learning activities in real time, S4, collecting and analyzing interactive learning activity data of students, selecting and establishing online communities, and issuing cooperative learning tasks in the communities; s5, collecting learning data of the students in all the learning paths, and analyzing the learning data; and S6, according to a learning data analysis result, a teaching strategy is adjusted in time. The virtual reality technology and the artificial intelligence technology are combined, brand-new, personalized and immersive English learning experience is provided for students, and the English teaching quality and the learning effect of the students are expected to be remarkably improved.

Description

Technical Field

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

[0002] As we all know, English is a language subject that is studied very widely. For students who have gone through the college entrance examination stage, in addition to improving vocabulary, grammar learning, professionalism, etc., oral English teaching is also very necessary. Especially for students who have just entered university, it is very necessary to improve their oral English expression ability.

[0003] In addition, existing English teaching generally still adopts the more traditional small-class teaching, and conducts situational English oral teaching in the class, which limits the English teaching scene. Therefore, for the teaching of spoken English, due to the inconsistency of English proficiency, it is difficult to provide targeted teaching for students of different levels. Summary of the invention

[0004] To this end, the present invention provides an interactive English teaching method and system to solve the problems of single English teaching scene and lack of personalized teaching in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] An interactive English teaching method comprises the following steps:

[0007] S1. Use virtual reality technology to build a virtual learning environment to simulate a real English communication environment. The virtual learning environment has built-in test scenarios for comprehensive English proficiency assessment. Students can choose or customize virtual scenarios according to their own learning needs and interests.

[0008] S2. Based on the test data collected in the test scenario, the AI ​​algorithm is used to comprehensively evaluate the students’ English proficiency. Based on the evaluation results, a personalized learning path is generated for each student, and learning resources and activities suitable for their level are recommended;

[0009] S3. Construct interactive learning activities of different levels based on the English proficiency assessment results and provide real-time feedback; design role-playing activities in a virtual environment to allow students to practice oral English in simulated real-life scenarios; AI tools provide real-time feedback when students practice using learning resources;

[0010] S4. Collect students’ interactive learning activity data and segment student groups through clustering algorithms; based on the segmentation results, identify student groups with similar characteristics, which will form the basis of online communities; select and establish multiple different online communities to accommodate students with different English proficiency levels; and publish collaborative learning tasks in the communities;

[0011] S5. Collect students’ learning data in all learning paths, including learning time, learning progress, and learning outcomes, and then analyze these learning data; interactive learning activities include interactive games based on English learning goals, which improve students’ learning interest and participation through gamification;

[0012] S6. Adjust teaching strategies and learning resources in a timely manner based on the results of learning data analysis to ensure the pertinence and effectiveness of teaching content and methods.

[0013] Further: For the construction of virtual learning environment, adopt virtual reality development engine, use the tools and resources provided by the development engine platform to build the layout of virtual scene and the placement of objects; write scripts to realize the object interaction and scene switching functions, and improve the interactivity and fun of the software;

[0014] When developing and building a virtual learning environment, 3D rendering algorithms are used to convert 3D models into images that can be processed by computers to present realistic virtual scenes; collision detection algorithms are used to detect collisions between objects in the virtual environment to prevent objects from penetrating or overlapping; the students' sense of reality in the virtual environment is improved and the learning effect is enhanced; AI intelligent algorithms are used to control intelligent entities in the virtual environment, realize complex interaction logic and behavior simulation, and improve the intelligence level of the virtual learning environment; data visualization algorithms are used to display large amounts of data to students in a graphical way to help them better understand and learn; support layout changes of digital and visual codes to meet multiple learning goals and improve accuracy; motion capture algorithms are used to capture the motion trajectory of objects in the real world and apply them to the virtual environment; improve the smoothness and realism of the movements of virtual characters or objects and enhance the sense of immersion.

