AIGC enabling auxiliary system for college second foreign language teaching and implementation method

Abstract

The invention discloses an AIGC enabling auxiliary system for college second foreign language teaching and an implementation method. The system is cooperatively operated by five core modules; the intelligent voice recognition and user demand analysis module analyzes the learning demand of a learner by using a voice-to-text technology, and assists the customization of teaching content; the AIGC content generation module gathers multi-source data, generates personalized teaching resources by using a frontier model and continuously updates and evolves the personalized teaching resources; the learning progress management and evaluation module collects learning data, dynamically adjusts a personalized learning plan and provides evaluation and feedback; the real-time interaction and feedback module builds a multi-dimensional interaction channel, collects feedback of all parties, analyzes and processes the feedback and presents an application result; the multi-platform support module guarantees the stable operation of the system on a plurality of operating systems and devices, and efficiently completes data synchronization sharing and performance optimization adaptation. According to the invention, an all-around learning environment is established for second foreign language learners in universities, and the learning efficiency and experience are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of language learning and artificial intelligence, and in particular, to an AIGC-enabled auxiliary system and implementation method for college second foreign language teaching. Background Art

[0002] With the in-depth development of globalization, international exchanges have become increasingly frequent, and mastering a second foreign language has become one of the key skills for college students to enhance their competitiveness. In such a demand of the times, the importance of college second foreign language teaching has become even more prominent. However, traditional foreign language teaching methods face many severe challenges.

[0003] In the traditional teaching mode, teaching mainly relies on human teachers, which greatly limits teaching resources. On the one hand, teachers' energy and knowledge reserves are limited, making it difficult to comprehensively cover various language knowledge points and rich cultural background content, and unable to meet the diverse learning needs of students. For example, when explaining complex grammar in German, teachers may be unable to provide enough practical application cases due to their own experience limitations, resulting in students' difficulty in understanding. On the other hand, the distribution of excellent foreign language teacher resources is uneven, and students in some regions are difficult to access high-quality teaching, which to a certain extent hinders the realization of educational fairness.

[0004] In addition, the personalization of the traditional teaching mode is seriously insufficient. In classroom teaching, teachers often adopt a one-size-fits-all teaching method, making it difficult to carry out targeted teaching according to each student's learning progress, learning ability and interests. For example, for students with a better foundation, the conventional teaching content may be too simple to meet their learning needs; while for students with a weak foundation, they may not be able to keep up with the teaching progress and gradually lose their confidence and interest in learning.

[0005] In the context of the global digital transformation of education, it has become a consensus in the education field to use information technology means to improve teaching efficiency and effectiveness. However, there are still many problems with foreign language teaching systems on the market. Most systems have a high teaching cost, low teaching efficiency, and imperfect system functions, and cannot quickly and effectively help students solve problems in learning. More importantly, the personalization of these systems is insufficient, and they cannot meet the diverse learning needs of different students. The traditional teacher-led teaching mode is difficult to meet the growing personalized needs of learners. In this case, developing and using special second foreign language teaching courseware auxiliary systems and teaching methods becomes a more appropriate choice. They can more accurately target the characteristics and needs of foreign language teaching and provide more effective teaching support. The AIGC-enabled automated auxiliary system for college second foreign language teaching can solve these problems through intelligent means, provide learners with a more efficient, convenient and personalized learning experience, and has broad application prospects and important research value. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention relates to an AIGC-empowered auxiliary system and implementation method for college second foreign language teaching. This system deeply integrates deep learning and natural language processing technologies, aiming to provide comprehensive, efficient, and personalized learning support for second foreign language learners.

[0007] The present invention is implemented through the following technical solutions:

[0008] An AIGC-empowered auxiliary system for college second foreign language teaching includes the following modules:

[0009] (1) Intelligent speech recognition and user demand analysis module: including an intelligent speech collection device, a speech preprocessing unit, a speech feature extraction unit, a speech recognition and conversion unit, a user demand analysis unit, an intent recognition subunit, and a demand association and integration subunit; used to accurately capture foreign language pronunciations and convert them into text, and precisely analyze user demands;

[0010] (2) AIGC content generation module: including a knowledge reserve and data integration unit, a content generation strategy unit, a generation algorithm and model application unit, and a content update and evolution unit; used to collect data from multiple sources, integrate and utilize advanced models to generate various forms of personalized teaching content, and can update and evolve;

[0011] (3) Learning progress management and evaluation module: including a learning data collection unit, a learning progress analysis unit, a personalized learning plan adjustment unit, and a learning effect evaluation and feedback unit; used to comprehensively collect learning data, analyze progress, adjust personalized learning plans, evaluate and feedback learning effects;

[0012] (4) Real-time interaction and feedback module: including a real-time interaction function unit, a feedback collection function unit, a feedback analysis and processing unit, and a feedback result presentation and application unit; supporting multiple interaction channels, collecting various aspects of feedback, analyzing, processing, and presenting application results;

[0013] (5) Multi-platform support module: including a cross-platform compatibility unit, a unified user experience unit, a data synchronization and sharing unit, and a performance optimization and adaptation unit; ensuring the stable operation of the system on multiple operating systems and devices, unifying the user experience, and realizing data synchronization, sharing, and performance optimization and adaptation.

[0014] Furthermore, the intelligent speech recognition and user demand analysis module includes an intelligent speech collection device, a speech preprocessing unit, a speech feature extraction unit, a speech recognition and conversion unit, a user demand analysis unit, an intent recognition subunit, and a demand association and integration subunit; specifically:

[0015] (1.1) Intelligent voice acquisition device, built-in with a ring microphone array, an integrated environmental noise adaptive cancellation chip, and a voiceprint recognition sensor; the device housing is made of an acoustically optimized material with a honeycomb sound-absorbing structure, and a laser projection module with an adjustable angle is embedded at the top for real-time display of the voice recognition status, supporting multiple voice input formats and being able to adapt to different voice environments;

[0016] (1.2) Voice preprocessing unit, which uses digital signal processing technology to perform noise reduction and enhancement processing on the collected voice signals;

[0017] (1.3) Voice feature extraction unit, which extracts voice features including but not limited to Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction Coefficients (LPC) from the preprocessed voice signals using acoustic models and phonetic principles, and performs normalization processing;

[0018] (1.4) Voice recognition and conversion unit, which matches and recognizes the extracted voice feature vectors with a pre-trained model based on a large-scale foreign language speech corpus and deep learning algorithms, and converts them into text form, while accurately discriminating homographs in context;

[0019] (1.5) User demand analysis unit, including: semantic understanding sub-unit, which uses foreign language grammar analysis and lexical semantic analysis technologies to perform natural language processing on the converted text content, and understands the text meaning in combination with foreign language language habits and second foreign language teaching scenarios;

[0020] (1.6) Intention recognition sub-unit, which obtains an intention classification model by training machine learning algorithms through sorting and annotating user voice expression data in second foreign language teaching scenarios, analyzes the vocabulary, grammar structure, and semantic information of the text content after semantic understanding to recognize the intention, and has the functions of processing fuzzy expressions and continuous learning and optimization;

[0021] (1.7) Demand association and integration sub-unit, which analyzes the associations of multiple user voice inputs and integrates the demands of different users; demand feedback and update sub-unit, which adjusts the analysis results according to changes in user demands and feeds back to other modules.

[0022] Furthermore, the AIGC content generation module includes a knowledge reserve and data integration unit, a content generation strategy unit, a generation algorithm and model application unit, and a content update and evolution unit, specifically:

[0023] (2.1) Knowledge reserve and data integration unit, which is used to collect second foreign language teaching-related information from data sources including authoritative foreign language textbooks, academic papers, foreign language language knowledge bases, online foreign language learning platform materials, and foreign language culture introduction documents; clean, classify, and annotate the collected data, and construct a second foreign language teaching knowledge graph;

[0024] (2.2) Content Generation Strategy Unit, which is used to determine the content generation direction based on the information of the intelligent speech recognition and user demand analysis module; generate various forms of teaching content to meet different teaching scenarios and user preferences; and achieve personalized content generation.

[0025] (2.3) Generation Algorithm and Model Application Unit, which is used to pre-train on a large-scale foreign language corpus using advanced generative deep learning models and fine-tune using specific data in the second foreign language teaching field; set up a quality assessment mechanism to evaluate the generated content in real time, and adjust or regenerate the model if it does not meet the quality standards.

[0026] (2.4) Content Update and Evolution Unit, which is used to receive feedback information from users and teachers and update the knowledge reserve to adjust and update the generated content.

[0027] Furthermore, the learning progress management and evaluation module includes a learning data collection unit, a learning progress analysis unit, a personalized learning plan adjustment unit, and a learning effect evaluation and feedback unit, specifically:

[0028] (3.1) Learning Data Collection Unit, which is used to collect the interaction data of users in the system, the results of exercises and tests, and the usage of learning resources.

[0029] (3.2) Learning Progress Analysis Unit, which is used to quantitatively evaluate the mastery level of students on various foreign language knowledge points, and analyze the learning trajectory, learning speed, and stability.

[0030] (3.3) Personalized Learning Plan Adjustment Unit, which is used to generate targeted learning suggestions and dynamically adjust the learning plan according to the results of the learning progress analysis.

[0031] (3.4) Learning Effect Evaluation and Feedback Unit, which is used to regularly generate a comprehensive evaluation report on the learning effect of students, provide information for teachers and students, and help teachers with teaching intervention and students with self-evaluation.

[0032] Furthermore, the real-time interaction and feedback module includes a real-time interaction function unit, a feedback collection function unit, a feedback analysis and processing unit, and a feedback result presentation and application unit, specifically:

[0033] (4.1) Real-time Interaction Function Unit, which is used to support voice interaction and text interaction; push messages in real time; group users and manage group information to achieve real-time interaction within the group.

[0034] (4.2) Feedback Collection Function Unit, which is used to collect experience feedback, learning effect feedback, and interaction quality feedback from users during the use of the system.

[0035] (4.3) Feedback analysis and processing unit, which is used to conduct sentiment analysis and problem classification and summarization on the feedback content, and propose targeted processing measures and improvement suggestions;

[0036] (4.4) Feedback result presentation and application unit, which is used to present the feedback analysis results to teachers and students in a visual manner and apply them to system optimization and teaching content adjustment.

[0037] Furthermore, the multi-platform support module includes a cross-platform compatibility unit, a unified user experience unit, a data synchronization and sharing unit, and a performance optimization and adaptation unit, specifically:

[0038] (5.1) Cross-platform compatibility unit, which is used to ensure the stable operation of the system on multiple mainstream operating systems and various types of devices, and optimize and adapt according to the characteristics of different operating systems and devices;

[0039] (5.2) Unified user experience unit, which is used to maintain a unified interface design style and interaction logic on different platforms and devices;

[0040] (5.3) Data synchronization and sharing unit, which is used to realize data synchronization of users on different platforms and teaching resource sharing among users of different platforms, and optimize teaching resources according to the characteristics of the platforms;

[0041] (5.4) Performance optimization and adaptation unit, which is used to perform performance optimization according to the differences in network environment and hardware performance of different platforms.

[0042] Another aspect of the present invention: discloses an implementation method of an AIGC-empowered auxiliary system for college second foreign language teaching, and the implementation method includes the following steps:

[0043] (1) Intelligent speech recognition and user demand analysis

[0044] (1.1) Speech collection: Equip with high-precision audio collection components or interfaces to receive voice input signals from various devices;

[0045] (1.2) Speech preprocessing: Denoise the collected speech signals, specifically: use advanced digital signal processing technology to identify and reduce environmental noise, device noise, and other interference factors; distinguish speech and noise frequency bands through spectrum analysis and specifically suppress the noise frequency bands; perform speech enhancement operations to optimize the quality of the speech, especially for speech inputs with special pronunciations or accents, adjust the volume, pitch, and tone parameters of the speech to make the subsequent recognition process more accurate;

[0046] (1.3) Speech feature extraction: Utilize acoustic models and phonetic principles to extract key speech features from the preprocessed speech signals; through the analysis of the speech signals in the frequency domain and time domain, convert the speech information into feature vectors recognizable by computers; perform normalization processing on the extracted speech features; the speech features include but are not limited to any one of Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction Coefficients (LPC);

[0047] (1.4) Speech recognition and conversion: Based on a large-scale foreign language speech corpus and advanced deep learning algorithms, match and recognize the extracted speech feature vectors with a pre-trained model; be able to accurately recognize kana, vocabulary, phrases, and sentences in a foreign language, including different intonations, tones, and dialectal variations; convert the recognized speech content into text form to provide a data basis for subsequent user demand analysis and teaching courseware generation; and during the conversion process, accurately discriminate homographs and select the most reasonable words in combination with context information;

[0048] (1.5) Semantic understanding: Perform natural language processing (NLP) on the text content after speech recognition and conversion; use foreign language grammar analysis and lexical semantic analysis techniques to analyze the structure and meaning of the text; combined with foreign language language habits and specific scenarios of second foreign language teaching, be able to distinguish whether it is a need for grammar explanation, vocabulary practice, or text reading guidance, providing support for accurate demand analysis;

[0049] (1.6) Intention recognition: The system will adopt advanced machine learning algorithms to regularly update and optimize the intention classification model using new data to adapt to the changing user expression habits and newly emerging intention types;

[0050] (1.7) Requirement association and integration: Analyze multiple speech inputs of the user over a period of time to identify the associations between different requirements; integrate the requirements of different users, coordinate various requirements according to the role characteristics and teaching process; the system can coordinate several requirements and optimize the courseware auxiliary functions;

[0051] (1.8) Requirement feedback and update: Dynamically adjust the analysis results according to the changes in user requirements. When the user's understanding of a certain knowledge point changes during the learning process, the system can promptly capture the new demand signal, update the demand analysis conclusion, and provide a basis for the real-time adjustment of the teaching courseware; feedback the user demand analysis results to the courseware generation module and the AIGC interaction module of the system, enabling the entire teaching courseware auxiliary system to work in coordination;

[0052] (2) AIGC content generation

[0053] (2.1) Knowledge Reserve and Data Integration: Collect information related to second foreign language teaching from data sources, clean, classify, and label the collected data, and remove noisy data; classify the data according to the grammar knowledge system, vocabulary difficulty level, and teaching scenarios to provide an orderly material library for subsequent content generation; at the same time, label the key information in the data for quick retrieval and application; build a knowledge graph for second foreign language teaching based on the sorted data, interconnect foreign language grammar, vocabulary, example sentences, and cultural backgrounds to form a structured knowledge network;

[0054] (2.2) Content Generation Strategy: Determine the direction of content generation based on the information transmitted by the intelligent speech recognition and user demand analysis module;

[0055] (2.3) Generation Algorithm and Model Application: Apply a generative deep learning model, namely a language model based on the Transformer architecture or a variational autoencoder VAE; generate natural and fluent foreign language teaching content according to the input prompt information; specifically, the model is pre-trained on a large-scale foreign language corpus to learn the language patterns, grammar rules, and semantic relationships of foreign languages; on the basis of pre-training, use specific data in the field of second foreign language teaching to adjust the model; by inputting the collected and sorted teaching-related data into the model, adjust the parameters of the model to make it more suitable for the task of second foreign language teaching content generation; at the same time, adopt optimization techniques such as gradient clipping or learning rate adjustment to improve the quality and stability of the content generated by the model; during the content generation process, set up a quality evaluation mechanism, and use language model evaluation indicators and specific evaluation rules based on foreign language teaching standards to evaluate the generated content in real time; if the generated content does not meet the quality standards, adjust the model or regenerate it;

[0056] (2.4) Content Update and Evolution: Receive feedback information from users, including evaluations of the generated content, modification suggestions, and new requirements; according to the feedback, AIGC will timely adjust and update the generated content;

[0057] (3) Learning Progress Management and Evaluation

[0058] (3.1) Learning Data Collection: Comprehensively collect various interaction data of users in the system, including the voice content input through the intelligent speech recognition module and the operation records on the courseware interface;

[0059] (3.2) Learning Progress Analysis: According to the collected data, quantitatively evaluate the students' mastery of each foreign language knowledge point; construct the students' learning trajectories, analyze their investment in foreign language learning at different time points and the sequence of knowledge acquisition; calculate the students' learning speed, that is, the number of new knowledge mastered per unit time; and judge the students' learning status by observing the fluctuations in the practice and test results;

[0060] (3.3) Personalized learning plan adjustment: Generate targeted learning suggestions for each student based on the results of learning progress analysis; Dynamically adjust the students' learning plans to ensure they match the learning progress.

[0061] (3.4) Learning effect evaluation and feedback: Regularly generate a comprehensive evaluation report on the learning effects of students, presenting the learning achievements to teachers and students; The report content includes the mastery of each knowledge point, the comparison between the learning progress and the plan, the advantages and disadvantages in the learning process, and other aspects of the analysis; Presented in a combination of visual charts and detailed written explanations for teachers to conduct targeted teaching interventions; Teachers can understand the overall learning level and individual differences in the class based on the evaluation report and adjust teaching strategies; Help students self-evaluate their learning progress and level.

[0062] (4) Real-time interaction and feedback

[0063] (4.1) Real-time interaction function: including voice interaction and text interaction; The voice interaction uses intelligent speech recognition technology to enable teachers and students to communicate with the system; The text interaction is suitable for inputting long questions or detailed feedback content; During the teaching process, the instructions sent by the teacher from the system can push messages to all participating students in real time, and for the questions or answers of students, they can also be quickly displayed on the interaction interfaces of relevant personnel; The system can also group students and teachers according to teaching needs.

[0064] (4.2) Feedback collection function: Collect the experience feedback of teachers and students during the use of the system, including evaluations of the quality of courseware content, the application of teaching methods, and the convenience of system operation. During the real-time interaction process, collect feedback on the interaction quality for the subsequent optimization of the interaction function of the system.

[0065] (4.3) Feedback analysis and processing: Conduct sentiment analysis on the collected feedback content to judge whether the user's attitude is positive, negative or neutral; Through natural language processing technology, identify the sentiment keywords and tone in the feedback text, as well as the satisfaction of teachers and students with the system and teaching content; The system classifies and summarizes the problems in the feedback content and provides a basis for targeted improvement; According to the results of the feedback analysis, propose targeted handling measures and improvement suggestions; At the same time, feedback the improvement suggestions to the relevant development and teaching teams to optimize the system and teaching content.