[0015] Further: The method of comprehensively evaluating English proficiency through artificial intelligence algorithms is as follows:

[0016] (1) Using natural language processing (NLP) technology, the words in the text collected and converted in the test scenario are converted into high-dimensional vectors, thereby capturing the semantic relationship between words and evaluating students' mastery of vocabulary and the breadth of their vocabulary; using technologies such as dependency parsing to analyze the structure of sentences and evaluate students' mastery of grammar;

[0017] (2) Use machine learning algorithms to assess students’ oral fluency; by collecting a large number of oral samples and annotating their fluency levels, train the model to automatically identify the fluency level of new samples;

[0018] (3) Clustering algorithms are used to group students’ learning performance in order to discover groups of students with similar characteristics and provide a reference for the formulation of personalized learning paths.

[0019] Further: The steps for generating a personalized learning path are as follows:

[0020] (1) Initial assessment:

[0021] We use natural language processing (NLP) and machine learning algorithms to design English proficiency test questions covering listening, speaking, reading and writing to comprehensively assess students’ English proficiency. If students have used certain English learning platforms or apps before, we analyze their learning records, completed tasks and test scores to understand their learning habits and progress.

[0022] (2) Personalized learning path design:

[0023] Recommend learning materials, online courses, and applications suitable for students based on their English level and learning needs; dynamically adjust the difficulty and content of the learning path as students progress.

[0024] Further: The method of establishing an online community is as follows:

[0025] (1) Determine the theme and goals of the community: Make it clear that the theme of the community is English learning, and the goal is to encourage students to share their learning experiences. Design the name, logo, and slogan of the community based on the theme and goals of the community;

[0026] (2) Choose a suitable platform: Choose a platform suitable for building an online community.

[0027] (3) Create a community and set rules: Create a community on the selected platform and set group announcements and group rules. Group announcements should clearly state the theme, goals, and activity schedule of the community. Group rules should specify the behavioral norms of community members.

[0028] (4) Invite members to join: Invite students to join the community through class groups, campus announcements, and social media channels; set entry thresholds to ensure that the learning levels of community members are at a relatively similar level;

[0029] (5) Create a community atmosphere: regularly publish information and resources related to English learning in the community, encourage members to share their learning experiences and exchange learning experiences; organize online or offline English learning activities to enhance the cohesion and activity of the community.

[0030] Further: The method of publishing collaborative learning tasks is as follows:

[0031] (1) Designing tasks: Based on the theme and goals of the community, design learning tasks that require group collaboration. Task design should take into account students’ English proficiency, interests, and practical abilities to ensure that the tasks are both challenging and feasible.

[0032] (2) Publish tasks: Publish task announcements in the community, clearly state the task’s theme, objectives, requirements, and deadline; and provide necessary resources and guidance;

[0033] (3) Grouping and division of labor: Students are grouped according to their English proficiency, interests, specialties, and personality traits to ensure that the members of each group are diverse and complementary; clear division of labor within the group to ensure that every member can participate in the task;

[0034] (4) Supervision and guidance: During the execution of tasks, regularly monitor the progress and quality of the team and provide necessary guidance and assistance; encourage communication and cooperation between teams to promote resource sharing and complementary advantages;

[0035] (5) Evaluation and Feedback: After the task deadline, evaluate the group's results; provide detailed feedback, point out the group's strengths and weaknesses, and encourage group members to reflect and improve.

[0036] Further: Interactive learning activities include interactive games based on English learning objectives, which increase students' learning interest and engagement through gamification.

[0037] Further: Regularly reassess students’ English proficiency and adjust the learning path based on the assessment results to ensure that the student’s learning path always matches their actual level to achieve the best learning effect.

[0038] In order to achieve the above object, the present invention also provides the following technical solution:

[0039] An interactive English teaching system, comprising:

[0040] The virtual learning environment construction module uses Unity or Unreal Engine as the virtual reality development engine, combining 3D rendering algorithms, collision detection algorithms, AI intelligent algorithms, data visualization algorithms, and motion capture algorithms to build a realistic virtual learning environment;

[0041] The assessment and learning path planning module uses NLP technology to assess vocabulary and semantic relationships, analyze and assess grammar mastery; uses algorithms such as logistic regression and SVM to assess oral fluency; uses clustering algorithms to group students' learning performance; analyzes students' learning records and recommends personalized learning materials, courses and activities based on assessment results; designs dynamic learning paths, adjusts difficulty and content according to students' progress; regularly reassesses students' levels and adjusts learning paths;

[0042] Interactive learning activity module, designing role-playing and interactive game learning activities in a virtual environment; AI tools provide real-time feedback on pronunciation correction and grammatical error prompts;

[0043] Online community module: determine the community theme and goals, select the appropriate platform to create the community, set rules, invite members to join, and create a community atmosphere; design challenging and feasible learning tasks, publish task announcements in the community, provide resources and guidance, group and divide the work, supervise and guide, evaluate and provide feedback;

[0044] The data statistics and analysis module collects students' learning data in virtual environments, online communities and collaborative learning tasks, and uses the system's built-in statistical tools or Python programming language to perform complex statistical analysis.

[0045] Going a step further: the virtual learning environment includes virtual classrooms, virtual libraries, virtual historical scenes, and virtual cafes, which students can choose or customize according to their interests and needs.

[0046] The present invention has the following advantages: the virtual learning environment constructed by virtual reality technology provides students with an immersive English learning experience, enhancing their interest and participation in learning; personalized learning path planning based on intelligent evaluation can meet the learning needs of different students and improve learning effects;

[0047] Design a variety of interactive learning activities to allow students to learn English through interaction and improve their oral expression and comprehensive application skills; promote communication and cooperation among students through online communities and cooperative learning tasks, and cultivate their teamwork and communication skills.

[0048] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practicing the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more intuitively illustrate the prior art and the present application, exemplary drawings are given below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing the present application; for example, those skilled in the art are capable of easily making conventional adjustments or further optimizations to the addition / reduction / attribution division, specific shapes, positional relationships, connection methods, dimensional ratios, etc. of certain units (components) based on the technical concepts and exemplary drawings disclosed in the present application.

[0050] Figure 1 A flowchart of an interactive English teaching method provided for one embodiment of the present application.

[0051] Figure 2 A system block diagram of an interactive English teaching system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following is an explanation of the implementation of the present invention by specific specific examples. People familiar with this technology can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. It should be understood that these embodiments are only to further illustrate the present invention and cannot be understood as limiting the scope of protection of the present invention. Technical engineers in this field can make some non-essential improvements and adjustments to the present invention based on the contents of the above invention; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 , an interactive English teaching method, comprising the following steps:

[0054] S1. Build a virtual learning environment. Use virtual reality technology to build a virtual learning environment, including virtual classrooms, virtual libraries, virtual historical scenes, virtual cafes and other scenes to simulate a real English communication environment; students can choose or customize virtual scenes according to their learning needs and interests, such as choosing a virtual classroom with a specific theme (such as travel, business, daily conversations, etc.) for immersive learning.

[0055] The virtual learning environment has built-in test scenarios for comprehensively evaluating English proficiency, including oral, vocabulary, and grammar tests. The test scenarios can collect students' test data, which can be converted into text format.

[0056] For the construction of virtual learning environment, Unity and Unreal Engine, two commonly used cross-platform virtual reality development engines, can be used. They provide a wealth of development tools and resources; use the tools and resources provided by the development platform to build the layout of the virtual scene, the placement of objects, etc. In addition, you can also design the interaction between students and the virtual environment, such as gesture recognition, voice control, etc.; write scripts to implement functions such as object interaction and scene switching, and improve the interactivity and fun of the software.

[0057] When developing and building a virtual learning environment, 3D rendering algorithms can be used to convert 3D models into images that can be processed by computers to present realistic virtual scenes; the original graphics can be processed through algorithms to calculate elements such as light, shadow, and material to improve the realism of the scene.