[0066] (4.4) Feedback Result Presentation and Application: Present the results of feedback analysis to teachers and students in a visual manner; present the overall class and individual student feedback to teachers to help them adjust teaching strategies; apply the feedback results to system optimization and teaching content adjustment; the development team improves the system's functions and performance based on the feedback; the teaching team adjusts courseware content, teaching methods, and teaching progress according to the feedback, and can modify or redesign part of the courseware content based on the feedback;

[0067] (5) Multi-platform Support

[0068] (5.1) Cross-platform Compatibility: The multi-platform support module ensures that the system can run stably on multiple mainstream operating systems, including but not limited to Windows, Mac OS, Linux, iOS, and Android; optimize and adapt according to the characteristics and technical architectures of different operating systems; support various types of devices, including but not limited to desktop computers, laptops, tablets, smartphones, and other mobile devices;

[0069] (5.2) Unified User Experience: Maintain a unified interface design style on different platforms and devices to ensure that the interaction logic of the system is the same on each platform, and users can get the same feedback when performing the same operation;

[0070] (5.3) Data Synchronization and Sharing: Achieve data synchronization for users on different platforms; when teachers or students create or modify teaching plans and learning progress information on a desktop computer, the data can be synchronized to other devices used by the users in real time; conversely, the operation data on mobile devices can also be synchronized back to the server and updated on other relevant devices; at the same time, the system optimizes teaching resources according to the characteristics of different platforms.

[0071] (5.4) Performance Optimization and Adaptation: Considering that different platforms may be in different network environments, multi-platform support needs to optimize network conditions; the system automatically adjusts the content loading strategy under different signal network conditions to ensure the operation of the system's basic functions; perform performance optimization according to the hardware performance differences of different platform devices; for high-performance desktop computers, the system can make full use of the computing power of its CPU and GPU to achieve complex graphics rendering and data processing; for mobile devices with limited hardware resources, the system adopts lightweight algorithms and optimized code structures to ensure the smooth operation of the system and the integrity of the foreign language teaching function.

[0072] Further, the specific steps of step (1.6) are as follows: By sorting and annotating a large amount of teaching data, including teachers' instructions, students' learning problems, and requests for teaching resources during the teaching process, rich training materials are provided for machine learning algorithms; through interaction and feedback with users, the model is further improved; and advanced machine learning algorithms are adopted, including but not limited to Support Vector Machine (SVM), Naive Bayes, or Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and its Long Short-Term Memory Network (LSTM) in deep learning, to learn the complex mapping relationship between different speech expressions and user intentions from the annotated data; when receiving the text content after semantic understanding, the system inputs it into the trained intention classification model; the model will analyze various features based on the vocabulary, grammar structure, and semantic information of the text, and can accurately identify the demand for the application of this grammar in sentence construction; the intention recognition function has a continuous learning mechanism. When the model makes a mistake in identifying a certain intention, the system will record and analyze the reason and adjust the model parameters.

[0073] Further, in step (2.1), collecting second foreign language teaching-related information from data sources specifically includes: authoritative foreign language textbooks, academic papers, foreign language knowledge bases, online foreign language learning platform materials, and foreign language culture introduction documents; the collected data includes knowledge of foreign language grammar, vocabulary, listening, speaking, reading, and writing, as well as content related to teaching methods and curriculum design.

[0074] Further, in step (2.2), based on the information transmitted by the intelligent speech recognition and user demand analysis module, determining the direction of content generation specifically includes: when the user's demand is to generate courseware content about a specific grammar point, and AIGC will focus on this grammar point, including grammar explanations, example sentences, and comparisons with other grammars; when the user's demand is oral practice, it can generate corresponding-level dialogue scenarios, role-playing content, and various forms of teaching content; and generate text-based courseware and audio content, including knowledge point explanations, practice questions, test questions, and pronunciation demonstrations of foreign language words and sentences and reading audio of dialogues; generate animations or multimedia presentations to more vividly display foreign language knowledge, and achieve personalized content generation according to the characteristics and learning progress of different types of users; for teachers, the generated courseware content can be customized according to the teaching syllabus and course progress, including teaching steps, guiding questions, and classroom activity suggestions; for students, generate targeted learning materials and special practice content according to the presented learning level, learning history, and weak links.

[0075] The beneficial effects of the present invention are as follows:

[0076] Through generative artificial intelligence technology, the present invention realizes all-round and personalized assistance for second foreign language learning. This system not only significantly improves the learning efficiency and learning effect of learners, but also has high flexibility and scalability, and can be customized and optimized according to the needs and preferences of different learners. This innovative learning assistance system will bring a more efficient, convenient and pleasant learning experience to second foreign language learners. Description of the Drawings

[0077] Figure 1 It is an architecture diagram of a second foreign language learning assistance system of a generative artificial intelligence (AIGC) of the present invention;

[0078] Figure 2 It is a block diagram of the intelligent speech recognition and user demand analysis module of the present invention;

[0079] Figure 3 It is a block diagram of the AIGC content generation module of the present invention;

[0080] Figure 4 It is a block diagram of the learning progress management and evaluation module of the present invention;

[0081] Figure 5 It is a block diagram of the real-time interaction and feedback module of the present invention;

[0082] Figure 6 It is a block diagram of the multi-platform support module of the present invention;

[0083] Figure 7 It is a design diagram of the intelligent voice acquisition device of the present invention.

[0084] Reference numerals: adjustable angle laser projection module - 1; external data / power cord - 2; device housing - 3; annular microphone array - 4; central processor and AI chip - 5; Detailed Description of the Invention

[0085] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When describing the following description and the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present invention. On the contrary, they are only examples of devices consistent with some aspects of the present invention as detailed in the appended claims. Each embodiment of this specification is described in a progressive manner.

[0086] It should be noted that all the directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a certain specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0087] In addition, the descriptions involving "first", "second", etc. in the present invention are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0088] The present invention relates to an AIGC-empowered automated assistance system and an implementation method for college second foreign language teaching. The system deeply integrates deep learning and natural language processing technologies, aiming to provide comprehensive, efficient, and personalized learning support for foreign language learners.

[0089] When using the system, the user first inputs their learning needs and preferences in voice through the user interface. Then, the intelligent speech recognition and user demand analysis module converts the voice into text and analyzes the user demand information. The AIGC content generation module generates personalized foreign language learning content based on this information and presents it to the user through the real-time interaction and feedback module. During the learning process, the user can interact with the system in various forms and obtain real-time feedback. At the same time, the learning progress management and evaluation module continuously tracks the user's learning progress and performance, and conducts evaluations and adjustments regularly. Finally, the system provides the user with a detailed learning report and suggestions to help the user better plan and manage their learning process. The system uses a multi-platform support module to maintain the stable operation of the system on different platforms, unifying the interface style and user experience.

[0090] The present invention provides an AIGC-empowered automated assistance system for college second foreign language teaching, and its architecture is as Figure 1 shown, presenting the overall architecture of the system, including the connection and data flow of the user interface, intelligent speech recognition and user demand analysis module, AIGC content generation module, learning progress management and evaluation module, real-time interaction and feedback module, and multi-platform support module; the system specifically includes the following modules:

[0091] (1) Intelligent speech recognition and user demand analysis module: including an intelligent speech recognition unit and a user demand analysis unit; used to accurately capture foreign language pronunciations and convert them into text, and precisely analyze user demands;

[0092] (2) AIGC Content Generation Module: It includes a knowledge reserve and data integration unit, a content generation strategy unit, a generation algorithm and model application unit, and a content update and evolution unit; it is used to collect data from multiple sources, integrate and utilize advanced models to generate various forms of personalized teaching content, and can be updated and evolved;

[0093] (3) Learning Progress Management and Evaluation Module: It includes a learning data collection unit, a learning progress analysis unit, a personalized learning plan adjustment unit, and a learning effect evaluation and feedback unit; it is used to comprehensively collect learning data, analyze the progress, adjust the personalized learning plan, evaluate and feedback the learning effect;

[0094] (4) Real-time Interaction and Feedback Module: It includes a real-time interaction function unit, a feedback collection function unit, a feedback analysis and processing unit, and a feedback result presentation and application unit; it supports multiple interaction channels, collects feedback from multiple aspects, analyzes, processes and presents the application results;

[0095] (5) Multi-platform Support Module: It includes a cross-platform compatibility unit, a unified user experience unit, a data synchronization and sharing unit, and a performance optimization and adaptation unit; it ensures the stable operation of the system on multiple operating systems and devices, unifies the user experience, and realizes data synchronization, sharing, performance optimization and adaptation.

[0096] The present invention also discloses an AIGC-enabled automation assistance system for college second foreign language teaching and its implementation method, which will be further elaborated in conjunction with the accompanying drawings, and specifically includes the following contents:

[0097] (1) The intelligent speech recognition and user demand analysis module is one of the important components of this system, as Figure 2 shown, and includes the following units:

[0098] (1.1) Intelligent Speech Recognition Unit, specifically as follows:

[0099] (1.1.1) The intelligent speech acquisition device is as Figure 7 shown, and its design description is as follows:

[0100] The intelligent voice acquisition device includes a laser projection module 1 with adjustable angle embedded at the top, an external data / power cable 2, a device housing 3, an annular microphone array 4, and a built-in central processor and AI chip 5. The laser projection module 1 with adjustable angle is embedded at the top of the device, which is used to display the voice recognition status (such as pitch accuracy, intonation waveform) in real time on the desktop, dynamically mark pronunciation error points (such as stress deviation, intonation error), and assist students in correcting pronunciation immediately. The external data / power cable 2 is used to supply power to the device and conduct data transmission and exchange with an external computer system. The device housing 3 is made of acoustically optimized materials (such as honeycomb sound-absorbing structure). The annular microphone array 4 is built-in (6-8 directional microphones), which can receive multiple voice input signals simultaneously, uses noise suppression technology to capture the voice information of teachers or students in different voice environments, and supports multiple voice input formats. The central processor and AI chip 5 (such as NPU) track the user sound source direction in real time through the microphone array, distinguish multi-user voices by combining deep learning algorithms (such as CNN+LSTM), and support directional sound pickup in group discussion scenarios. Store the user's voiceprint characteristics, automatically adapt to the pronunciation habits of different users (such as dialects, accents), and improve the accuracy of voice recognition.

[0101] The annular microphone array 4 uses noise suppression technology to capture the voice information of teachers or students in different voice environments, specifically:

[0102] (a) Based on the real-time monitoring of environmental noise, the parameters of the filter are continuously adjusted using an adaptive filtering algorithm. That is, in an environment with a certain background noise, by analyzing the background noise samples, the filter automatically generates filtering coefficients opposite to the characteristics of the noise signal, and filters the collected mixed signal containing voice and noise, thereby effectively suppressing the background noise and highlighting the voice signal. The least mean square (LMS) adaptive filtering algorithm can be used, which can continuously iterate and update the filter coefficients according to the error signal to make the output voice signal purer.

[0103] (b) Perform short-time Fourier transform (STFT) on the collected voice signal, convert it to the frequency domain, and analyze the distribution differences between voice and noise in the frequency domain. By estimating the spectrum of the background noise, then subtracting the noise spectrum from the spectrum of the noisy voice, and then performing inverse short-time Fourier transform (ISTFT) to restore the denoised voice signal. This method has a good effect in dealing with stationary or slowly changing background noise and can improve the accuracy of voice capture in different environments.

[0104] (c) In a voice environment with echo, the acoustic echo cancellation (AEC) algorithm is utilized. Based on the correlation analysis between the sound played by the speaker and the sound collected by the microphone, an echo path model is established. The echo signal is estimated through an adaptive algorithm and subtracted from the collected mixed signal to avoid the interference of echo on the capture of voice information and ensure the accurate acquisition of the voice of the teacher or student. The commonly used AEC algorithm based on an adaptive filter will adjust the filter parameters in real time according to the change of the echo path to achieve a good echo cancellation effect.

[0105] (d) For the case of being equipped with multiple microphones (i.e., the microphone array of the intelligent voice collection device), spatial filtering and beamforming technologies are adopted. By analyzing information such as the time difference and phase difference of the sound signals received by different microphones, the direction of the sound source is determined, and then a beam pointing to the sound source (the speaking direction of the teacher or student) is formed to enhance the voice signal from the target direction and suppress the interfering sounds from other directions (including background noise and irrelevant reflections, etc.).

[0106] It supports multiple voice input formats, specifically in the following ways:

[0107] (a) It has a rich built-in audio codec library, such as the codec algorithms for common audio coding formats such as MP3, WAV, and AAC. When receiving a voice input signal in the corresponding format, it can quickly and accurately decode it and convert it into raw voice data that can be processed.

[0108] (b) When receiving a voice input signal, it first performs format recognition on the signal, and judges the type of the audio format to which it belongs by detecting information such as the file header identifier and specific coding features. Then, according to the recognition result, it automatically calls the corresponding codec process and subsequent adapted processing methods to ensure that voice input in any format can be effectively processed by the system according to a unified standard.

[0109] (1.1.2) Voice preprocessing unit: The collected voice signal is subjected to noise reduction processing by filtering based on spectrum analysis; specifically:

[0110] (a) The collected voice signal is framed. The short-time Fourier transform is applied to each frame of the voice signal to transform it from the time domain to the frequency domain, obtaining the spectral representation of each frame, and obtaining the energy distribution of the voice signal at different frequencies. At the same time, it can also distinguish the characteristics of interference factors such as environmental noise and device noise in the frequency domain. The noise reduction processing can also use methods such as spectral subtraction noise reduction, Wiener filter noise reduction, or noise reduction based on wavelet transform, and then use advanced digital signal processing technologies to identify and reduce interference factors such as environmental noise and device noise to improve the clarity of the voice signal. For example, by spectrum analysis, the voice and noise frequency bands are distinguished, and the noise frequency band is specifically suppressed.

[0111] (b) The voice enhancement operation is performed using automatic gain control (AGC), including volume adjustment, pitch adjustment, and timbre adjustment. Specifically, for volume adjustment: First, the energy of the voice signal is detected. The energy of each frame of the voice signal is obtained by calculating the sum of the squares of the signal amplitudes or the root mean square value (RMS) to measure the strength of the voice signal. According to the set target volume range, the gain of the voice signal is dynamically adjusted through a gain adjustment algorithm to avoid problems such as difficult recognition due to too low volume or distortion due to too high volume. A smoothing factor for gain adjustment can also be set to make the change process of the gain smoother and more natural, ensuring the voice quality. Specifically, for pitch adjustment: Fundamental frequency extraction and modification are adopted. The fundamental frequency information is extracted from the voice signal through the fundamental frequency extraction algorithm. The fundamental frequency reflects the pitch of the voice. For voice inputs with weak pronunciation or accents that result in unclear or inaccurate pitch, the extracted fundamental frequency is appropriately adjusted according to the best adaptation requirements of the voice recognition system for pitch features and the pitch distribution law of normal voices. Specifically, for timbre adjustment: The linear prediction coding technology is used to model the voice signal. The timbre characteristics of the voice are characterized by analyzing the spectral envelope of the voice signal. For voice inputs with unclear timbre or timbre deviation from the standard due to factors such as accents, the spectral envelope of the voice is changed by adjusting the LPC coefficients, or the corresponding LPC coefficients can be appropriately adjusted according to the timbre model of the standard voice to make the timbre closer to the normal and clear state, thereby realizing the optimization of the timbre. In addition, a deep learning-based voice conversion model can be constructed using a deep learning-based voice conversion method, adopting architectures such as generative adversarial networks (GAN) and variational autoencoders (VAE), and training on voice data. The model learns the mapping relationship between normal voices and abnormal voices in terms of timbre, etc., making the subsequent recognition process more accurate.

[0112] (1.1.3) Voice feature extraction: Using an acoustic model and phonetic principles, key voice features are extracted from the preprocessed voice signal using Mel Frequency Cepstral Coefficients (MFCC). Specifically:

[0113] (a) First, a pre-emphasis operation is performed on the preprocessed voice signal, which is achieved through a first-order high-pass filter. Its transfer function is usually H(z) = 1 - αz -1(The value of α is usually between 0.95 and 0.97). The pre-emphasized signal y(n) is obtained by passing the speech signal x(n) through this filter, that is, y(n) = x(n) - αx(n - 1). This can enhance the energy of the high-frequency part. Then, the pre-emphasized speech signal is framed to discretize the speech signal in time for analyzing its short-time characteristics. After that, each frame of the signal is windowed (window functions include Hamming window, Hanning window, etc.). By multiplying each frame of the speech signal with the window function, the spectral leakage caused by frame truncation can be reduced, making the spectral analysis result more accurate. Then, the fast Fourier transform is performed on each windowed frame of the speech signal to convert the time-domain signal into a frequency-domain signal, obtaining its spectral representation X(k). Then, the magnitude spectrum |X(k)| is calculated to reflect the energy distribution of the speech signal at different frequencies. According to the Mel frequency scale of human auditory perception, a set of Mel filter banks is constructed, usually containing 20 - 40 triangular filters. These filters are evenly distributed on the Mel frequency scale and cover the main frequency range of the speech signal. The magnitude spectrum is filtered through this set of Mel filter banks to obtain the energy distribution on the Mel frequency scale, denoted as M(k). The logarithm operation is performed on the energy M(k) on the Mel frequency scale, that is, L(k) = log(M(k)), to compress the dynamic range of the data. Finally, the discrete cosine transform is performed on the logarithmic energy value, and the first few coefficients (usually 12 - 13) after DCT are taken as the Mel frequency cepstral coefficients (MFCC). These coefficients form the key feature vectors reflecting the spectral characteristics of the speech signal and can effectively characterize the characteristics of the speech in the frequency domain.

[0114] (b) Feature extraction is carried out through the comprehensive application of frequency-domain and time-domain analysis. Among them, for time-domain analysis, the zero-crossing rate needs to be calculated for supplementary feature extraction; the number of times the speech signal crosses the zero value within a unit time (usually in frames) is counted, that is, the zero-crossing rate; the zero-crossing rate can reflect the frequency characteristics of the speech signal and plays an important role in distinguishing the voiced and unvoiced segments (such as silence, voiceless sounds, and voiced sounds). Then, the zero-crossing rate is counted by judging the sign change of adjacent sampling points. If two adjacent sampling points x(n) and x(n + 1) satisfy x(n)x(n + 1) < 0, it is recorded as one zero-crossing. The zero-crossing rate of each frame of the speech signal is calculated and added to the feature vector. Then, the short-time energy of each frame of the speech signal is calculated, which reflects the intensity change of the speech signal and is used to judge the start and end points of the speech and distinguish speech segments of different intensities; usually, the short-time energy E(n) is obtained by calculating the sum of the squares of the amplitudes of each frame of the signal, that is (where N is the frame length and x(n) is the speech signal), and it is incorporated into the feature vector to further enrich the feature representation of the speech in the time domain.