[0058] A collision detection algorithm is used to detect collisions between objects in a virtual environment and prevent objects from penetrating or overlapping; this can improve students' operational realism in a virtual environment and enhance learning outcomes.

[0059] AI intelligent algorithms are used to control intelligent entities in virtual environments, such as virtual teachers, virtual students, etc.; complex interactive logic and behavior simulation can be implemented to improve the intelligence level of the virtual learning environment.

[0060] Data visualization algorithms are used to present large amounts of data to students in a graphical way to help them better understand and learn; it can support layout changes of various numbers and visual codes to meet multiple learning objectives and improve accuracy.

[0061] Motion capture algorithms are used to capture the motion trajectory of objects in the real world and apply them to the virtual environment; this can improve the smoothness and realism of the movements of virtual characters or objects and enhance the sense of immersion.

[0062] S2. Based on the test data collected in the test scenarios, the learning path is planned after the English learning level is assessed. The students’ English level is comprehensively assessed through artificial intelligence algorithms, including vocabulary, grammar mastery, oral fluency, etc. Based on the assessment results, a personalized learning path is generated for each student, and learning resources and activities suitable for their level are recommended.

[0063] The method of comprehensively assessing English proficiency through artificial intelligence algorithms is as follows:

[0064] (1) Using natural language processing (NLP) technology, Word2Vec, GloVe and other algorithms are used to convert words in the text into high-dimensional vectors, thereby capturing the semantic relationship between words. This helps to assess students' mastery of vocabulary and the breadth of their vocabulary; through dependency parsing and other technologies, the structure of sentences is analyzed to assess students' mastery of grammar.

[0065] (2) Classification algorithms such as logistic regression and support vector machine (SVM) are used to evaluate students' oral fluency. By collecting a large number of oral samples and annotating their fluency levels, the model is trained to automatically identify the fluency level of new samples.

[0066] (3) Use clustering algorithms (such as K-means) to group students' learning performance in order to find groups of students with similar characteristics and provide a reference for the formulation of personalized learning paths.

[0067] The method for generating personalized learning paths is as follows:

[0068] (1) Initial assessment:

[0069] Use NLP and machine learning algorithms to design English proficiency test questions, including vocabulary tests, grammar tests, listening tests, and speaking tests; the test can cover four aspects: listening, speaking, reading, and writing, to comprehensively evaluate students' English proficiency.

[0070] If students have used certain English learning platforms or apps before, their learning records, completed tasks, and test scores can be analyzed to understand their learning habits and progress.

[0071] (2) Personalized learning path design:

[0072] Recommend suitable learning materials, online courses, applications, etc. to students based on their English level and learning needs. For example, for students with insufficient vocabulary, vocabulary learning applications can be recommended; for students whose oral fluency needs to be improved, oral practice software or English corner activities can be recommended.

[0073] Design dynamic learning paths: As students progress, the difficulty and content of the learning path can be adjusted dynamically. For example, when students have mastered a certain grammar point, more complex exercises can be added or new grammar points can be introduced.

[0074] S3. Based on the assessment results of English proficiency, construct interactive learning activities of different levels and provide real-time feedback. Design role-playing activities in a virtual environment, such as simulated business negotiations, travel directions, etc., so that students can practice oral English in simulated real-life scenarios.

[0075] Develop interactive games based on English learning objectives, such as vocabulary chain games and grammar puzzles, to increase students' learning interest and participation through gamification;

[0076] When students use learning resources for practice, AI tools can provide real-time feedback, such as pronunciation correction and grammatical error prompts, which helps students correct errors in a timely manner and improve learning efficiency.

[0077] Regularly reassessing students' English proficiency and adjusting their learning paths based on the assessment results can ensure that students' learning paths always match their actual level, thereby achieving the best learning results.

[0078] In addition, you can also introduce English movies, music, games and other multimedia resources to enrich the learning content and increase the interest of learning.