[0115] (c) Combine the features such as MFCC extracted from the frequency domain and the features such as zero-crossing rate and short-time energy in the time domain in a certain order to form a comprehensive speech feature vector; finally, perform mean-variance normalization (Z-score normalization). First, calculate the mean μ and variance σ of each feature dimension (such as each coefficient dimension of MFCC, etc.) in all speech samples (the set of feature vectors corresponding to different speech inputs). For the feature vector of a certain speech sample, its normalized feature vector is calculated by the following formula: This can transform the data of each feature dimension into the range of a standard normal distribution with a mean of 0 and a variance of 1, so that the features of different speech inputs (regardless of individual differences such as the speaker's volume and speaking speed) are distributed within the same numerical interval, avoiding deviations in the results during subsequent speech recognition and other calculation processes due to some feature values being too large or too small, and ensuring the consistency and comparability of the features. Through these specific methods of speech feature extraction, key speech features can be efficiently and accurately extracted from the preprocessed speech signal, and through normalization, it is made suitable for subsequent computer processing tasks such as speech recognition and speech analysis, improving the overall speech processing effect and accuracy.

[0116] (1.1.4) Speech recognition and conversion: Based on a large-scale foreign language speech corpus and advanced deep learning algorithms (such as deep neural networks, DNNs), match and recognize the extracted speech feature vectors with a pre-trained model. Specifically:

[0117] (a) Build a large-scale foreign language speech corpus, collect foreign language speech data from multiple channels, including but not limited to foreign language radio programs, foreign language movies, TV dramas, foreign language audiobooks, foreign language spoken dialogue recordings, etc., to cover speech samples in different scenarios and different speaker groups (covering different ages, genders, regions, etc.); collect professional foreign language pronunciation teaching audio, and for vocabulary, phrases, and sentences, mark their complete written forms and grammatical structures, etc. For speech with dialect variants, mark in detail the dialect regions to which they belong and the corresponding standard foreign language expressions, facilitating the model to learn the mapping relationship between different dialects and the common foreign language; and through acoustic feature analysis combined with manual listening, mark the rising and falling tones, stress positions, and emotional tone categories expressed by the speech.

[0118] (b) Deep neural network architectures such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs) and their variants (such as bidirectional LSTMs, BiLSTMs), gated recurrent units (GRUs), etc. can be combined and applied to the speech recognition model according to requirements for pre-training model selection and construction; the CNN is good at capturing local features of speech signals in the frequency domain and time domain; the LSTM or BiLSTM is suitable for modeling speech sequences (sequences composed of kana, vocabulary, etc.) because it can handle long-term dependencies in sequence data; construct a hybrid architecture such as a deep bidirectional LSTM-hidden Markov model (DBLSTM-HMM), and use the HMM to model the joint probability between the acoustic features and the language model of speech, combined with the powerful sequence processing ability of the DBLSTM to improve the accuracy and robustness of speech recognition.

[0119] (c) The matching and recognition of the speech feature vector and the pre-trained model can input the normalized speech feature vector obtained through the previous speech feature extraction link (for example, a vector containing comprehensive features such as MFCC and LPC) into the pre-trained deep neural network model; according to the language rules of a foreign language (such as grammar structure, vocabulary collocation, etc.) and the language patterns learned during the model training process, judge which combination best conforms to the actual content expressed by the speech, so as to complete the accurate recognition of foreign language speech from kana to a complete sentence.

[0120] (d) Speech-to-text conversion. Once the words, phrases, sentences, etc. corresponding to the speech are determined through the speech recognition model, these recognized language units are combined into complete text content in accordance with the writing norms and grammar requirements of the foreign language. For the recognized words and phrases, they are arranged in a reasonable word order to construct a sentence structure that conforms to the grammar of the foreign language, forming the final text form, which serves as the basic data for subsequent processing. It can also accurately recognize words, phrases and sentences in a foreign language, including different intonations, tones and dialect variants. Convert the recognized speech content into text form to provide a data basis for subsequent user demand analysis and teaching courseware generation. At the same time, during the conversion process, accurately distinguish homographs, etc., and select the most reasonable words in combination with context information. The combination of context information is judged. During the speech recognition conversion process, when encountering homographs, analyze the content before and after the speech segment where it is located, that is, use context information to determine the most appropriate word selection; model and analyze the entire text context after speech conversion by constructing a deep learning-based natural language processing model (such as a language model based on a recurrent neural network or a Transformer architecture), calculate the probabilities of different homographs appearing in this context, and select the one with the highest probability as the final judgment result to ensure that the converted text content accurately conveys the true meaning expressed by the speech.

[0121] (1.2) The functions of the user requirement analysis unit are as follows:

[0122] (1.2.1) Semantic understanding: Perform natural language processing (NLP) on the text content after speech recognition conversion. Adopt methods of text structure parsing, lexical semantic analysis, and semantic relationship recognition based on foreign language grammar analysis to analyze the structure and meaning of the text. For example, identify components such as the subject, predicate, and object in a sentence, understand the semantic relationships between words, and determine whether it is a teaching instruction, a learning problem, or other types of expressions. Combine foreign language language habits and the specific scenarios of second foreign language teaching to gain a deeper understanding of the text and provide support for accurate requirement analysis.

[0123] (a) The text structure parsing based on foreign language grammar analysis is specifically as follows: First, construct a foreign language grammar rule knowledge base; organize and classify these grammar rules according to a certain classification system to build a comprehensive and well-organized foreign language grammar rule knowledge base; for the convenience of computer processing, represent the foreign language grammar rules in a formalized way, enabling computer programs to analyze the grammar structure in the text according to the rules.

[0124] (b) The lexical semantic analysis and semantic relationship recognition are specifically as follows: First, create a lexical semantic knowledge base. Integrate multiple authoritative foreign language dictionaries (including paper dictionaries and electronic dictionary resources), research results related to foreign language lexical semantics, and actual usage examples of words in foreign language corpora, and collect detailed semantic information such as the basic definitions, parts of speech, semantic roles, semantic classifications, synonyms, antonyms, and polysemy of foreign language words. Build a rich and comprehensive lexical semantic knowledge base to provide basic data support for subsequent semantic analysis. Then, in the lexical semantic knowledge base, annotate and model the semantic relationships between words. Construct a lexical semantic relationship model through technical means such as semantic networks and knowledge graphs to clearly present the semantic associations between different words, facilitating the tracking of the semantic context between words when analyzing the text. For each word in the input text, query in the constructed lexical semantic knowledge base to obtain its corresponding semantic information. Then analyze the position, collocation, and context of the word in the sentence to determine its specific semantic role and semantic orientation, and gain a deeper understanding of the text meaning.

[0125] (c) Combine the foreign language language habits with the specific scenarios of second foreign language teaching to gain a deeper understanding of the text. Specifically: First, sort out the types of teaching needs. Conduct research on the common needs and teaching processes of second foreign language teaching, and summarize the typical text demand types corresponding to different teaching links; classify and describe the characteristics of these demand types in detail to build a knowledge base of the characteristics of second foreign language teaching needs for subsequent text matching and classification judgment. Then, summarize the foreign language language habits, deeply study the language habits of foreign languages in teaching scenarios and daily communication, such as the commonly used euphemistic tone, the usage of honorifics, and the specific ways of introducing and ending topics in foreign language expressions. Summarize these language habit characteristics to form corresponding rules and feature sets to assist in accurately grasping the intention and emotional tendency of the text during the text understanding process. Next, conduct text classification and understanding based on scenario characteristics. By checking whether keywords corresponding to the demand types appear in the text, whether they conform to specific sentence structures, and whether the overall semantics fits, etc., judge whether the text belongs to specific types such as grammar explanation needs, vocabulary practice needs, or text reading guidance needs, etc., so as to more accurately grasp the actual needs behind the text and provide strong support for subsequent demand analysis and targeted services. On the basis of classification, further combine the foreign language language habits and the specific teaching scenario context where the text is located to deeply understand the subtle meaning and potential intention of the text in order to provide services or guidance that better meet the actual needs of users.

[0126] (1.2.2) Intention Recognition: By means of intention recognition, the user needs in second foreign language teaching are met. Specifically: Abundant training materials are provided for machine learning algorithms through large-scale teaching data; Advanced machine learning algorithms such as support vector machine (SVM), Naive Bayes, or convolutional neural network (CNN), recurrent neural network (RNN) and its variants (such as long short-term memory network, LSTM) in deep learning are used to learn the complex mapping relationship between different speech expressions and user intentions from the labeled data; The machine learning algorithm can adopt support vector machine (SVM). For the labeled teaching data, feature extraction is performed on the text data (such as representing it in the form of a bag-of-words model, counting the frequency of each word in the text, and converting the text into a vector form; or using the TF-IDF method to construct a vector considering the importance weight of the words), and then the SVM model is trained based on these feature vectors. SVM divides the boundary between different intention categories by finding the optimal hyperplane and can effectively handle linearly and non-linearly separable data situations in a high-dimensional feature space, being suitable for processing intention recognition tasks with relatively not particularly high feature dimensions and moderate data volumes, and is used for text intention classification tasks with obvious category discrimination features; When the text content after semantic understanding is received, the system inputs it into the trained intention classification model. The model analyzes it based on various features such as the vocabulary, grammatical structure, and semantic information of the text. For example, if the text contains words such as "practice", "words", "pronunciation", and the grammatical structure indicates an interrogative tone, the model will tend to classify it into the intention category of "requirements related to word pronunciation practice". For complex sentences, the model comprehensively considers the semantic logic of the entire sentence. For example, "I want to know how to correctly use this newly learned grammar point to express my thoughts in a dialogue situation and if there are related exercises", the model can recognize that this involves the intention of applying grammar to actual situations and related exercises, rather than just a simple grammar query; To cope with the ambiguity of user expressions, the intention recognition model has a strong generalization ability and can recognize sentences with similar intentions even if the expressions are imprecise. For example, "I'm not very clear about how to use this grammar in a sentence" and "How do I use this grammar when making sentences" can both be accurately recognized as the need for the application of this grammar in sentence construction; The intention recognition function has a continuous learning mechanism. As the system is applied in the actual teaching environment, new user speech data accumulates continuously. The system will regularly use this new data to update and optimize the intention classification model to adapt to the changing user expression habits and newly emerging intention types; The model is further improved through interaction feedback with users. If the model has a deviation in the recognition of a certain intention, the system will record and analyze the reasons, adjust the model parameters, and improve the accuracy of intention recognition, so as to better meet the complex and changing user needs in second foreign language teaching.

[0127] (1.2.3) Requirement Association and Integration: Analyze the multiple voice inputs of users over a period of time to identify the associations between different requirements. The specific implementation is as follows:

[0128] (a) It is necessary to use speech recognition technology of Deep Neural Network - Hidden Markov Model (DNN - HMM) to convert the user's voice input into text format for subsequent analysis and processing. That is, first extract speech features through DNN, and then use HMM to model the speech sequence, so as to accurately convert speech into text content; there are also end - to - end deep learning models, such as the architecture combining Convolutional Neural Network (CNN) and Long Short - Term Memory Network (LSTM), or the Transformer architecture with attention mechanism (such as Google's DeepSpeech, etc.), which directly map speech to text, improving the accuracy and robustness of speech recognition. After obtaining the text, semantic understanding needs to be carried out on it through Natural Language Processing (NLP) technology to identify the associations between different requirements. Use a word segmentation algorithm to divide the text into individual words to facilitate the subsequent analysis of the relationships between words. At the same time, use a part - of - speech tagging algorithm for part - of - speech tagging to determine the part - of - speech category of each word; use a syntactic analysis algorithm to construct the syntactic structure tree of the sentence. The syntactic analysis algorithm is a syntactic analysis algorithm based on Probabilistic Context - Free Grammar (PCFG), a deterministic syntactic analysis algorithm based on shift - reduce, etc., to clarify the syntactic relationships of each component in the sentence; use a word vector model (such as pre - trained language models like Word2Vec, GobiVe, Bert, etc.) to map the words in the text into vector representations, and measure the semantic similarity between words and sentences through vector operations., further explore deep - level semantic associations, and accurately identify the internal connections between different requirements. Based on the results of the above semantic understanding, use association rule mining algorithms (such as the classic Apriori algorithm, FP - Growth algorithm, etc.) to discover the frequent patterns and association rules between different requirements. According to these association rules, integrate the related requirement content to make the courseware content more coherent. For example, present the grammar point explanation and the corresponding example sentences in sequence to meet the coherence requirements of users' learning.

[0129] (b) Integrate the requirements of different users (teachers and students). The specific implementation is as follows:

[0130] When a user first registers and logs in to the system, the user role is initially determined by having the user select or fill in the corresponding role information (teacher or student), and different permissions and function access scopes are assigned to different roles in the system to facilitate subsequent differentiation of requirements based on roles. For example, the teacher role may have the permission to upload and edit teaching resources, while the student role mainly has the permissions to study, practice, and view learning feedback, etc. In addition to the initial registration information, the role judgment can be further confirmed or supplemented by analyzing the user's behavior patterns within the system. Using clustering analysis algorithms in machine learning (such as K-Means clustering, DBSCAN clustering, etc.), clustering is performed based on behavioral data such as the usage frequency of different function modules in the system, the operation sequence, and the resource access type of the user, and users with similar behavior patterns are grouped into one category to assist in judging whether the user is more inclined to the teacher role or the student role. For various demand expressions of users with different roles in the system (which can be in the form of text after voice input conversion, written opinions submitted through feedback channels, etc.), feature extraction techniques are used for processing. Key features are extracted from the demand text, such as topic keywords (extracting important and discriminative words in the text through the TF-IDF algorithm), demand type labels (using text classification algorithms, based on supervised learning methods such as the Naive Bayes classifier, support vector machine, and convolutional neural network in deep learning for text classification to divide the demands into different categories such as teaching resource organization, learning content understanding, practice and consolidation, etc.), etc., and the demands are represented by structured feature vectors to facilitate subsequent analysis and integration; according to the user role and the extracted demand features, the demands of different users are classified and summarized into the corresponding role demand sets.

[0131] (c) The process of demand coordination and integration based on the teaching process is as follows: Construct a standard teaching process model for second foreign language teaching, which can be designed based on pedagogical theories and actual teaching experience, covering all aspects of teaching, and clarifying the common tasks and demand characteristics of teachers and students at each stage. According to the teaching process model, analyze the corresponding relationships of various demands in the demand sets of teachers and students at different teaching stages, and use optimization algorithms such as the greedy algorithm and dynamic programming algorithm to coordinate and integrate the demands of different roles on the basis of meeting the rationality of the teaching process and the needs of all parties. In this way, the needs of all parties are coordinated, the auxiliary functions of the courseware are optimized, and the overall teaching effect is improved.

[0132] (1.2.4) Demand feedback and update: Dynamically adjust the analysis results according to the changes in user needs. Specifically as follows:

[0133] (a) Through the technical means and algorithms for evaluating the degree of understanding of knowledge points, when users perform exercises, tests, exams, etc. related to knowledge points, the system will collect answer data and use text similarity algorithms (such as cosine similarity, edit distance, etc. combined with word vector models to compare the similarity between user answers and standard answers) to evaluate the quality of answers. Use machine learning algorithms to analyze answer data, such as using logistic regression models to predict the probability of users mastering knowledge points. Take the correct answer rate, question difficulty coefficient, etc. as input features, and use whether the knowledge point is truly mastered (which can be set by the comprehensive results after multiple verifications, such as continuous correctness reaching a certain threshold to be considered mastered) as the output label for training. The trained model is used to judge the user's mastery of each knowledge point in real time to reflect the degree of understanding. In addition to answer data, the system will also record the user's behavior when learning learning materials related to knowledge points (such as video viewing progress, whether to repeatedly watch certain key explanation parts, reading time and dwell time of text materials, etc.). By analyzing these behavioral data, we can explore the user's concentration in the learning process, interest in knowledge points, and possible difficulties in understanding. For example, if a user frequently pauses and replays a video explaining a certain knowledge point, it may mean that he or she has difficulty understanding the knowledge point; conversely, quickly skipping certain content may mean that the knowledge point has been mastered. Cluster analysis algorithms (such as K-Means clustering) are used to group users with similar learning behavior characteristics into one category. By comparing the commonalities of knowledge point mastery among similar users, the judgment of the current user's level of understanding of the knowledge point can be further calibrated. For example, if it is found that other users with similar behavior patterns as the current user generally have a good grasp of a certain knowledge point, while the current user's performance in answering questions is poor, their level of understanding can be re-evaluated in a targeted manner.

[0134] (b) The demand signal capture and demand analysis conclusion update algorithm is as follows: Set a monitoring period (such as every once in a while, or after the user completes a certain amount of learning tasks) to collect and analyze the data related to the understanding degree of the above knowledge points in real time. When it is found that the understanding degree index of a certain knowledge point of the user (such as the mastery probability, the compliance degree of behavior characteristics, etc.) changes significantly (exceeding the preset change threshold, and the threshold can be set through experiments and experience), trigger the demand signal capture mechanism. The demand signal can be defined and quantified based on factors such as the changed knowledge point, the direction of change (whether the understanding is deepened or there are difficulties in understanding), and the change amplitude. For example, a vector containing elements such as the knowledge point identifier, the understanding degree change value, and the change timestamp is used to represent the demand signal, which is convenient for subsequent processing. Build a demand analysis model based on the Bayesian network, taking various characteristics of the user (such as the initial learning foundation, learning style, etc.), learning behavior data, and the understanding degree of knowledge points as nodes in the network. The nodes are connected to each other through conditional probability relationships to form a probabilistic description structure of the user's needs. Initially, determine the probability distribution of each node based on the existing user data and prior knowledge, and construct a prior demand analysis conclusion. When a new demand signal (as new observed evidence) is captured, use Bayes' formula to update the probabilities of each node in the Bayesian network, thereby updating the entire demand analysis conclusion to enable it to reflect the user's latest demand situation in a timely manner. For example, according to the signal that the user's understanding degree of a certain grammar knowledge point has decreased, update the analysis conclusions on the user's subsequent learning resource requirements (possibly requiring more basic explanations, examples, etc.) and teaching method requirements (whether an interactive practice session needs to be added, etc.).

[0135] (c) Feedback the user demand analysis results to other modules to achieve collaborative work as follows:

[0136] First, at the system architecture level, design dedicated communication interfaces for the requirement feedback and update module and other related modules (such as the courseware generation module, AIGC interaction module) to ensure efficient and accurate data transmission between modules. The communication interfaces can be built based on common network communication protocols (such as HTTP, WebSocket, etc.), and select appropriate communication methods according to the deployment methods of different modules (whether it is local deployment or distributed deployment, etc.). For example, for modules running locally, inter-process communication mechanisms (such as pipes, shared memory, etc., with corresponding implementation methods in different operating systems) can be used; for distributed modules, communicate through network service interfaces to ensure reliable data transmission across network environments. Uniformly specify the data format of the user requirement analysis results so that each module can correctly parse and use them. General data formats such as JSON (JavaScript Object Notation) or XML (eXtensible Markup Language) can be used for encapsulation. For example, when representing the requirement analysis results in JSON format, it includes fields such as user identifiers, requirement types (such as learning resource requirements, teaching method requirements, etc.), specific requirement contents (such as needing more examples about a certain foreign language grammar point, hoping to increase oral interaction exercises, etc.), and the priority of the requirements (which can be set according to factors such as the urgency of the requirements and the impact on teaching effects), so that the receiving module can clearly obtain the requirement information and make corresponding processing. Then, implement feedback through an asynchronous communication mechanism based on a message queue. Introduce message queue middleware (such as RabbitMQ, Kafka, etc.) to achieve asynchronous transmission of the requirement analysis results, improving the overall response performance and scalability of the system. Select an appropriate message queue system according to factors such as the scale of the system, concurrent processing requirements, and data traffic, and perform corresponding installation, configuration, and cluster setup (if high availability and high throughput are required).

[0137] For example, if the system needs to handle a large number of real-time user requirement feedbacks and has high requirements for reliability, Kafka may be a suitable choice. It has characteristics such as high throughput, distributed, and persistent messaging, and can meet the data transfer requirements in complex business scenarios. In the requirement feedback and update module, the updated user requirement analysis results are encapsulated into messages and published to the corresponding message queue topic according to a predetermined data format. For example, different topics can be set for different module requirement types, such as the "courseware generation requirement" topic, the "AIGC interaction requirement" topic, etc., to facilitate each module to accurately obtain relevant information. Modules such as the courseware generation module and the AIGC interaction module act as subscribers to the message queue and subscribe to the topic messages they are interested in in advance. When a new requirement analysis result message is published to the corresponding topic, the subscription module can receive the message in a timely manner, parse the requirement information therein, and adjust its own working logic based on this information to achieve collaborative work among modules. For example, after the courseware generation module receives a requirement message to add more examples of a certain foreign language grammar point, it will correspondingly enrich the example part of this grammar point in the generated courseware content to make it more in line with the current learning needs of users.

[0138] (d) The feedback result verification and adjustment mechanism is implemented as follows:

[0139] To ensure that the feedback processing of the requirement analysis results by each module is effective, corresponding verification indicators need to be set. For the courseware generation module, indicators can be set from aspects such as the matching degree between the courseware content and user requirements (such as whether the knowledge points mentioned in the requirements are correspondingly reflected in the courseware, the proportion of key content of the requirements in the courseware space, etc.), the practicality of the courseware (indirectly reflected by subsequent user usage duration of the courseware, improvement of learning effects, etc.); for the AIGC interaction module, verification indicators can be determined from angles such as whether the interaction answers meet the user's question intention, the fluency and accuracy of the interaction. Regularly (such as weekly, monthly) collect data on these verification indicators, and observe the change trend of the indicators through data analysis tools (such as using the data analysis library Pandas combined with visualization tools such as Matplotlib for data statistics and visualization display) to intuitively understand the response effect of each module to requirement feedback. According to the analysis results of the verification indicators, if it is found that the processing effect of a certain module on requirement feedback is not good (such as the matching degree between the courseware content and requirements remains low, the user satisfaction with the AIGC interaction answers is not high), then the processing logic of this module needs to be adjusted. Ensure that the entire teaching courseware assistance system can continuously and effectively meet various needs of users in the process of second foreign language teaching.

[0140] (2) The functions of the AIGC content generation module are as Figure 3 shown:

[0141] (2.1) Knowledge reserve and data integration unit

[0142] (2.1.1) Collect information related to second foreign language teaching from rich data sources, that is, write a crawler program based on the Scrapy framework or BeautifulSoup library of Python combined with the requests library. By defining the URL rules of the target website, web page tag features, etc., accurately extract the text information in the web page. For example, use XPath or CSS selectors to locate the element nodes containing foreign language teaching content, and capture the relevant text content such as foreign language grammar explanations, vocabulary examples, cultural introductions, etc., and store them in a local database or corresponding data structure for subsequent processing. These data cover knowledge in all aspects of foreign language grammar, vocabulary, listening, speaking, reading and writing, as well as content related to teaching methods and curriculum design.

[0143] (2.1.2) Clean, classify and label the collected data. Remove noisy data, such as incorrect grammar explanations, inaccurate vocabulary translations, etc. The specific implementation is as follows:

[0144] (a) The incorrect grammar explanations are implemented using a grammar error checking algorithm, which can be based on regular expressions of foreign language grammar rules to check whether there are expressions that do not conform to the rules in the grammar explanation part of the text. For example, for the verb conjugation rules of a foreign language, by writing regular expressions that match the correct formats of different tenses and voices, scan the verb conjugation content in the grammar explanation part of the collected data, mark the incorrect grammar explanation statements that do not conform to the regular expression rules, and then delete or correct the prompts; the vocabulary translation accuracy verification algorithm can use the existing authoritative foreign language-Chinese dictionary data (which can be constructed in the form of a database or a key-value pair storage structure) to compare the collected vocabulary translation content. Determine and filter out inaccurate vocabulary translation information by exact matching or similarity calculation (such as the edit distance algorithm, which calculates the edit distance of the character differences between the collected translation and the authoritative translation. If the edit distance is too large, the translation may be inaccurate).

[0145] (b) The classification of data is based on a text feature-based classification algorithm (such as K-Means clustering). For the need to classify according to dimensions such as the grammar knowledge system, vocabulary difficulty level, and teaching scenarios, text feature vectors can be extracted first. For example, for grammar knowledge texts, the characteristics of grammar structures, common auxiliary word collocations, etc. are used as features, and the text is transformed into feature vectors using the Bag of Words model or the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm. Then, the K-Means clustering algorithm is used to divide the grammar knowledge-related texts into different categories according to the pre-set number of clustering centers (corresponding to different classification categories, such as primary grammar, intermediate grammar, etc. as clustering centers respectively). The same idea can also be applied to aspects such as vocabulary difficulty level and teaching scenario classification, except that the feature selection will vary according to the specific classification objectives.

[0146] The labeled data uses a keyword extraction algorithm (such as TextRank). The TextRank algorithm imitates the idea of the PageRank algorithm. By constructing a word graph structure of the text, calculating the correlation weights between words, and finally extracting the important words and phrase combinations with higher weights, these extracted contents are used as key information for labeling to facilitate subsequent retrieval and application.

[0147] (2.1.3) Construct a second foreign language teaching knowledge graph based on the organized data. The specific implementation is as follows:

[0148] (a) The knowledge graph construction uses a deep learning-based named entity recognition (NER) algorithm (such as BiLSTM-CRF). First, the BiLSTM-CRF (Bidirectional Long Short-Term Memory Network combined with Conditional Random Field) model is used to identify entities such as foreign language grammar, vocabulary, example sentences, and cultural backgrounds in the text. This model performs bidirectional sequence learning on the word vector representation of foreign language sentences and uses the CRF layer to consider the constraint relationship of the tags in the context to accurately identify different types of entities.

[0149] (b) For the extraction of relationships between entities, a relationship extraction model based on a convolutional neural network (CNN) or Transformer architecture is used. The identified entity pairs and their contexts are used as inputs to automatically learn and judge the relationship types between entities (such as the collocation relationship between grammar and vocabulary, the association relationship between cultural background and example sentences, etc.), and then build the relationship network of the entire knowledge graph, connecting the knowledge points with each other to form a structured knowledge network.

[0150] (2.2) Content generation strategy unit

[0151] (2.2.1) Determine the direction of content generation based on the information transmitted by the intelligent speech recognition and user demand analysis module. The specific implementation is as follows:

[0152] (a) Match the keywords and sentence patterns in the user demand text through predefined rules to determine the content generation direction. For example, if the text input by the user contains keywords such as "specific grammar point" and "grammar explanation", it is determined that the demand is to generate courseware content about specific grammar points; if words such as "oral practice" and "dialogue scenario" appear, it is considered a demand related to oral practice. Tools such as regular expressions can be used to write these rules to achieve fast and accurate intention judgment.

[0153] (b) Adopt machine learning classification algorithms (such as Naive Bayes, Support Vector Machine, etc.). First, collect a large number of user demand sample data with pre-labeled content generation directions (such as categories like grammar point courseware, oral practice, etc.), extract features from these text data. For example, use the bag-of-words model or TF-IDF method to transform the text into feature vectors. Then use the Naive Bayes algorithm to calculate the probability distribution under different categories according to Bayes' theorem, or use the Support Vector Machine algorithm to distinguish different demand categories by finding the optimal classification hyperplane, classify the newly input user demand text, and then determine the corresponding content generation direction.

[0154] (c) Adopt sequence classification algorithms in deep learning (such as BERT-based classification models), use the pre-trained language model BERT (Bidirectional Encoder Representations from Transformers). First, input the user demand text into the BERT model for encoding to obtain a vector representation that integrates context semantic information, and then add a simple classification layer (such as a fully connected layer with a Softmax activation function) for classification to determine which content generation direction the demand belongs to, so that AIGC can clearly determine what to focus on for subsequent content generation work, such as around specific grammar points or oral practice scenarios, etc.

[0155] (2.2.2) Be able to generate various forms of teaching content to meet different teaching scenarios and user preferences. The specific implementation is as follows:

[0156] (a) A language model based on the Transformer architecture (such as GPT series, ERNIE, etc.) can be adopted. When it is necessary to generate courseware in text form, such as detailed knowledge point explanations, exercise questions, test questions, etc., this kind of powerful pre-trained language model can be used. By inputting appropriate prompt information into the model, the model automatically generates text content that meets the requirements based on the large amount of language knowledge and semantic relationships it has learned, and uses its self-attention mechanism to capture long-distance dependencies in the text, making the generated content logically coherent and well-organized.

[0157] (b) A template filling and text assembly algorithm can be adopted. Prepare text templates for various knowledge point explanations, exercise questions, etc. According to the specific generation requirements, extract key information such as relevant grammar and vocabulary, fill them into the corresponding template positions, and then assemble the text in a certain logical order. For example, for the grammar point explanation template, fill in information such as the structure and usage examples of the grammar into the corresponding positions, and then integrate them to form the complete text content of the grammar point explanation.

[0158] (c) For audio content generation, speech synthesis technology (such as TTS, Text-To-Speech) is used: To generate pronunciation demonstrations of foreign language words and sentences, and reading audio of dialogues, a professional speech synthesis system can be adopted. For example, TTS algorithms based on deep learning, common architectures include WaveNet, Tacotron, and their derivative models. These models first preprocess the input text (such as foreign language words, sentences) into representations of speech units such as phonemes, and then learn the mapping relationship from speech units to acoustic features (such as Mel spectrograms, etc.) through neural networks, and finally synthesize natural and fluent audio waveforms to achieve the generation of audio content such as pronunciation demonstrations.

[0159] (d) When generating the reading audio of dialogues, perform cutting and splicing operations on different speech segments (such as the audio of statements of different characters). Use an audio processing library (such as the pysoundfile library in Python combined with numpy array operations), and cut and splice the audio of statements corresponding to each character at appropriate time points according to the order of the dialogue, ensuring the coherence and accuracy of the audio content.

[0160] (e) For the generation of animations or multimedia presentations, the following is achieved:

[0161] For the need to display the conjugation process of foreign language verbs through animation, several key frame states in the verb conjugation process can be determined first (such as the base form, the forms at different conjugation stages, etc.). Then, using animation software or programming libraries (such as the matplotlib animation module in Python or the scripting programming interface of professional animation production software like Adobe Animate), through interpolation algorithms between key frames (such as linear interpolation, Bezier curve interpolation, etc., which are used to generate intermediate frames for the transition between key frames), a complete animation sequence can be constructed to vividly display the verb conjugation process.

[0162] For generating multimedia presentation slides, the programming interfaces provided by existing presentation software (such as Microsoft PowerPoint or LibreOffice Impress, etc.) can be utilized, or some template-based automated generation tools can be adopted. First, prepare presentation templates for different teaching topics (including page layouts, basic styles, etc.). Then, according to the foreign language knowledge content to be presented, elements such as grammar points, example sentences, pictures, etc. are filled into the corresponding slide pages in a certain logic to achieve the automatic generation of multimedia presentation slides, which is convenient for more vividly presenting foreign language knowledge.

[0163] (2.2.3) Considering the characteristics and learning progress of different users (teachers and students), personalized content generation is realized.

[0164] The specific implementation is as follows:

[0165] (a) The customized content generation for teachers can be based on the matching algorithm of the teaching syllabus and the course progress. The teaching syllabus text provided by the teacher (key information such as chapter topics, teaching objectives, key points and difficulties in the syllabus can be extracted through text parsing algorithms) and the current course progress situation (such as the knowledge points already taught, the remaining class hours, etc.) are used as inputs. Then, using rule matching or machine learning classification algorithms (similar to the demand classification algorithm mentioned earlier, but the features focus more on teaching-related content), content that meets the current teaching needs is selected from the existing courseware template library or content material library, such as matching relevant modules corresponding to teaching steps, guiding questions, classroom activity suggestions, etc., and integrated in a certain order and format to generate customized courseware content for teachers to use.

[0166] With the help of the second foreign language teaching knowledge graph constructed previously, taking the core knowledge points in the teaching syllabus as nodes, query and expand along the relationship links of the knowledge graph (such as the sequential order and correlation degree between knowledge points), and recommend teaching content suitable for the current course progress and teaching objectives. For example, query the extended grammar, typical example sentences, cultural background knowledge, etc. associated with the grammar point being taught currently, and integrate these contents into parts such as guiding questions and classroom activity suggestions in the courseware to make the courseware more in line with the actual teaching needs.

[0167] (b) The generation of personalized learning materials for students is realized as follows:

[0168] First, conduct an assessment of learning levels and weak links, collect students' learning history data (such as information on the completion of usual homework, test scores, classroom interaction performance, etc.), and perform quantitative analysis on these data. Data analysis methods can be used, such as calculating statistical indicators such as the correct rate and error rate of students in different grammar points, vocabulary types, etc. Determine students' learning levels and weak links through clustering analysis (such as roughly classifying students by learning level using K-Means clustering) or rule-based classification (judging whether a student is weak in a certain knowledge point according to the set error rate threshold). Then, based on the evaluated students' learning levels and weak links, use collaborative filtering algorithms (if there is learning data of multiple students, find other students with similar learning situations and recommend the learning materials with better effects they used) or content-based recommendation algorithms (according to the knowledge points where students are weak, search for special practice contents, targeted explanation materials, etc. directly related to them in the learning database) to generate targeted learning materials for students to help students strengthen learning effects and make up for knowledge shortboards.

[0169] (2.3) Generation algorithm and model application unit

[0170] (2.3.1) Apply advanced generative deep learning models, such as language models based on the Transformer architecture (such as improved versions of GPT-like models) or variational autoencoders (VAE), etc. Specifically as follows:

[0171] Apply advanced generative deep learning models, such as language models based on the Transformer architecture (like improved versions of GPT-like models) or variational autoencoders (VAE), etc., and conduct pre-training on a large-scale foreign language corpus. In this process, the model gradually masters the language patterns, grammar rules, and semantic relationships of the foreign language through learning a large amount of foreign language texts, thus laying a solid foundation for subsequent fine-tuning and practical applications.

[0172] (2.3.2) On the basis of pre-training, the model is fine-tuned using specific data in the field of second foreign language teaching. Specifically: after completing pre-training, specific data in the field of second foreign language teaching is used to finely adjust the model. First, the data collected and sorted out that is closely related to foreign language teaching is input into the model. This data covers rich information such as teaching syllabuses, teaching materials content, classic teaching cases, etc. Through the input of this data, the parameters of the model are optimized and adjusted so that it can better adapt to the specific task of generating second foreign language teaching content. In this process, various optimization techniques are also adopted. For example, gradient clipping technology is used to prevent gradient explosion or disappearance problems, and a reasonable learning rate adjustment strategy is used to balance the learning speed and accuracy of the model, so as to improve the quality and stability of the content finally generated by the model, and minimize the possible errors and unreasonable parts in the generated content.

[0173] (2.3.3) During the content generation process, a quality assessment mechanism is set up. The generated content is evaluated in real time using language model evaluation metrics (such as perplexity) and specific evaluation rules based on foreign language teaching standards. The specific implementation is as follows: In the actual stage of generating foreign language teaching content, a complete set of quality assessment mechanisms is established. On the one hand, general language model evaluation metrics, such as perplexity, are used to measure the language fluency and reasonableness of the generated content; on the other hand, specific evaluation rules are formulated according to foreign language teaching standards. For example, carefully check whether the generated grammar explanation part is accurate, whether the listed examples are truly in line with the daily expression habits of the foreign language, whether the designed exercises have sufficient discrimination to meet the needs of students at different levels and whether they have strong pertinence to strengthen the consolidation of specific knowledge points, etc. If it is found through evaluation that the generated content fails to meet the pre-set quality standards, then the model will be adjusted accordingly, such as further optimizing the parameters, increasing the weight of specific data, etc., or directly regenerating the content. Through such a repeated process of evaluation and adjustment, it is ensured that the finally output foreign language teaching content has a high quality level and can effectively meet the actual needs of second foreign language teaching.

[0174] (2.4) Content update and evolution unit

[0175] (2.4.1) Receive feedback information from users and teachers, including evaluations of the generated content, modification suggestions, and new requirements. The specific implementation is as follows:

[0176] (a) First, establish special feedback collection channels, such as online questionnaires, teaching platform comment areas, emails, etc., in order to efficiently receive various feedback information from users (students) and teachers. This feedback covers evaluations of the generated teaching content in multiple dimensions such as accuracy, understandability, interestingness, etc., as well as specific modification suggestions and new teaching content requirements.

[0177] (b) Then develop an intelligent analysis system to classify and prioritize the collected feedback. For example, for high-frequency issues reported by a large number of users, such as the clarity of specific grammar explanations, AIGC quickly launches a content optimization program, using natural language processing technology to reorganize the explanation text of the relevant grammar points, add more examples, or use more vivid explanations to improve students' understanding.

[0178] (c) For the highly professional teaching method innovations or new ideas for curriculum design proposed by teachers, it is necessary to organize education experts and technical teams to evaluate and transform them. The reasonable and practical suggestions should be transformed into specific algorithmic rules or content templates, and integrated into the content generation logic of AIGC to ensure that the subsequent generated teaching content reflects advanced teaching concepts and methods.

[0179] (2.4.2) With the development of foreign languages ​​and the deepening of second foreign language teaching research, the knowledge reserve is continuously updated.