[0079] S4. Collect students’ interactive learning activity data and segment student groups through clustering algorithms; based on the segmentation results, identify student groups with similar characteristics, which will form the basis of online communities; select and establish multiple different online communities to accommodate students with different English proficiency levels; and publish collaborative learning tasks;

[0080] Use the system's built-in statistical tools, such as participation statistics and performance statistics, to collect and analyze students' learning activity data;

[0081] For scenarios that require complex statistics and analysis, such as sentiment analysis of learning activities and topic clustering, you can use programming languages ​​such as Python for automated statistics; write scripts to capture text data such as discussion content and learning experience sharing in the community, and then use natural language processing (NLP) technology to perform sentiment analysis, topic clustering and other statistical and analytical work.

[0082] By establishing an online English learning community, students are encouraged to share and exchange learning experiences in the community; learning tasks that require group cooperation are designed, such as jointly planning a virtual English party, writing a virtual English magazine, etc., to improve students' teamwork ability and comprehensive English application ability through cooperative learning.

[0083] Here’s how to build an online community:

[0084] (1) Analysis of student characteristics:

[0085] Utilize the collected student data (such as English proficiency, learning style, interests, etc.) and segment the student groups through clustering algorithms (such as K-means, DBSCAN, etc.); based on the segmentation results, identify student groups with similar characteristics, which will form the basis of the online community.

[0086] (2) Community building:

[0087] For each identified group of students, create an online community, which could be a discussion group, study group, or interest group based on a specific topic. Give the community a clear name, description, and rules so that students understand the purpose of the community and how to participate.

[0088] (3) Member invitation and management:

[0089] Invite students who meet the community characteristics to join through system notifications or emails; implement community management strategies, such as setting administrators, formulating community norms, and regularly maintaining community activity.

[0090] The method of publishing collaborative learning tasks is as follows:

[0091] (1) Task design:

[0092] 1) Goal setting: Based on the community topic and student level, use algorithms such as decision trees or rule engines to set clear learning goals;

[0093] For example, if the community theme is "Business English Communication", the goal may be set as "Improve students' English communication skills in business scenarios."

[0094] 2) Task generation: Based on learning objectives and community member characteristics, a variety of cooperative learning tasks are generated using random generation algorithms or template matching algorithms;

[0095] For example, a task of "simulating business negotiation" is generated for the "Business English Communication" community.

[0096] (2) Task release:

[0097] Through programming, the API interface of the social platform is connected, and algorithms such as HTTP requests are used to automatically push task information to the community; for example, a POST request is sent using Python's requests library to upload task title, content, resource link and other information to the API endpoint of the social platform.

[0098] (3) Task allocation:

[0099] 1) Student portrait: Use machine learning algorithms (such as SVM) to build student portraits based on students’ historical learning data, interest preferences, ability levels, etc.

[0100] 2) Role allocation: Based on student profiles and task requirements, greedy algorithms or heuristic search algorithms are used to automatically allocate task roles or groups;

[0101] For example, in the "simulated business negotiation" task, students who are good at oral expression are assigned as negotiation representatives, and students who are good at information collection are assigned as logistical support.

[0102] (4) Task tracking and feedback:

[0103] 1) Progress tracking: Through data analysis algorithms (such as time series analysis, state machines, etc.), real-time tracking of task progress and member participation; for example, statistics on the number of times each student speaks in the task, the quality of the submitted homework, and other data.

[0104] 2) Performance evaluation: Use natural language processing (NLP) algorithms or machine learning algorithms to automatically evaluate students’ assignments and speeches, and provide feedback and scores. For example, use NLP algorithms to analyze students’ oral expressions and evaluate their fluency, grammatical accuracy, etc.

[0105] S5. Collect and analyze learning data, including learning time, learning progress, learning outcomes, and other data from all students’ learning paths in the virtual environment (such as interactive learning activities, online community activities, collaborative learning tasks, etc.), and then analyze these learning data.