[0180] The specific implementation is as follows:

[0181] (a) First, form a language monitoring team or an educational research tracking group to closely follow the development of foreign language in terms of vocabulary, grammar, expression, etc., as well as the latest achievements and trends in the field of second foreign language teaching research. For example, regularly analyze language usage data in foreign language mainstream media and social platforms to capture emerging popular vocabulary and grammatical changes; at the same time, conduct in-depth research on the latest teaching research results published in academic journals and educational conferences to obtain innovative teaching concepts and methods.

[0182] (b) Structuring and organizing the collected new language knowledge and teaching concepts into a data format that can be recognized and applied by the AIGC system, such as updating the nodes and relationships in the knowledge graph and expanding the text examples and annotation information in the data set. Taking new popular foreign language vocabulary as an example, not only are the vocabulary itself and its common usage and examples added to the data set, but also related annotations are combined with relevant cultural background information, so that AIGC can provide rich, vivid and practical teaching materials when generating vocabulary learning content, so that the foreign language knowledge learned by students is closely connected with language application in real life, and the timeliness and practicality of the teaching content are maintained.

[0183] (c) Through the close cooperation and continuous operation of the above two aspects, the content update and evolution unit can ensure that the second foreign language teaching content generated by the AIGC system always maintains high quality and keeps pace with the times, and continuously meets the growing teaching needs and requirements for teaching quality of users and teachers.

[0184] (3) Learning progress management and evaluation module functions, such as Figure 4 As shown:

[0185] (3.1) Learning data collection unit

[0186] (3.1.1) Comprehensively collect various interaction data of users in the system, including the voice content input through the intelligent speech recognition module, operation records on the courseware interface (such as answering questions, page stay time, browsing order, etc.). Specifically as follows:

[0187] (a) Collect data through the intelligent speech recognition module. Embed an intelligent speech recognition module with high accuracy and strong compatibility in the system to ensure that the voice content input by users can be accurately captured. Whether it is the voice generated in scenarios such as students practicing foreign language speaking, asking questions to the system, or participating in oral interaction activities, it can be completely and clearly recorded. At the same time, timestamp the voice to facilitate subsequent correspondence to specific learning links and knowledge point categories, providing basic materials for analyzing students' oral expression ability, knowledge doubts, etc.; Use natural language processing technology to perform preliminary processing on the voice content, such as performing voice-to-text operations and extracting key information, such as the foreign language words that appear, the use of grammar structures, and the core themes of questions, so as to more efficiently reflect the students' oral expression habits, language proficiency, and doubts about specific knowledge.

[0188] (b) Collect data through operation records on the courseware interface. In the front-end page design of the courseware, implant special data collection code to comprehensively and secretly record every operation behavior of students on the interface. For answering questions, accurately record the selection results, fill-in-the-blank content, etc. of each question; For the page stay time, record the stay duration of students on different knowledge point pages in seconds to judge the attention of students to this knowledge point and the time required for understanding and digestion; The recording of the browsing order can be achieved by tracking the page jump path, thereby depicting the overall learning path of students and understanding the sequence and logical habits of their knowledge acquisition; Use data analysis tools to integrate and visually present the collected operation record data, converting scattered data into intuitive charts or reports. For example, show the flow of students between different course sections through a learning path map, and clearly present the difference in students' attention to different knowledge points through a bar comparison chart of the stay duration on each knowledge point page, providing convenience for subsequent in-depth analysis.

[0189] (3.1.2) Detailedly record the results of various exercises and tests completed by students in the system. The specific implementation is as follows:

[0190] (a) Record data based on the basic information of answering questions. That is, when students start various exercises and tests, the system automatically creates independent data record units for each question in the background, and records the answers submitted by students in real time, clearly marking the correct or incorrect status of the answers. For example, in foreign language grammar multiple-choice questions, accurately record whether the options selected by students are consistent with the correct answers; for subjective questions such as short-answer questions and translation questions, determine the correctness of the answers through preset scoring rules and record the corresponding scores in detail; at the same time, start the timer function to accurately record the time spent by students from seeing the question to submitting the answer, accurate to the millisecond level, so as to measure the speed and thinking efficiency of students answering questions. In addition, for the modification situation during the answering process, use the version record method to record the content of each modification by students, the modification time, and the differences before and after the modification. For example, in foreign language writing exercises, record the modification situation of students in aspects such as sentence structure and vocabulary use, so as to comprehensively analyze the thinking process and knowledge application ability of students.

[0191] (b) Conduct in-depth data mining and correlation analysis. Integrate and correlate the above-mentioned data of each question to construct a multi-dimensional data set with students as individuals and different exercises and tests as classifications. Through data analysis algorithms such as correlation analysis and clustering analysis, dig out the learning rules hidden behind the data. For example, analyze the relationship between the answering time and the correctness of the answers to explore whether there are situations where wrong answers are caused by too long thinking time or mistakes due to hasty answering; compare the differences in the modification situations of questions on different knowledge points to understand the firmness of students' mastery of each knowledge point and the places where they are easily confused, providing a strong basis for subsequent personalized learning suggestions.

[0192] (3.1.3) Monitor the usage frequency and usage methods of various learning resources by students. The specific implementation is as follows:

[0193] (a) Conduct resource usage frequency statistics. For various learning resources in the system, such as grammar explanation videos, foreign language listening materials, reading texts, etc., embed counter and timer codes in their corresponding function modules such as play and open. Whenever a student clicks to play a grammar explanation video or opens a listening material to start learning, the corresponding counter will automatically increment by one to accurately record the number of times the resource is used; at the same time, the timer starts to record the actual duration of the student watching the video and listening to the listening material, which is statistically counted in minutes, so as to clarify the usage frequency and input time of students for different resources; regularly summarize and organize the data collected by these counters and timers to generate a resource usage frequency report, which shows the change trend of the usage times of different resources within a certain period (such as weekly, monthly) in intuitive forms such as bar charts and line charts, as well as the distribution of the time spent by students on each resource, facilitating a quick understanding of the preference degree and input degree differences of students for learning resources in different knowledge sections.

[0194] (b) In the case of statistical analysis of learning resource usage data, further analyze whether students have the behavior of repeatedly learning certain resources. By comparing data such as the time intervals between multiple uses of resources and changes in learning durations, judge the students' mastery of specific knowledge sections and their learning needs. For example, if it is found that a student frequently watches the video explanation of a certain grammar difficulty point and the viewing duration each time is relatively long, it indicates that the student may have difficulty understanding this grammar point and requires targeted tutoring or more practice consolidation materials; combined with attribute information such as the type of resources and the knowledge sections they belong to, use data mining techniques to analyze the overall learning behavior patterns of students. For example, it is found that students tend to watch grammar explanation videos first and then conduct corresponding listening practice, or their preference for the learning order of foreign language reading texts at different difficulty levels, etc., providing valuable reference basis for optimizing learning resource recommendations and adjusting the arrangement of course content to better fit the learning habits and needs of students.

[0195] Through the above detailed and comprehensive implementation method, the learning data collection unit can accurately and effectively collect various types of data related to students' learning, laying a solid data foundation for subsequent learning effect evaluation, personalized learning services and other links.

[0196] (3.2) Learning progress analysis unit

[0197] (3.2.1) Quantitatively evaluate the students' mastery of each foreign language knowledge point based on the collected data.

[0198] The specific implementation is as follows:

[0199] (a) The collection of learning behavior data can be carried out from page browsing records. That is, when students use foreign language learning courseware or related learning platforms, through web tracking technology (such as embedding JavaScript code in the page to monitor events such as page loading and switching) or in-app embedding technology (setting code recording points at key interaction positions in the APP), real-time collect information on students' browsing of different learning courseware pages, including the URL (Uniform Resource Locator, used to identify specific page content) of the page, the page title (usually reflecting the theme of this part of the learning content), the access timestamp (accurately recording when the student accesses this page), etc. These data can reflect the learning content actively selected by students and the sequence.

[0200] For various learning resources, such as video tutorials, audio explanations, text materials, practice questions, etc., usage records are also kept. When a student clicks to play a video, open an audio file, download text materials, or start doing practice questions, the system records the identifier of the corresponding resource (such as the unique number of the resource, file name, etc.), the start time of use, the end time of use (if there is a clear end behavior, such as the video playing to the end, the practice questions being submitted, etc.), and the knowledge point tags corresponding to the resource (each learning resource is pre-labeled with the main knowledge points it covers for subsequent correlation analysis); all the collected timestamp data is standardized according to a unified time format (such as the ISO 8601 standard format) to ensure the consistency of time representation for data from different sources, facilitating subsequent time series analysis. At the same time, the timestamp is converted into a time difference based on a certain fixed starting time (such as taking the time when the student first logs in to the learning system as zero, and the timestamps of all subsequent behaviors are converted into the number of seconds or milliseconds from this zero point), which is convenient for numerical calculations and comparisons in the time dimension.

[0201] (b) Take each learning behavior of the student (page browsing, resource usage, etc.) as a data point in the time series, and arrange them in chronological order to form the learning behavior time series data exclusive to each student.

[0202] (3.2.2) Learning trajectory analysis: Construct the learning trajectory of the student and analyze their investment in foreign language learning and the sequence of knowledge acquisition at different time points. The specific implementation is as follows:

[0203] (a) Carefully sort out and label the knowledge points covered by all learning coursewares and learning resources to establish a complete foreign language knowledge point classification system. For each learning resource, determine the specific set of knowledge points it involves through manual annotation combined with natural language processing techniques (such as using text mining algorithms to extract key concepts from the resource content and match them with the corresponding knowledge points) so as to accurately associate the student's learning behaviors with the corresponding knowledge points in the future.

[0204] Based on the records of the student's use of learning resources and the knowledge point annotations corresponding to the resources, convert the student's learning behaviors into a learning sequence of knowledge points, intuitively presenting the sequence of the student's knowledge acquisition, and constructing a learning trajectory based on knowledge points.

[0205] (b) Complete the quantitative representation of learning investment, that is, calculate the learning duration. According to the start time and end time records of students' use of learning resources, calculate the duration spent on each learning behavior. Associate these calculated duration data with the corresponding knowledge points to represent the learning investment time of students on each knowledge point; count the learning frequency, that is, count the number of times students access learning resources related to each knowledge point within a certain time range. This frequency data can reflect the degree of attention and investment of students in this knowledge point from another angle. Combined with the learning duration data, it can more comprehensively quantify the learning investment of students on different knowledge points.

[0206] (3.2.3) Calculate the learning speed of students and analyze the stability of the learning process at the same time. Judge the learning state of students by observing the fluctuations of practice and test results. Specifically as follows: (a) First, to complete the abnormal analysis of the time invested in knowledge points, it is necessary to conduct time distribution statistics and standardization first, that is, statistically summarize the learning investment time of students on each knowledge point (considering both learning duration and learning frequency), and form the learning time distribution of each student on knowledge points. To facilitate the comparison and analysis of the time investment of different knowledge points and different students, the time data can be standardized. For example, the Z-score standardization method can be used to convert the learning time of each knowledge point into a standard score with a mean of 0 and a standard deviation of 1, eliminating the influence of factors such as the learning difficulty and resource volume of different knowledge points themselves, making the data comparable.

[0207] (b) Then use anomaly detection algorithms (such as the 3σ principle based on statistics, the outlier detection method based on clustering, the anomaly detection model based on deep learning, etc.) to identify the abnormal situations where students spend too much or too little time on certain knowledge points. Taking the 3σ principle as an example, if the standardized score of the learning time of a certain knowledge point exceeds the range of the mean ± 3 times the standard deviation, it can be determined as an abnormal situation of time investment. For the knowledge points determined to be abnormal, further analyze the reasons in combination with the learning trajectory of students (such as behaviors such as whether they frequently return to review, whether they quickly skip, etc.). For example, if a student frequently returns to review a vocabulary knowledge point, it may indicate that this knowledge point is of certain difficulty to him and requires additional learning support or guidance.

[0208] (3.3) Personalized learning plan adjustment unit

[0209] (3.3.1) Generate targeted learning suggestions for each student according to the results of learning progress analysis. The specific implementation is as follows:

[0210] (a) To obtain and quantitatively represent the learning progress analysis results, first, evaluate the mastery level of knowledge points. That is, comprehensively evaluate the student's mastery of each knowledge point through various methods. On the one hand, collect the data of the student's answering situation in practice questions and test questions. For objective questions (such as multiple-choice questions and true-false questions), directly judge according to the correct rate of answering; for subjective questions (such as short-answer questions, translation questions, writing questions, etc.), use text similarity algorithms (such as methods based on cosine similarity, edit distance, etc. combined with word vector models to compare the similarity between the student's answer and the standard answer) to measure the answering quality, and then infer the mastery level of knowledge points. On the other hand, analyze the behavioral data of the student's use of learning resources. For example, check the completeness of watching learning videos, the number of repeated visits to materials related to key knowledge points, the pauses and replays during the learning process, etc., to assist in judging the understanding and mastery status of knowledge points; quantitatively represent the mastery level of knowledge points to facilitate subsequent comparison and analysis.

[0211] (b) Then calculate the learning speed and overall progress, that is, calculate the learning speed of the student in different learning stages and different knowledge point sections. It is measured based on the time spent by the student to complete each learning task (such as finishing a unit course, finishing a set of practice questions, etc.). For example, divide the total time spent on learning a certain knowledge point by the standard learning time corresponding to this knowledge point (which can be obtained according to the teaching syllabus or the average learning time statistics of a large number of students in the past) to get the learning speed coefficient of this knowledge point. A coefficient greater than 1 indicates a slower learning speed, and a coefficient less than 1 indicates a faster learning speed.

[0212] (c) The learning advice generation algorithm based on the degree of mastery includes the recommendation of extended learning content (for better - mastered knowledge points), that is, constructing a foreign - language knowledge graph, connecting each knowledge point according to semantic relationships (such as the similarity between grammars, the associated collocations of vocabulary, the application relationships between listening, speaking, reading, and writing skills and corresponding knowledge points, etc.) to form a network structure. When it is determined that a student has a good mastery of a certain knowledge point, graph search algorithms (such as Depth - First Search DFS, Breadth - First Search BFS) are used to find high - level content nodes in the knowledge graph that are extensionally related to this knowledge point. Then, learning resource matching is carried out. According to the type of extended content (such as reading materials, oral practice scenarios, etc.), matching resources are screened from the learning resource library. For the recommendation of reading materials, more advanced texts can be selected based on a text difficulty assessment algorithm (such as constructing a difficulty assessment model using indicators such as word frequency statistics, sentence length, and grammar complexity to adapt the reading materials to the student's current level); for the recommendation of oral practice scenarios, considering the student's oral level and the complexity of the scenario, appropriate and more challenging oral practice scenarios are recommended for them; for weak knowledge points, by analyzing the degree - of - mastery vector of knowledge points, knowledge points with a degree - of - mastery value lower than a certain threshold (such as 0.6, which can be adjusted according to teaching experience and actual effects) are determined as weak knowledge points and the reasons for weakness are further analyzed.

[0213] (3.3.2) Dynamically adjust the student's learning plan to ensure its match with the learning progress. The specific implementation is as follows:

[0214] (a) Implement the evaluation of the completion of learning tasks and the mastery effect. That is, first, task judgment is completed. The completion status of each learning task of the student is monitored in real - time. Whether the task is completed is determined by recording the start time, end time, and the submitted results of the task. For some tasks with clear completion criteria, judgment is made according to the criteria; for relatively flexible learning tasks, reasonable completion behavior indicators are set to determine the completion of the task. After the student completes a learning task, the completion situation is compared with the expected progress in the learning plan to see whether it is completed ahead of schedule, on time, or behind schedule, providing basic data for subsequent progress adjustment; using the above - mentioned knowledge - point mastery degree evaluation method, after the student completes a learning task, the mastery effect of the corresponding knowledge points is analyzed in a timely manner.

[0215] (b) In the dynamic adjustment of the learning plan, when the progress shows an accelerating adjustment situation, that is, when it is determined that the student has completed a certain stage of learning tasks ahead of schedule and has a good mastery, according to the knowledge progression relationship of the curriculum system and the student's learning trajectory, the learning content of the subsequent stage is screened and pushed. Dynamically adjust the time arrangement in the learning plan, and reasonably compress the time interval of each learning task according to the amount of time completed in advance and the expected difficulty of the subsequent learning content; linear programming algorithms or heuristic algorithms (such as greedy algorithms, which select the learning task that is currently most conducive to accelerating progress each time for time optimization) can be used to optimize the learning time allocation, so that the student can enter the next stage of learning faster while ensuring the learning effect.

[0216] By analyzing the student's wrong performance, distribution of weak knowledge points, and learning behavior characteristics (such as frequent pauses, still not mastering after repeated learning many times, etc.) in relevant tasks, find out the reasons for learning difficulties, then screen out the corresponding review materials from the learning resource library, incorporate these materials back into the learning plan, and arrange additional review time and times; appropriately extend the time period for learning the current difficult content in the learning plan and slow down the progress of pushing the subsequent learning content. And quantify and increase the learning time by a certain proportion according to indicators such as the error rate of learning passing, the gap between the mastery level and the expectation, and postpone the start time of the subsequent learning tasks in the original plan to ensure that the learning plan conforms to the student's actual learning ability and needs.

[0217] (3.4) Learning Effect Evaluation and Feedback Unit

[0218] (3.4.1) Regularly generate a comprehensive evaluation report on the student's learning effect and display the learning achievements to teachers and students. The specific implementation is as follows:

[0219] (a) Conduct data collection and collation, that is, extract the student's data in each learning module from the learning management system. Record and quantify the participation in classroom interactions. Score and classify according to the predetermined evaluation criteria; count the student's correct rate, error types and distribution frequencies.

[0220] (b) Design a set of simple, clear and easy-to-understand visualization chart systems, such as using bar charts to show the mastery of knowledge points, line charts to present the change trend of learning achievements, and pie charts to analyze the learning time allocation, etc.

[0221] (3.4.2) Provide teachers with detailed information about the student's learning situation to facilitate targeted teaching intervention. The specific implementation is as follows:

[0222] (a) Conduct an analysis of the overall learning level of the class, that is, calculate the average value, median, and standard deviation of the class students on various evaluation indicators (such as total score, average score of each knowledge point, average score of classroom participation, etc.), so as to measure the overall learning level and performance distribution of the class and provide reference for teachers to formulate differentiated teaching strategies.

[0223] (b) Conduct an analysis of individual differences, that is, generate personalized learning profiles for each student, including the student's basic information, learning history records (such as past course grades, learning habit characteristics, etc.), detailed data and analysis results of this learning effect evaluation, so as to formulate targeted tutoring plans.

[0224] (c) Put forward suggestions for adjusting teaching strategies, that is, according to the analysis results of the overall and individual learning situations of the class, provide specific suggestions for teachers to adjust teaching strategies. And regularly track the learning progress of students and adjust tutoring strategies in a timely manner.

[0225] (3.4.3) Help students conduct self-assessment. The specific implementation is as follows:

[0226] (a) Conduct learning achievement displays, that is, create personal learning achievement display pages for each student, and present the achievements of students in aspects such as grade changes, knowledge point mastery, and skill improvement at each learning stage in a combination of charts and text.

[0227] (b) Conduct an analysis of the progress trend. That is, in addition to showing the current learning achievements, it is also necessary to help students analyze their learning progress trends. By comparing the learning data of students at different time periods (such as the growth of vocabulary per week, the improvement of reading speed per month, etc.), draw a progress trend curve.

[0228] (c) Implement incentive and guidance measures, that is, add motivating words and suggestions to the students' self-assessment reports, and recommend learning resources such as foreign language learning websites, books, and APPs at the students' current levels, as well as learning methods and skills.

[0229] (4) The functions of the real-time interaction and feedback module, such as Figure 5 shown as follows:

[0230] (4.1) Real-time interaction function unit

[0231] (4.1.1) This module supports multiple interaction channels, including voice interaction, text interaction, etc. The specific implementation is as follows:

[0232] (a) The speech recognition algorithm is adopted, that is, feature extraction. First, the collected speech signal is preprocessed, including noise reduction, framing, and extraction of acoustic features. The acoustic features are extracted using Mel Frequency Cepstral Coefficients (MFCC), which simulates the human ear's perception characteristics of sound frequencies. It is obtained through a series of operations such as fast Fourier transform and Mel scale filtering on the speech signal, and can effectively represent the characteristic information of speech.

[0233] (b) The combination of the acoustic model and the language model is realized. That is, a deep neural network - hidden Markov model (DNN - HMM) or an end - to - end deep learning architecture (such as a model combining convolutional neural network CNN and long short - term memory network LSTM, Transformer architecture, etc.) is used as the acoustic model to map the extracted speech acoustic features to the probability distribution of speech basic units such as phonemes. The acoustic model learns the relationship between the acoustic features of speech and the pronunciation rules. At the same time, a large - scale text corpus is used to train the language model (such as a statistical language model based on n - gram, a neural network - based language model such as the GPT series, pre - trained language models such as Bert fine - tuned in the foreign language field). The language model is used to predict the probability distribution of language structures such as words and sentences, helping to improve the accuracy of speech recognition, especially in processing continuous speech, differentiating homophones, etc.

[0234] (c) Decoding and recognition are completed. That is, through a decoder (such as a decoder based on the Viterbi algorithm, which can find the most likely speech recognition result path in the search space jointly constructed by the acoustic model and the language model), the probability distribution output by the acoustic model and the prediction result of the language model are combined, and finally the speech signal is converted into text content, enabling teachers and students to communicate with the system smoothly and naturally through speech.

[0235] (d) When the system needs to reply to teachers or students in voice form, it first analyzes the text to be synthesized into speech, including natural language processing operations such as word segmentation, part-of-speech tagging, and prosody tagging, to better understand the grammatical structure and semantic information of the text and prepare for subsequent speech synthesis; it uses parametric synthesis, concatenative synthesis, or deep learning-based end-to-end speech synthesis methods to generate the acoustic parameters of the speech. Parametric synthesis is based on a vocal tract model and synthesizes speech by calculating acoustic parameters such as fundamental frequency and formants, but the naturalness of the synthesized speech is limited; concatenative synthesis is to splice pre-recorded speech segments (such as phonemes, syllables, etc.) according to certain rules to form complete speech, and the naturalness is improved; deep learning-based end-to-end speech synthesis (such as the Tacotron series and its variant models) directly generates speech waveforms from text input, and the effect is more natural and fluent. For example, when generating the speech of a foreign language sentence, according to the results of text analysis and the selected synthesis method, the corresponding acoustic parameters are generated or the speech waveform data is directly obtained; in the case of generating acoustic parameters, a vocoder (a module that converts acoustic parameters into speech waveforms, such as advanced vocoders like WaveNet and Parallel WaveGAN) is used to convert the acoustic parameters into audible speech waveforms to complete the speech synthesis process. And the synthesized speech can be post-processed and optimized, such as adjusting parameters such as volume, speed, and timbre to make it more in line with the communication scenario and user preferences, realizing natural and fluent speech interaction and simulating a real classroom dialogue environment.

[0236] (e) For text input processing, a front-end interactive interface is designed to provide a convenient and friendly input interface for text interaction. For example, a text input box is set in a web-based or mobile application, supporting common input method switches (such as foreign language input methods, Chinese input methods, English input methods, etc., to facilitate teachers and students with different language backgrounds), and the input box is functionally optimized, such as supporting auto-completion (intelligently completing according to the content already entered by the user through predefined vocabulary lists or common words in learning resources to improve input efficiency), grammar check and prompt (using a foreign language grammar rule library to check in real time whether there are grammar errors in the input text and giving prompts to help users express accurately), and other functions.

[0237] After the user enters the text, it is preprocessed at the back end, including encoding conversion (uniformly converting the input text into a common character encoding format, such as UTF-8, to ensure the correct processing and storage of the text in the system), removing special characters (cleaning some illegal or interfering characters that may affect subsequent analysis), and performing basic text normalization operations (such as converting full-width characters to half-width characters, unifying case, etc., to make the text format meet the processing requirements), etc., to prepare for further text analysis.

[0238] (f) Natural language understanding and intent analysis can be achieved as follows: First, use word segmentation algorithms (such as the forward maximum matching method, reverse maximum matching method based on dictionary matching, and hidden Markov model word segmentation method, conditional random field word segmentation method based on statistical machine learning, etc.) to divide the input text into individual words, facilitating subsequent analysis of the relationships between words. At the same time, perform part-of-speech tagging to determine the part-of-speech category of each word (noun, verb, adjective, etc.). Commonly used part-of-speech tagging algorithms are models constructed based on machine learning algorithms such as hidden Markov models, maximum entropy models, and conditional random fields, which help understand the sentence structure and initially grasp the user's intent; Second, use syntactic analysis algorithms to construct the syntactic structure tree of the sentence, such as syntactic analysis algorithms based on probabilistic context-free grammar (PCFG), deterministic syntactic analysis algorithms based on shift-reduce, etc., to clarify the syntactic relationships of each component in the sentence and understand the semantic logic of the text.

[0239] (4.1.2) During the teaching process, messages can be pushed in real time. The specific implementation is as follows:

[0240] (a) The message push architecture and communication protocol select a real-time communication architecture based on WebSocket (suitable for web applications). Among them, WebSocket is a network protocol for full-duplex communication on a single TCP connection. It allows the server to actively push messages to the client, which is very suitable for real-time message push scenarios. Support for the WebSocket protocol is implemented on both the server side and the client side (the browser interfaces used by teachers and students) of the system. The server side creates a WebSocket server, listens on a specific port, and waits for connection requests from the client; the client, when the web page is loaded, initiates a WebSocket connection request through JavaScript code to establish a two-way communication channel with the server.

[0241] When the teacher initiates a message that needs to be pushed, such as a classroom discussion topic, the server encapsulates the message into the WebSocket message frame format and immediately sends it to all participating student clients through the established connection channel. After the browser of the student client receives the message, the message is displayed on the interactive interface through mechanisms such as JavaScript callback functions, ensuring that the information can be conveyed to the students in a timely and accurate manner, and guaranteeing the timeliness and coherence of communication.

[0242] (b) Message Queue and Push Service (Applicable to Mobile Applications or Distributed System Scenarios) Introduce a message queue middleware (such as RabbitMQ, Kafka, etc.) to manage and transmit messages. Messages initiated by teachers are first sent to the message queue, and the message queue stores and sorts messages according to certain rules (such as based on the user groups of recipients, topic classification of messages, etc.). For example, for messages related to classroom discussion topics, a specific "Classroom Discussion" topic can be set, and relevant messages are placed in the queue of this topic.

[0243] As a consumer of the message queue, the push service is responsible for retrieving messages from the queue and pushing the messages to the corresponding student client applications based on the device information of the target students (such as push tokens of mobile devices). The push service can integrate existing mobile push platforms (such as Apple's APNs for iOS device push, Google's Firebase Cloud Messaging for Android device push, etc.), and utilize their stable and efficient push mechanisms to ensure that messages can be quickly delivered to the student's interaction interface, realizing the real-time message push function. Regardless of the type of mobile device the student uses, they can receive messages in a timely manner to participate in the communication.

[0244] (c) Build a mechanism for ensuring message real-time and concurrent processing. Whether it is a WebSocket connection or a connection between a mobile application and a server, it is necessary to maintain a long connection state to ensure the reliability of real-time message push. Adopt a heartbeat mechanism to detect the validity of the connection, that is, the client periodically sends a very small heartbeat packet to the server, and the server replies with a confirmation message after receiving the heartbeat packet. If the client does not receive a reply within the specified time, it is determined that the connection may have a problem, and it will try to re-establish the connection to ensure the smoothness of the message channel and avoid message push interruption caused by network fluctuations and other reasons.

[0245] The server effectively manages the long connection, records the status of each connection (such as connection time, the time of the last activity, client information, etc.), and when there is a message to be pushed, it can quickly locate the corresponding connection and accurately push the message.

[0246] (d) Implement concurrent processing and message order guarantee. That is, in a teaching interaction scenario involving multiple people, there may be multiple teachers initiating message pushes or multiple students asking questions and answering at the same time, resulting in a large number of concurrent messages. The server uses concurrent processing technologies such as multi-threading, multi-processing, or asynchronous I / O to handle these messages. For example, through a thread pool mechanism, a certain number of worker threads are pre-created. When a message arrives, it is assigned to an idle thread for processing, avoiding the impact on the timeliness of other message pushes due to the long time-consuming processing of a single message.

[0247] For the sequential guarantee of messages, a unique identifier and timestamp are added to each message. At the receiving end, the messages are sorted and displayed according to the identifier or time order, ensuring that the messages received by students are presented in the order in which the teacher sent them or the logical order of event occurrence, maintaining the coherence of communication and avoiding message chaos caused by concurrent message processing.

[0248] (4.1.3) Students and teachers can be grouped according to teaching needs. The specific implementation is as follows:

[0249] (a) Design the grouping data structure, that is, design a dedicated grouping data table in the system's database to store grouping-related information. The structure of the data table can include fields such as group number (uniquely identifying each group), group name (facilitating identification by teachers and students), group leader information (which can be a student designated by the teacher or the default first student to join the group, recording their user identifier), member list (storing an array of user identifiers of group members), creation time, update time, etc., clearly recording the basic situation and member composition of each group.

[0250] (b) The grouping creation and modification operation algorithm means that when a teacher needs to create a group, they input information such as the group name and select members through the grouping management interface provided by the system. The system encapsulates this data into the corresponding record format in the background and inserts it into the grouping data table. At the same time, the associated relationship table is updated to complete the grouping creation operation. (c) The establishment of the in-group message channel can be based on the real-time message push architecture mentioned above, and an independent message channel is established for each group. For example, in the WebSocket application scenario, when a student joins a group, the WebSocket connection established between the client and the server will be marked with the identifier of that group. When the server pushes messages, according to the target group identifier of the message, the message is only sent to the member clients belonging to that group, realizing the directional push of in-group messages and ensuring that the interaction information within the group is only propagated within the group.

[0251] (c) Similarly, in the message queue and push service scenario, specific message queue topics are set for each group (such as topics like "Group 1 Discussion" and "Group 2 Communication"). Messages sent by group members are all published to the corresponding group topic queues, and the push service retrieves the messages from these queues and pushes them to the corresponding group members, ensuring the independence and privacy of in-group interaction messages.

[0252] (d) The realization of the teacher monitoring and guidance function requires permission management and interface design, that is, special permissions are granted to teachers so that they can view and manage the activities of each group in the system.

[0253] (4.2) Feedback collection function unit

[0254] (4.2.1) Provide convenient feedback entrances for teachers and students to collect their experience feedback during the use of the system. The specific implementation is as follows:

[0255] (a) Design feedback entrances, that is, in the user interface (UI) of the learning system, set prominent and easy-to-operate feedback buttons, and distinguish and identify them for teacher and student users respectively; ensure that users can quickly locate the feedback submission area. After clicking the feedback button, a detailed feedback form will pop up, and the form content covers the following main aspects:

[0256] (a1) User basic information: Automatically obtain and fill in basic information such as the user's name, class (for students), and taught courses (for teachers) to facilitate subsequent classification and targeted analysis of feedback information.

[0257] (a2) Feedback type selection: Provide a drop-down menu for users to select the general category to which the feedback belongs, such as courseware content, teaching methods, system operations, others, etc., to facilitate the system's preliminary screening and sorting of feedback.

[0258] (a3) Specific feedback content filling area: In the form of a text box, allow users to describe their usage experience and opinions in detail.

[0259] (b) Collect feedback on the quality of courseware content, including the clarity of knowledge point explanations, the practicality of cases and examples, and the effects of multimedia elements such as animations and audio.

[0260] (c) Collect feedback on the application of teaching methods, including students' classroom interaction experiences and analysis of teachers' method adaptability. The actual application situation in teaching can be detailedly feedbacked to provide a practical basis for the continuous innovation and optimization of teaching methods.

[0261] (d) Collect feedback on the convenience of system operations, including the settings of login and registration processes, the ease of use of function operations, and device compatibility; so that the technical team can optimize and adapt for different devices to ensure that users can use the learning system smoothly on various common devices.

[0262] (4.2.2) Collect learning effect feedback from multiple perspectives. The specific implementation is as follows:

[0263] (a) Automatic feedback collection based on exercise and test data

[0264] When students are doing online exercises and tests, the system automatically records the students' answering behavior data. At the same time, for some questions with subjective answering nature (such as foreign language writing, translation, etc.), the system can use natural language processing technology to preliminarily analyze the students' answers and conduct quantitative statistics, generating data reports on each student's scores, error rate distributions, answering speeds, etc. on various knowledge points and question types, providing a data basis for subsequent learning effect evaluation and feedback.

[0265] Through in-depth analysis of students' exercise and test data, the system automatically generates a learning situation diagnosis report and learning suggestions for each student. At the same time, using big data analysis technology, it conducts horizontal comparison and trend analysis on the overall exercise and test data of the class students, identifying common problems and advantageous areas in the learning process of the class students.

[0266] (b) Collection of active feedback from students and teachers

[0267] Construction of students' active feedback channels, that is, setting up a special "Learning Difficulties and Progress Feedback" section in the learning system. Students can log in to this section at any time and use text descriptions to detail the difficulties and problems they encounter in the learning process, as well as the progress and achievements they have made. To facilitate students' feedback, this section can be classified according to different aspects of foreign language learning. Students only need to select the corresponding classification label to quickly enter the feedback filling page, filling in evaluations of the difficulty of the current learning task, analysis of the effectiveness of the learning methods and strategies they used in the process of completing the task, and expectations and plans for future learning. At the same time, teachers can also regularly view the content of students' active feedback, timely understand the learning dynamics of students, and give targeted guidance and encouragement.

[0268] Improvement of teachers' active feedback mechanism, that is, establishing a regular teacher feedback system, requiring teachers to submit a detailed feedback report on the teaching process and students' learning situation through the teacher feedback entrance provided by the system after completing the teaching tasks of each teaching stage. The content of the feedback report should include but not be limited to: the achievement of teaching objectives in this stage, the evaluation of the implementation effect of the teaching methods adopted, the overall change trend of the class students in terms of learning attitude and academic performance, case analysis of the outstanding performance and learning difficulties of individual students, and the use feelings and improvement suggestions for teaching resources (such as textbooks, courseware, exercise questions, etc.).

[0269] (4.2.3) During real-time interaction, collect feedback on the interaction quality. The specific implementation is as follows:

[0270] (a) After each voice interaction activity ends, the system automatically pops up a voice interaction quality feedback questionnaire. The content of the questionnaire mainly includes voice clarity evaluation, voice recognition accuracy feedback, and feedback on understanding difficulties.

[0271] (b) For the text interaction functions in the system (such as online chat windows, forum post replies, homework correction comments, etc.), set corresponding feedback collection methods, including the evaluation of the operation convenience of the text input box, the information delay feedback during real-time text interaction, and the feedback on the integrity of the interaction function.

[0272] Through the above detailed refined design of the feedback collection functional units, various feedback information from teachers and students during the use of the learning system can be comprehensively and deeply collected, providing strong support for the continuous optimization of the system and the continuous improvement of teaching quality.

[0273] (4.3) Feedback Analysis and Processing Unit

[0274] (4.3.1) Conduct sentiment analysis on the collected feedback content to judge whether the user's attitude is positive, negative or neutral. The specific implementation is as follows:

[0275] (a) Establish a sentiment keyword library, that is, construct a comprehensive and professional sentiment keyword library covering various words and phrases of positive, negative and neutral emotions; this keyword library needs to be continuously updated and improved to adapt to different users' expression habits and newly emerging language expressions, ensuring the accuracy of sentiment analysis.

[0276] For foreign language feedback content, establish a corresponding foreign language sentiment keyword library at the same time, and establish a mapping relationship with the Chinese keyword library so that the system can accurately identify and classify the sentiment tendencies of feedback in different languages.

[0277] (b) Use advanced natural language processing (NLP) algorithms, such as text classification models based on deep learning, to preprocess the feedback text. First, tokenize the text, perform part-of-speech tagging and named entity recognition, and convert the text into structured data that the computer can understand. Then, through word vector representation technology, map each word to a low-dimensional vector space to better capture the semantic relationships between words.

[0278] In the sentiment analysis stage, use a pre-trained sentiment classification model, input the feedback text into the model, and the model judges its sentiment tendency according to the occurrence frequency, intensity and context semantic information of sentiment keywords in the text.

[0279] To improve the accuracy of sentiment analysis, adopt an ensemble learning method, combine multiple different sentiment classification models for voting or weighted averaging, reduce the errors and biases of a single model. At the same time, continuously train and optimize the model, and use newly collected feedback data to continuously adjust the model parameters so that it can adapt to the sentiment analysis needs of different user groups and feedback scenarios.

[0280] (c) Present the results of sentiment analysis to relevant personnel in an intuitive visual manner, such as using a bar chart or pie chart to show the proportion distribution of positive, negative, and neutral feedback.

[0281] (4.3.2) Classify and summarize the problems in the feedback content. The specific implementation is as follows:

[0282] (a) For problems with courseware content, establish a multi-level classification framework. First, classify according to the foreign language knowledge system; under each major category, further subdivide according to specific problem types, such as grammar explanation problems, vocabulary selection problems, rationality problems of example sentences,

[0283] For system-related problems, also construct a detailed classification system. Include categories such as interaction function problems, interface design problems, stability problems, security problems (such as the risk of user information leakage), etc., to ensure that all types of problems that may occur in the system can be comprehensively covered.