[0106] S6. Readjust teaching strategies and learning resources in a timely manner based on the results of learning data analysis to ensure the pertinence and effectiveness of teaching content and methods.

[0107] The present invention also provides an interactive English teaching system, comprising:

[0108] The virtual learning environment building module uses Unity or Unreal Engine as the virtual reality development engine, combined with 3D rendering algorithms, collision detection algorithms, AI intelligent algorithms, data visualization algorithms and motion capture algorithms to build a realistic virtual learning environment; including virtual classrooms, virtual libraries, virtual historical scenes, virtual cafes, etc., students can choose or customize according to their interests and needs.

[0109] The assessment and learning path planning module uses NLP technology (such as Word2Vec and GloVe) to assess vocabulary and semantic relationships; assesses grammar mastery through dependency syntax analysis; uses algorithms such as logistic regression and SVM to assess oral fluency; uses clustering algorithms (such as K-means) to group students' learning performance; analyzes students' learning records and recommends personalized learning materials, courses, and activities based on assessment results; designs dynamic learning paths and adjusts difficulty and content according to student progress; and regularly reassesses students' levels and adjusts learning paths.

[0110] Interactive learning activity module, which designs learning activities such as role-playing and interactive games in a virtual environment; AI tools provide real-time feedback such as pronunciation correction and grammatical error prompts.

[0111] Online community module: determine the community theme and goals, select a suitable platform to create the community, set rules, invite members to join, and create a community atmosphere; design challenging and feasible learning tasks, publish task announcements in the community, provide resources and guidance, group and divide labor, supervise and guide, evaluate and provide feedback.

[0112] The data statistics and analysis module collects students' learning data in virtual environments, online communities and collaborative learning tasks, and performs complex statistical analysis using the system's built-in statistical tools or programming languages ​​such as Python.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An interactive English teaching method, characterized in that: The following steps are involved: S1. Use virtual reality technology to build a virtual learning environment to simulate a real English communication environment; the virtual learning environment has built-in test scenarios for comprehensive English proficiency assessment. Students can choose or customize virtual scenarios according to their own learning needs and interests for immersive learning; S2. Based on the test data collected from the test scenario, the AI ​​algorithm is used to comprehensively evaluate the English proficiency of students. Based on the evaluation results, a personalized learning path is generated for each student, and learning resources and activities suitable for their level are recommended. The method of comprehensively evaluating English proficiency through the AI ​​algorithm is as follows: (1) Using natural language processing (NLP) technology, the words in the text collected and converted in the test scenario are converted into high-dimensional vectors, thereby capturing the semantic relationship between words and evaluating students' mastery of vocabulary and the breadth of their vocabulary; using technologies such as dependency parsing to analyze the structure of sentences and evaluate students' mastery of grammar; (2) Use machine learning algorithms to assess students’ oral fluency; by collecting a large number of oral samples and annotating their fluency levels, train the model to automatically identify the fluency level of new samples; (3) Clustering algorithms are used to group students’ learning performance in order to find groups of students with similar characteristics and provide a reference for the formulation of personalized learning paths; S3. Construct interactive learning activities of different levels based on the English proficiency assessment results and provide real-time feedback; design role-playing activities in a virtual environment to allow students to practice oral English in simulated real-life scenarios; AI tools provide real-time feedback when students practice using learning resources; S4. Collect students’ interactive learning activity data and segment student groups through clustering algorithms; based on the segmentation results, identify student groups with similar characteristics. These groups will form the basis of online communities, and select and establish multiple different online communities to accommodate students with different English proficiency levels; And publish collaborative learning tasks in the community; S5. Collect students’ learning data in all learning paths, including learning time, learning progress, and learning outcomes, and then analyze these learning data. All learning paths include interactive learning activities in virtual scenes, online community activities, and collaborative learning tasks; S6. Adjust teaching strategies and learning resources in a timely manner based on the results of learning data analysis to ensure the pertinence and effectiveness of teaching content and methods.