[0284] (b) Use text classification algorithms based on machine learning or deep learning to automatically classify the feedback content. First, collect a large number of feedback texts with labeled problem types as training data, use feature engineering techniques to extract features of the text, such as bag-of-words model, TF-IDF features, word vector features, etc., and then train a classification model (such as support vector machine, decision tree, convolutional neural network, etc.) so that it can learn the text feature patterns of different problem types.

[0285] In the actual classification process, input the newly collected feedback text into the trained model. The model predicts the problem category to which the text belongs according to the text features and automatically classifies it into the corresponding classification folder or database table. At the same time, adopt semi-supervised learning methods, combine manual annotation and automatic classification results, continuously optimize the classification performance of the model, improve the accuracy and recall rate of classification, and ensure that all types of problems can be accurately classified and summarized.

[0286] (c) Conduct statistical analysis on the classified feedback problems, calculate the frequency and proportion of each problem category, and understand the prevalence of various problems.

[0287] According to the severity and scope of influence of the problems, prioritize various problems. Through prioritization, the development and teaching teams can clarify which problems need to be solved first, reasonably allocate resources, and improve the efficiency and effectiveness of problem handling.

[0288] (4.3.3) Based on the results of feedback analysis, propose targeted handling measures and improvement suggestions. The specific implementation is as follows:

[0289] (a) After the feedback analysis and classification summary are completed, according to the priority and type of the problems, assign them to the corresponding responsible personnel or teams. For problems with courseware content, such as unclear grammar explanations, the teaching content editing team is responsible for handling them.

[0290] (b) During the process of handling feedback problems, for system compatibility problems that occur repeatedly, it is recommended that the development team establish a more perfect compatibility testing process. Before system updates and upgrades, conduct comprehensive tests on mainstream devices and browsers to discover and solve potential compatibility hidden dangers in advance; for the problem of insufficient in-depth explanation of knowledge points frequently appearing in courseware content, it is recommended that the teaching team strengthen the research on teaching syllabuses and textbooks.

[0291] At the same time, establish an improvement suggestion tracking mechanism, regularly check and evaluate the implementation of improvement measures, adjust and improve the improvement suggestions according to the actual effects, and form a continuous improvement closed-loop management process to continuously improve the quality of the system and teaching content and user satisfaction.

[0292] Through the above specific refinement of the feedback analysis and processing unit, the feedback information of teachers and students can be processed more scientifically and efficiently, realizing the optimization and upgrade of the system and teaching content, and providing a better learning and teaching experience for users.

[0293] (4.4) Feedback Result Presentation and Application Unit

[0294] (4.4.1) Present the results of the feedback analysis to teachers and students in a visual way. The specific implementation is as follows:

[0295] Build a dedicated feedback result visualization platform that is seamlessly integrated with the learning system. Teachers and students can conveniently access the feedback result page by logging in to their personal accounts on the learning system. The platform adopts an intuitive and user-friendly interface design to ensure that even users without a technical background can easily understand and operate it.

[0296] In terms of technical implementation, use front-end development frameworks (such as React, Vue.js, etc.) combined with data visualization libraries (such as Echarts, D3.js, etc.) to achieve a rich variety of visual component displays. At the same time, ensure that the platform has a good responsive design and can adaptively display on different devices (such as computers, tablets, mobile phones) to meet the needs of teachers and students to view feedback results anytime and anywhere.

[0297] (4.4.2) Apply the feedback results to the optimization of the system and the adjustment of teaching content. The specific implementation is as follows:

[0298] (a) The implementation steps of system optimization based on feedback, that is, the development team holds feedback analysis meetings regularly (such as weekly) to conduct in-depth discussions and analyses on the system-related feedback collected. According to the needs and dissatisfaction of users with system functions in the feedback, a detailed function improvement plan is formulated.

[0299] (b) Implement performance optimization measures, that is, for the system performance problems mentioned in the feedback (such as lag, slow loading, etc.), the development team uses performance monitoring tools (such as New Relic, AppDynamics, etc.) to conduct a comprehensive performance detection and analysis of the system. According to the analysis results, corresponding optimization measures are taken, such as increasing server memory and bandwidth resources, optimizing database query statements and index design, and optimizing algorithms and refactoring key code segments, etc., to ensure that users can use the learning system smoothly.

[0300] (c) Iteratively optimize the user experience design, that is, combining the feedback from users on aspects such as system interface design and interaction operations, the design team conducts iterative work on optimizing the user experience design.

[0301] Through the above specific refinement of the feedback result presentation and application unit, the value of feedback information can be fully utilized, promoting the continuous improvement of the learning system and teaching quality, and enhancing the satisfaction and learning experience of teachers and students.

[0302] (5) The functions of the multi-platform support module, such as Figure 6 as shown:

[0303] (5.1) Cross-platform compatibility unit

[0304] (5.1.1) The multi-platform support module ensures that the system can run stably on a variety of mainstream operating systems, including Windows, Mac OS, Linux, iOS, and Android, etc. The specific implementation is as follows:

[0305] (a) A comprehensive optimization strategy has been adopted for the Windows system. In terms of software ecosystem utilization, the interfaces of various commonly used office software, tool software, and multimedia software are deeply integrated to achieve seamless data interaction and function collaboration between the system and these software.

[0306] For the two major mobile operating systems, iOS and Android, the application development specifications and design guidelines of their respective platforms are strictly followed. In terms of interface design, layouts, icons, and font styles that conform to the native style of the platform are adopted to ensure that users can feel familiar and friendly when first contacting the system. In terms of function implementation, various native APIs provided by the system are fully utilized.

[0307] For Mac OS, relevant functions such as graphics processing and audio - video editing of the system are optimized specifically. For example, in the display of teaching content, the excellent color management and high - resolution display support of Mac OS are utilized. At the same time, the operation logic of the system follows the user habits of Mac OS.

[0308] Linux systems are known for their high degree of customizability and stability. Comprehensive compatibility tests and adaptations are carried out for different Linux distributions (such as Ubuntu, Fedora, CentOS, etc.). In terms of software installation and update, common package management tools for each distribution (such as apt, yum, etc.) are used to ensure that the dependent software of the system can be installed and updated smoothly, avoiding system failures caused by software version incompatibilities. At the same time, aiming at the wide application of Linux systems on the server side, the network performance and security performance of the system are optimized to ensure stable operation in scenarios such as online teaching and online learning platforms, providing a safe and reliable learning environment for users.

[0309] (5.1.2) Support multiple types of devices, from traditional desktop computers, laptops, to mobile devices such as tablets and smartphones. For desktop devices, make full use of the advantages of large screens and high - performance hardware to present richer and more detailed teaching content, such as high - quality multimedia courseware, complex knowledge graph displays, etc. For mobile devices, considering their portability and touch - operation characteristics, optimize the interface design and interaction methods to facilitate students to learn anytime and anywhere, such as realizing convenient courseware browsing and voice interaction functions through touch operations.

[0310] (5.2) Unified user experience. (5.2.1) Maintain a unified interface design style on different platforms and devices. The specific implementation methods are as follows:

[0311] (a) Similarity in overall layout, that is, the teaching courseware display interface maintains a similar layout structure on different devices. This similarity enables users to quickly find the information they need without having to adapt to a new layout when switching devices.

[0312] (b) Consistency of interactive buttons, that is, the interactive buttons on all platforms are consistent in position and function. No matter which device the user uses, they can interact based on their existing operation habits, avoiding confusion caused by device differences.

[0313] (c) Unification of visual elements, that is, unify visual elements such as color schemes, font selections, and icon designs; ensure clear display on various devices; adopt standardized icons so that users can quickly identify their functions. These unified visual elements help to strengthen the user's overall perception of the system and enhance the coherence of the user experience.

[0314] (5.2.2) Ensure that the interaction logic of the system is the same on all platforms. The specific implementation is as follows:

[0315] (a) Ensuring that the interaction logic of the system is the same on all platforms is the key to achieving a seamless cross-platform experience. That is, whether the user operates a desktop device via a mouse and keyboard or a mobile device via touch, the same operation should receive the same feedback.

[0316] (b) Ensure the consistency of operations and feedback, that is, common operations should receive consistent responses on any platform. (c) The universality of cross-platform interaction functions, that is, the core interaction functions of the system should be universal and perform consistently on different platforms.

[0317] (c) The unity of error handling and prompts, that is, when the user makes an error during an operation, the error prompt messages and handling methods on different platforms should be consistent.

[0318] (5.3) Data Synchronization and Sharing Unit

[0319] (5.3.1) Implement data synchronization for users across different platforms. The specific implementation is as follows:

[0320] (a) Assign a unique identifier to each user. Regardless of the device the user uses to log in to the system, authentication is performed using this identifier. This identifier becomes the key link for data synchronization, tightly connecting the user's operations and data on different platforms.

[0321] When the user registers or logs in, the system records the device information used by the user, including device type (desktop computer, tablet, smartphone, etc.), device model, and operating system version, etc., for better data management and synchronization.

[0322] (b) When a teacher or student creates or modifies information such as teaching plans and learning progress on a desktop computer, the system immediately captures these operations. The data generated by these operations is marked with the user identifier and operation timestamp, and then sent to the server via a network request. After receiving the data, the server validates and organizes the data to ensure its accuracy and integrity. Then, based on the device information associated with the user, the server pushes these updated data to other devices used by the user (such as tablets or smartphones). On the mobile device side, the system listens for server push messages in real time. Once new data is received, it immediately updates the locally stored data and displays the latest information on the interface, allowing the user to see the results of their operations on the desktop computer in a timely manner.

[0323] (c) When the user operates on the mobile device (such as completing a foreign language vocabulary exercise), the device will also mark the operation data (such as exercise results, answering time, etc.) with the user identifier and operation timestamp, and then send it to the server through the mobile network. After the server processes and validates these data, it will synchronize the updated data to other devices associated with the user, including the desktop computer. On the desktop computer side, the system will detect the data update and automatically update the local learning records to ensure that the learning data seen by the user on different devices is consistent.

[0324] (d) During the data synchronization process, the system processes data based on the principle of timestamp priority. When the server receives conflicting data from different devices, it will compare the timestamps of these data, update based on the data with the latest timestamp, and synchronize this result to all relevant devices. At the same time, the system will record the situation of data conflicts for administrators or users to view and analyze to ensure the accuracy and consistency of the data.

[0325] (e) The system also supports the offline data synchronization function. When the user operates on information such as teaching plans and learning progress in the offline state, the device will temporarily store these operations in the local database. Once the device reconnects to the network, the system will automatically detect the unsynchronized data in the local database and send it to the server for synchronization. After receiving these data, the server will process them according to the normal data synchronization process to ensure that the data of offline operations can also be updated to other devices in a timely manner.

[0326] (5.3.2) Users on different platforms can share teaching resources. The specific implementation is as follows:

[0327] (a) When the teacher uploads resources such as new courseware and teaching materials on the desktop computer, the system will check and verify the format of the uploaded files to ensure the integrity and availability of the files. The uploaded teaching resources will be stored in the file system of the server and associated with the teacher's user account. At the same time, the system will generate a unique identifier for each teaching resource for resource access and management on different platforms.

[0328] (b) Students on all supported platforms (including desktop computers, tablets, and smartphones) can browse and search for teaching resources uploaded by teachers through the system interface. Students can choose to download the corresponding teaching resources for learning according to their own needs. During the download process, the system will automatically provide students with a suitable version of the teaching resources based on the device type and platform characteristics used by the students. For mobile devices with limited storage space and bandwidth, the system will automatically compress the teaching resources to generate an optimized version that can still ensure the teaching effect for students to download. This not only meets the learning needs of students on mobile devices but also avoids problems such as slow or failed downloads due to large resource sizes.

[0329] (c) For high-resolution multimedia courseware, such as teaching materials containing high-definition pictures, videos, and animations, when providing resources to mobile devices, the system will process them using advanced compression algorithms. These algorithms will minimize the file size while ensuring the integrity of the teaching content and visual effects.

[0330] (d) In addition to compressing the teaching resources, the system will also adapt the file formats of the teaching resources according to the device characteristics and software support of different platforms. For example, on mobile devices, some file formats may not be directly openable or playable, and the system will convert these files into formats supported by mobile devices to ensure that students can smoothly learn the teaching resources.

[0331] (e) To ensure the update and consistency of teaching resources, the system conducts version management of teaching resources. When a teacher modifies or updates the uploaded teaching resources, the system will generate a new version number for the new resources and notify all students who have downloaded the resources of this update. When students log in to the system next time, they will receive a prompt for resource updates, and they can choose whether to download the latest version of the teaching resources to obtain the latest teaching content and information.

[0332] (5.4) Performance Optimization and Adaptation Unit

[0333] (5.4.1) Considering that different platforms may be in different network environments, the multi-platform support module optimizes the network conditions. The specific implementation is as follows:

[0334] (a) Overall Logic Overview

[0335] It is divided into two key steps: network environment detection and loading corresponding content according to the detection results. First, a mechanism is needed to detect the network environment where the current device is located and determine whether it is a high-speed Wi-Fi environment, or an environment with poor network signal or using mobile data. Then, according to the detection results, choose to load high-quality multimedia content (in a high-speed Wi-Fi environment), or load lightweight content such as text (in the case of poor network signal or using mobile data).

[0336] (b) Implementation methods of network environment detection on different platforms

[0337] In a Web application, the navigator.connection API can be used to detect network connection information. By listening to the change event of the connection object, the change of the network status can be obtained in real time. For example, when the value of connection.effectiveType is 'wifi', it means that the current is in a Wi-Fi network environment; when the value is'slow-2g', '3g' or '4g', it means that it is in a mobile data network environment, and the network speed can be further judged according to different values.

[0338] In mobile application development (taking Android and iOS as examples):

[0339] Android uses the ConnectivityManager class to obtain network connection information. The instance of ConnectivityManager is obtained through the getSystemService(Context.CONNECTIVITY_SERVICE) method, and then the getActiveNetworkInfo() method is called to obtain the current active network information. By judging the type of network information (TYPE_WIFI or TYPE_MOBILE), it can be determined whether the device is connected to a Wi-Fi or mobile data network.

[0340] iOS uses the NWPathMonitor class to monitor the network path status. Create an NWPathMonitor instance and set its pathUpdateHandler closure. In the closure, judge whether the network is connected and whether the connection type is Wi-Fi or cellular network (mobile data) by checking the status of the path and the usesInterfaceType method.

[0341] (c) Loading content according to the network environment

[0342] When the device is detected to be in a high-speed Wi-Fi environment, a dedicated function (such as loadHighQualityContent) is called to load high-quality multimedia content. The logic for obtaining high-definition pictures, audio, video, etc. from the server is implemented inside this function. When the device is detected to be in an environment with poor network signal or using mobile data, another function (such as loadLightweightContent) is called to load lightweight content. This function is mainly responsible for obtaining lightweight content such as text from the server, or reducing the quality of multimedia content to reduce data traffic. For example, for pictures, a low-resolution version can be obtained, and for videos, the bitrate can be reduced, etc. Similarly, a network request library is also needed to interact with the server to obtain the corresponding lightweight content.

[0343] (d) Example process display

[0344] When the application starts, the network environment detection function is executed first. The detection function obtains the current network environment information according to the implementation methods of different platforms. According to the detection results, if it is a high-speed Wi-Fi environment, the loadHighQualityContent function is called to load high-quality multimedia content; if it is an environment with poor network signal or mobile data, the loadLightweightContent function is called to load lightweight content.

[0345] (5.4.2) Performance optimization is carried out according to the hardware performance differences of different platform devices. The specific implementation is as follows:

[0346] (a) Detect the hardware performance of the device

[0347] (a1) Desktop computer detection: When the application starts, the system obtains the CPU and GPU related information of the computer through the interfaces provided by the operating system or a dedicated hardware detection library. (a2) Mobile device detection: For mobile devices, the system uses the APIs provided by the device operating system to obtain hardware information.

[0348] (b) Performance optimization strategies for different devices

[0349] (b1) High-performance desktop computers:

[0350] When content such as complex 3D foreign language grammar structure models needs to be displayed, the system uses the powerful GPU computing power of the desktop computer for graphics rendering. Advanced graphics rendering technologies such as ray tracing and real-time global illumination are adopted to achieve a more realistic 3D model display effect. At the same time, multi-threading technology is used to give full play to the multi-core advantages of the CPU to parallel process the data of the 3D model and accelerate the generation and rendering process of the model.

[0351] When processing a large amount of data related to foreign language teaching, such as large-scale vocabulary libraries, grammar rule libraries, etc., the system utilizes the powerful computing capabilities of the CPU to perform complex data processing tasks. It adopts efficient algorithms and data structures, such as parallel sorting algorithms, hash tables, etc., to improve the query and processing efficiency of data. At the same time, the GPU is used for some specific data calculation tasks, such as matrix operations, etc., to further accelerate the data processing process.

[0352] (b2) Mobile devices with relatively limited hardware resources:

[0353] For mobile devices, the system adopts lightweight algorithms to process tasks related to foreign language teaching. For example, when performing foreign language speech recognition, simplified acoustic models and language models are adopted to reduce the computational load and memory occupancy. In terms of text processing, simple and efficient string matching algorithms are used to avoid using complex regular expressions and other operations with large overheads.

[0354] Optimize the application code on mobile devices to reduce unnecessary code logic and object creation. Adopt memory management techniques, such as object pooling techniques, to reuse the created objects, reduce the number of memory allocations and releases, and thus reduce memory occupancy. At the same time, perform performance analysis on the code, identify performance bottlenecks and conduct targeted optimizations, such as reducing unnecessary calculations in loops, optimizing function calls, etc.

[0355] While adopting lightweight algorithms and optimizing the code structure, ensure the integrity of the foreign language teaching functions. Evaluate each teaching function to ensure that it can operate normally on mobile devices and important functions will not be lost due to performance optimization.

[0356] (c) Implement dynamic adjustment

[0357] To better adapt to the operating conditions of different devices, the system can also implement a dynamic adjustment function. During the operation of the device, the system continuously monitors the usage of hardware resources. When it is found that the device performance has a bottleneck, the performance optimization strategy is automatically adjusted.

[0358] It should be noted that the above specific embodiments are exemplary. Those skilled in the art can come up with various solutions inspired by the disclosed content of the present invention, and these solutions also fall within the scope of the disclosure of the present invention and within the protection scope of the present invention. Those skilled in the art should understand that the specification and drawings of the present invention are illustrative and do not constitute a limitation on the claims. The protection scope of the present invention is defined by the claims and their equivalents. The specification of the present invention contains multiple inventive concepts. Expressions such as "preferably", "according to a preferred embodiment", or "optionally" all indicate that the corresponding paragraphs disclose an independent inventive concept. The applicant reserves the right to file divisional applications according to each inventive concept.

Claims

1. An AIGC empowerment assistance system for second foreign language teaching in colleges, characterized by: Includes the following modules: (1) Intelligent speech recognition and user demand analysis module: including intelligent speech acquisition device, speech preprocessing unit, speech feature extraction unit, speech recognition and conversion unit, user demand analysis unit, intention recognition subunit and demand association and integration subunit; Used to accurately capture foreign language pronunciation and convert it into text, and accurately analyze user needs; (2) AIGC content generation module: including knowledge reserve and data integration unit, content generation strategy unit, generation algorithm and model application unit, and content update and evolution unit; It is used to collect data from multiple sources, integrate and use advanced models to generate personalized teaching content in various forms, and can be updated and evolved; (3) Learning progress management and evaluation module: including learning data collection unit, learning progress analysis unit, personalized learning plan adjustment unit and learning effect evaluation and feedback unit; Used to comprehensively collect learning data, analyze progress, adjust personalized learning plans, and evaluate and provide feedback on learning effects; (4) Real-time interaction and feedback module: including real-time interaction functional unit, feedback collection functional unit, feedback analysis and processing unit, and feedback result presentation and application unit; Support multiple interactive channels, collect feedback from multiple aspects, analyze and process, and present application results; (5) Multi-platform support module: including cross-platform compatibility unit, unified user experience unit, data synchronization and sharing unit, and performance optimization and adaptation unit; ensuring the stable operation of the system on multiple operating systems and devices, unifying the user experience, and realizing data synchronization and sharing and performance optimization and adaptation.

2. According to claim 1, an AIGC empowerment assistance system for second foreign language teaching in colleges and universities is characterized by: The intelligent speech recognition and user demand analysis module includes an intelligent speech acquisition device, a speech preprocessing unit, a speech feature extraction unit, a speech recognition and conversion unit, a user demand analysis unit, an intention recognition subunit and a demand association and integration subunit; specifically: (1.1) Intelligent voice acquisition device, with built-in circular microphone array, integrated environmental noise adaptive cancellation chip and voiceprint recognition sensor; the shell of the device is made of acoustic optimization material with honeycomb sound absorption structure, and the top is embedded with an adjustable angle laser projection module for real-time display of voice recognition status, supporting multiple voice input formats and adapting to different voice environments; (1.2) Speech preprocessing unit, which uses digital signal processing technology to reduce noise and enhance the collected speech signals; (1.3) a speech feature extraction unit, which extracts speech features including but not limited to Mel frequency cepstral coefficients MFCC and linear prediction coefficients LPC from the preprocessed speech signal by using an acoustic model and phonetics principles, and performs normalization processing; (1.4) Speech recognition and conversion unit, which matches and recognizes the extracted speech feature vectors with the pre-trained model based on a large-scale foreign language speech corpus and a deep learning algorithm, and converts them into text form, while accurately distinguishing homophones based on the context; (1.5) User demand analysis unit, including: semantic understanding sub-unit, which uses foreign language grammar analysis and lexical semantic analysis technology to perform natural language processing on the converted text content, and understands the meaning of the text in combination with foreign language habits and second foreign language teaching scenarios; (1.6) Intent recognition subunit, which trains machine learning algorithms to obtain intent classification models by organizing and annotating user speech expression data in second language teaching scenarios, analyzes vocabulary, grammatical structure, and semantic information of text content after semantic understanding to identify intent, and has the functions of processing fuzzy expressions and continuous learning optimization; (1.7) The demand association and integration sub-unit analyzes the association between multiple user voice inputs and integrates the needs of different users; the demand feedback and update sub-unit adjusts the analysis results according to changes in user needs and provides feedback to other modules.

3. According to claim 1, the AIGC empowerment assistance system for second foreign language teaching in colleges is characterized by: The AIGC content generation module includes a knowledge reserve and data integration unit, a content generation strategy unit, a generation algorithm and model application unit, and a content update and evolution unit, specifically: (2.1) Knowledge reserve and data integration unit, which is used to collect information related to second foreign language teaching from data sources including authoritative foreign language textbooks, academic papers, foreign language knowledge bases, online foreign language learning platform materials, and foreign language culture introduction literature; clean, classify and annotate the collected data to construct a second foreign language teaching knowledge map; (2.2) a content generation strategy unit, used to determine the content generation direction based on the information of the intelligent speech recognition and user demand analysis modules; Generate various forms of teaching content to meet different teaching scenarios and user preferences; Realize personalized content generation; (2.3) Generative algorithm and model application unit, which is used to pre-train advanced generative deep learning models on large-scale foreign language corpora and fine-tune them using data specific to the second foreign language teaching field; set up a quality assessment mechanism to evaluate the generated content in real time, and adjust or regenerate the model if it does not meet the quality standards; (2.4) Content update and evolution unit, used to receive user and teacher feedback and update knowledge reserves to adjust and update generated content.

4. According to claim 1, the AIGC empowerment assistance system for second foreign language teaching in colleges is characterized by: The learning progress management and evaluation module includes a learning data collection unit, a learning progress analysis unit, a personalized learning plan adjustment unit, and a learning effect evaluation and feedback unit, specifically: (3.1) Learning data collection unit, used to collect user interaction data in the system, exercise and test results, and learning resource usage; (3.2) Learning progress analysis unit, which is used to quantitatively evaluate students’ mastery of various foreign language knowledge points and analyze learning trajectory, learning speed and stability; (3.3) Personalized learning plan adjustment unit, used to generate targeted learning suggestions and dynamically adjust the learning plan based on the learning progress analysis results; (3.4) Learning effect evaluation and feedback unit is used to regularly generate comprehensive evaluation reports on students’ learning effects, provide information for teachers and students, and help teachers conduct teaching interventions and students conduct self-evaluation.

5. According to claim 1, the AIGC empowerment assistance system for second foreign language teaching in colleges is characterized by: The real-time interaction and feedback module includes a real-time interaction functional unit, a feedback collection functional unit, a feedback analysis and processing unit, and a feedback result presentation and application unit, specifically: (4.1) Real-time interaction function unit, used to support voice interaction and text interaction; real-time push messages; Group users and manage group information to achieve real-time interaction within the group; (4.2) Feedback collection functional unit, used to collect user experience feedback, learning effect feedback and interaction quality feedback during the use of the system; (4.3) Feedback analysis and processing unit, used to analyze the sentiment of feedback content, classify and summarize issues, and propose targeted processing measures and improvement suggestions; (4.4) Feedback result presentation and application unit, used to present the feedback analysis results to teachers and students in a visual manner, and apply them to system optimization and teaching content adjustment.

6. The AIGC empowerment assistance system for second foreign language teaching in colleges according to claim 1 is characterized in that: The multi-platform support module includes a cross-platform compatibility unit, a unified user experience unit, a data synchronization and sharing unit, and a performance optimization and adaptation unit, specifically: (5.1) Cross-platform compatibility unit, which is used to ensure the stable operation of the system on multiple mainstream operating systems and various types of devices, and to optimize and adapt the system to the characteristics of different operating systems and devices; (5.2) Unified user experience unit, used to maintain a unified interface design style and interaction logic on different platforms and devices; (5.3) Data synchronization and sharing unit, used to synchronize data between users on different platforms and share teaching resources between users on different platforms, and optimize teaching resources according to platform characteristics; (5.4) Performance optimization and adaptation unit, used to optimize performance according to the network environment and hardware performance differences of different platforms.

7. A method for implementing the AIGC empowerment assistance system for second foreign language teaching in colleges according to any one of claims 1 to 6, characterized in that: The implementation method includes the following steps: (1) Intelligent speech recognition and user needs analysis (1.1) Voice collection: Equipped with high-precision audio collection components or interfaces to receive voice input signals from a variety of devices; (1.2) Speech preprocessing: noise reduction processing is performed on the collected speech signals, specifically: using advanced digital signal processing technology to identify and reduce environmental noise, equipment noise and other interference factors; distinguishing speech and noise frequency bands through spectrum analysis, and suppressing the noise frequency band in a targeted manner; performing speech enhancement operations to optimize the quality of speech, especially for speech input with special pronunciation or accent, by adjusting the volume, pitch and timbre parameters of the speech, so that the subsequent recognition process is more accurate; (1.3) Speech feature extraction: using acoustic models and phonetics principles, extracting key speech features from preprocessed speech signals; converting speech information into computer-recognizable feature vectors by analyzing speech signals in the frequency domain and time domain; normalizing the extracted speech features; the speech features include but are not limited to any one of Mel-frequency cepstral coefficients MFCC and linear prediction coefficients LPC; (1.4) Speech recognition and conversion: Based on a large-scale foreign language speech corpus and advanced deep learning algorithms, the extracted speech feature vectors are matched and recognized with the pre-trained model; it can accurately recognize foreign language kana, vocabulary, phrases and sentences, including different intonations, voices and dialect variants; it converts the recognized speech content into text form, providing a data basis for subsequent user demand analysis and teaching courseware generation; and in the conversion process, it accurately distinguishes homophones and heteronyms, and selects the most reasonable vocabulary based on context information; (1.5) Semantic understanding: Perform natural language processing (NLP) on the text content converted by speech recognition; use foreign language grammar analysis and lexical semantic analysis technology to analyze the structure and meaning of the text; combine foreign language language habits and the specific scenarios of second language teaching to distinguish whether it is about the need for grammar explanation, vocabulary practice or text reading guidance, providing support for accurate needs analysis; (1.6) Intent Identification: The system uses advanced machine learning algorithms to regularly update and optimize the intent classification model using new data to adapt to changing user expression habits and emerging intent types; (1.7) Demand association and integration: Analyze multiple voice inputs of users over a period of time and identify the associations between different demands; integrate the demands of different users and coordinate the demands of all parties according to the role characteristics and teaching process; the system can coordinate several demands and optimize the auxiliary functions of the courseware; (1.8) Demand feedback and update: Dynamically adjust the analysis results according to changes in user needs. When the user's understanding of a certain knowledge point changes during the learning process, the system can capture new demand signals in a timely manner, update the demand analysis conclusions, and provide a basis for real-time adjustment of teaching courseware; feedback the user demand analysis results to the system's courseware generation module and AIGC interaction module, so that the entire teaching courseware auxiliary system can work together; (2) AIGC content generation (2.1) Knowledge reserve and data integration: Collect information related to second language teaching from data sources, clean, classify and annotate the collected data, and remove noise data; classify the data according to the grammatical knowledge system, vocabulary difficulty level, and teaching scenario to provide an orderly material library for subsequent content generation; at the same time, annotate the key information in the data to facilitate rapid retrieval and application; construct a second language teaching knowledge map based on the sorted data, and relate foreign language grammar, vocabulary, examples, and cultural background to each other to form a structured knowledge network; (2.2) Content generation strategy: Determine the direction of content generation based on the information from the intelligent speech recognition and user demand analysis modules; (2.3) Generative algorithms and model applications: Generative deep learning models are used, i.e., language models based on the Transformer architecture or variational autoencoders (VAEs); natural and fluent foreign language teaching content is generated based on the input prompt information; specifically, the model is pre-trained on a large-scale foreign language corpus to learn the language patterns, grammatical rules, and semantic relationships of the foreign language; based on the pre-training, the model is adjusted using specific data in the field of second foreign language teaching; the model parameters are adjusted by inputting the collected and organized teaching-related data into the model to make it more suitable for the task of generating second foreign language teaching content; at the same time, optimization techniques such as gradient clipping or learning rate adjustment are used to improve the quality and stability of the content generated by the model; in the process of content generation, a quality assessment mechanism is set up to evaluate the generated content in real time using language model evaluation indicators and specific evaluation rules based on foreign language teaching standards; if the generated content does not meet the quality standards, the model is adjusted or regenerated; (2.4) Content update and evolution: Receive feedback from users, including evaluation of generated content, modification suggestions, and new requirements; AIGC will adjust and update generated content in a timely manner based on the feedback; (3) Learning progress management and evaluation (3.1) Learning data collection: Comprehensively collect various interactive data of users in the system, including the voice content recorded by the intelligent voice recognition module and the operation records on the courseware interface; (3.2) Learning progress analysis: Based on the collected data, quantitatively evaluate the students’ mastery of various foreign language knowledge points; construct the students’ learning trajectory and analyze their investment in foreign language learning and the order of knowledge acquisition at different time points; calculate the students’ learning speed, that is, the amount of new knowledge mastered per unit time; and judge the students’ learning status by observing the fluctuations in practice and test results; (3.3) Personalized learning plan adjustment: Generate targeted learning suggestions for each student based on the results of learning progress analysis; dynamically adjust the student's learning plan to ensure that it matches the learning progress; (3.4) Learning effect evaluation and feedback: Regularly generate comprehensive evaluation reports on students’ learning effects to show teachers and students the learning outcomes; the report content includes the mastery of each knowledge point, the comparison between learning progress and plan, the advantages and disadvantages in the learning process, and other analysis; it is presented in a combination of visual charts and detailed text descriptions to facilitate teachers to carry out targeted teaching interventions; teachers can understand the overall learning level and individual differences of the class based on the evaluation report and adjust teaching strategies; help students to self-evaluate their learning progress and level; (4) Real-time interaction and feedback (4.1) Real-time interactive functions: including voice interaction and text interaction. The voice interaction uses intelligent voice recognition technology to enable teachers and students to communicate with the system. The text interaction is suitable for inputting long questions or detailed feedback content. During the teaching process, the instructions issued by the teacher from the system can be pushed to all participating students in real time, and the questions or answers of the students can also be quickly displayed on the interactive interface of the relevant personnel. The system can also group students and teachers according to teaching needs. (4.2) Feedback collection function: Collect feedback from teachers and students during the use of the system, including evaluation of the quality of courseware content, application of teaching methods, and ease of system operation. During real-time interaction, collect feedback on the quality of interaction for subsequent optimization of the system's interactive functions. (4.3) Feedback analysis and processing: Conduct sentiment analysis on the collected feedback content to determine whether the user's attitude is positive, negative or neutral; use natural language processing technology to identify the emotional keywords and tone in the feedback text, as well as the teacher and student satisfaction with the system and teaching content; the system will classify and summarize the problems in the feedback content and provide a basis for targeted improvements; based on the results of the feedback analysis, propose targeted processing measures and improvement suggestions; at the same time, feedback the improvement suggestions to the relevant development and teaching teams to optimize the system and teaching content; (4.4) Presentation and application of feedback results: Present the results of feedback analysis to teachers and students in a visual way; present the feedback of the class as a whole and individual students to teachers to help them adjust their teaching strategies; apply the feedback results to system optimization and teaching content adjustment; the development team improves the functions and performance of the system based on the feedback; the teaching team adjusts the courseware content, teaching methods and teaching progress based on the feedback, and can modify or redesign some courseware content based on the feedback; (5)Multi-platform support (5.1) Cross-platform compatibility: The multi-platform support module ensures that the system can run stably on a variety of mainstream operating systems, including but not limited to Windows, Mac OS, Linux, iOS and Android; optimizes and adapts to the characteristics and technical architecture of different operating systems; supports a variety of devices, including but not limited to desktop computers, laptops, tablets and smartphones and other mobile devices; (5.2) Unified user experience: Maintain a unified interface design style across different platforms and devices to ensure that the system’s interaction logic is the same across platforms and that users receive the same feedback when performing the same operation; (5.3) Data synchronization and sharing: Realize data synchronization between users on different platforms; when teachers or students create or modify teaching plans and learning progress information on desktop computers, the data can be synchronized in real time to other devices used by users; conversely, the operation data on mobile devices can also be synchronized back to the server and updated to other related devices; at the same time, the system will optimize teaching resources according to the characteristics of different platforms; (5.4) Performance optimization and adaptation: Considering that different platforms may be in different network environments, multi-platform support requires optimization of network conditions; the system automatically adjusts the content loading strategy under different signal network conditions to ensure the operation of the basic functions of the system; performance optimization is performed based on the hardware performance differences of different platform devices; for high-performance desktop computers, the system can fully utilize the computing power of its CPU and GPU to achieve complex graphics rendering and data processing; for mobile devices with limited hardware resources, the system uses lightweight algorithms and optimized code structures to ensure the smooth operation of the system and the integrity of foreign language teaching functions.

8. The method for implementing AIGC-enabled automated assistance for second foreign language teaching in colleges according to claim 7 is characterized in that: The step (1.6) is specifically as follows: through large-scale teaching data, including the instructions of teachers in the teaching process, students' learning problems and the organization and annotation of requests for teaching resources, a rich training material is provided for the machine learning algorithm; through interactive feedback with users, the model is further improved; and advanced machine learning algorithms are adopted, including but not limited to support vector machines SVM, naive Bayes or convolutional neural networks CNN, recurrent neural networks RNN and their long short-term memory networks LSTM in deep learning, to learn the complex mapping relationship between different voice expressions and user intentions from the annotated data; when receiving the text content after semantic understanding, the system inputs it into the trained intention classification model; the model will analyze the multi-faceted features according to the vocabulary, grammatical structure and semantic information of the text, and can accurately identify the demand for the application of the grammar in sentence construction; the intention recognition function has a continuous learning mechanism. When the model makes an error in recognizing a certain intention, the system will record and analyze the cause and adjust the model parameters.

9. The method for implementing AIGC-enabled automated assistance for second foreign language teaching in colleges according to claim 7 is characterized in that: In the step (2.1), information related to second foreign language teaching is collected from data sources, specifically including authoritative foreign language textbooks, academic papers, foreign language knowledge bases, online foreign language learning platform materials and foreign language culture introduction documents; the collected data includes knowledge of foreign language grammar, vocabulary, listening, speaking, reading and writing, as well as content related to teaching methods and curriculum design.

10. The method for implementing AIGC-enabled automated assistance for second foreign language teaching in colleges according to claim 7, characterized in that: In the step (2.2), the direction of content generation is determined based on the information transmitted by the intelligent speech recognition and user demand analysis module, specifically: when the user demand is to generate courseware content about a specific grammar point, AIGC will develop around the grammar point, including grammar explanation, example sentence enumeration, and comparison with other grammars; when the user demand is oral practice, it can generate dialogue scenes and role-playing content that meet the corresponding level and generate various forms of teaching content; and generate text-based courseware and audio content, including knowledge point explanations, exercises, test questions, and pronunciation demonstrations of foreign language words and sentences and audio readings of dialogues; generate animations or multimedia presentations to more vividly display foreign language knowledge, and realize personalized content generation according to the characteristics and learning progress of different types of users; for teachers, the generated courseware content can be customized according to the syllabus and course progress, including teaching steps, guiding questions, and classroom activity suggestions; for students, targeted learning materials and special practice content are generated according to the presented learning level, learning history and weak links.

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