2. An interactive English teaching method according to claim 1, characterized in that: For the construction of virtual learning environment, we use virtual reality development engine, and utilize the tools and resources provided by the development engine platform to build the layout of virtual scenes and the placement of objects; we write scripts to realize the functions of object interaction and scene switching, and improve the interactivity and fun of the software; When developing and building a virtual learning environment, 3D rendering algorithms are used to convert 3D models into images that can be processed by computers to present realistic virtual scenes; collision detection algorithms are used to detect collisions between objects in the virtual environment to prevent objects from penetrating or overlapping; improve students' sense of reality in the virtual environment and enhance learning effects; AI intelligent algorithms are used to control intelligent entities in the virtual environment, realize complex interaction logic and behavior simulation, and improve the intelligence level of the virtual learning environment; data visualization algorithms are used to display large amounts of data to students in a graphical way to help them better understand and learn; Supports layout changes of digital and visual encodings to meet multiple learning goals and improve accuracy; uses motion capture algorithms to capture the motion trajectory of objects in the real world and apply them to the virtual environment; Improve the smoothness and realism of the movements of virtual characters or objects and enhance the sense of immersion.

3. An interactive English teaching method according to claim 1, characterized in that: The steps for generating a personalized learning path are as follows: (1) Initial assessment: We use natural language processing (NLP) and machine learning algorithms to design English proficiency test questions covering listening, speaking, reading and writing to comprehensively assess students’ English proficiency. If students have used certain English learning platforms or apps before, we analyze their learning records, completed tasks and test scores to understand their learning habits and progress. (2) Personalized learning path design: Recommend learning materials, online courses, and applications suitable for students based on their English level and learning needs; dynamically adjust the difficulty and content of the learning path as students progress.

4. An interactive English teaching method according to claim 1, characterized in that: In online communities, we write scripts to capture text data in the community, and then use natural language processing (NLP) technology to perform sentiment analysis and topic clustering for statistics and analysis.

5. An interactive English teaching method according to claim 1, characterized in that: Interactive learning activities include interactive games based on English learning objectives, which increase students' learning interest and engagement through gamification.

6. An interactive English teaching method according to any one of claims 1 to 5, characterized in that: Regularly reassess students' English proficiency and adjust their learning paths based on the assessment results to ensure that students' learning paths always match their actual level to achieve the best learning results.

7. An interactive English teaching system, characterized in that: include: The virtual learning environment construction module uses Unity or Unreal Engine as the virtual reality development engine, combining 3D rendering algorithms, collision detection algorithms, AI intelligent algorithms, data visualization algorithms, and motion capture algorithms to build a realistic virtual learning environment; The assessment and learning path planning module uses NLP technology to assess vocabulary and semantic relationships, analyze and assess grammar mastery; uses algorithms such as logistic regression and SVM to assess oral fluency; and uses clustering algorithms to group students' learning performance; Analyze students’ learning records and recommend personalized learning materials, courses, and activities based on assessment results; design dynamic learning paths to adjust difficulty and content based on student progress; Regularly reassess student levels and adjust learning paths; Interactive learning activity module, designing role-playing and interactive game learning activities in a virtual environment; AI tools provide real-time feedback on pronunciation correction and grammatical error prompts; Online community module: determine the community theme and goals, select the appropriate platform to create the community, set rules, invite members to join, and create a community atmosphere; design challenging and feasible learning tasks, publish task announcements in the community, provide resources and guidance, group and divide the work, supervise and guide, evaluate and provide feedback; The data statistics and analysis module collects students' learning data in virtual environments, online communities and collaborative learning tasks, and uses the system's built-in statistical tools or Python programming language to perform complex statistical analysis.

8. An interactive English teaching system according to claim 6, characterized in that: The virtual learning environment includes virtual classrooms, virtual libraries, virtual historical scenes, and virtual cafes, which students can choose or customize according to their interests and needs.

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