AI-driven personalized foreign language learning content generation platform

Abstract

The invention discloses an AI-driven personalized foreign language learning content generation platform, and belongs to the technical field of artificial intelligence. Comprising the following modules: a data center and user portrait module used for comprehensively presenting personalized learning features and demands; the language ability evaluation module predicts the learning ability in combination with historical learning data and generates a standardized ability evaluation report; the intelligent content service module preferentially recommends the matched existing content, automatically generates personalized learning resources and dynamically adjusts the content difficulty; the learning path planning module continuously optimizes a path planning algorithm through a feedback closed loop to realize long-term formulation and dynamic adjustment of a learning strategy; the real-time monitoring and diagnosis module monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes the learning effect based on data driving; and the multi-modal interaction module is used for providing scene-based adaptive interface display and ensuring interaction experience consistency and fluency among the functional modules.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and more specifically, to an AI-driven personalized foreign language learning content generation platform. Background Art

[0002] As the process of globalization continues to accelerate, the importance of foreign language learning has become increasingly prominent. Whether it is to improve workplace competitiveness or to enrich personal cultural literacy, more and more people are beginning to learn foreign languages. However, traditional foreign language learning methods often have problems such as single teaching content, rigid learning paths, and difficulty in meeting individual learning needs, resulting in unsatisfactory learning results.

[0003] In recent years, the development of artificial intelligence technology has brought new opportunities for foreign language learning. In particular, the development of natural language processing (NLP), machine learning and deep learning technologies has made personalized learning possible. AI-driven learning platforms can generate personalized learning content based on the specific needs and learning level of each learner, dynamically adjust learning paths, and provide real-time feedback and suggestions, greatly improving learning efficiency and learning results.

[0004] At present, there are some AI-based foreign language learning applications on the market, but these applications generally have the following problems: First, there is a lack of in-depth analysis of user learning behavior, making it difficult to fully understand users' learning needs; second, the accuracy of learning content generation and recommendation is not high, making it difficult to meet the personalized needs of different users; third, there is a lack of real-time monitoring and feedback mechanisms, and problems encountered by users during the learning process cannot be solved in a timely manner; fourth, the multimodal interaction experience is poor, making it difficult to provide an immersive learning experience.

[0005] To sum up, how to use AI technology to generate and dynamically adjust personalized foreign language learning content, improve the accuracy and efficiency of learning, and enhance the learning experience and participation has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] In order to overcome a series of defects in the prior art, the purpose of this application is to provide an AI-driven personalized foreign language learning content generation platform for the above problems, including the following modules:

[0007] The data center and user portrait module collects user behavior data, learning trajectories and feedback information in real time, builds a dynamically updated user learning portrait, and fully presents personalized learning characteristics and needs;

[0008] The language ability assessment module uses natural language processing technology to accurately assess the user's language ability level, combines historical learning data to predict learning ability, and generates a standardized ability assessment report;

[0009] Intelligent content service module, based on user profile and learning goals, prioritizes and recommends matching existing content, automatically generates personalized learning resources, and dynamically adjusts content difficulty to meet learning needs;

[0010] The learning path planning module formulates personalized learning paths based on user portraits, continuously optimizes the path planning algorithm through a feedback loop, and realizes the long-term formulation and dynamic adjustment of learning strategies;

[0011] Real-time monitoring and diagnosis module: monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes learning effects and improves learning efficiency based on data-driven;

[0012] The multimodal interaction module supports input modes including voice, text, and images, optimizes the interaction experience for different terminal devices, provides scenario-based adaptive interface display, and ensures the consistency and smoothness of the interaction experience between various functional modules.

[0013] Furthermore, the data center and user portrait module includes the following components:

[0014] Unified data collection unit to collect users’ learning behavior data, interaction logs and feedback information in real time, including learning time, learning frequency, course selection and error records;

[0015] User behavior analysis unit, which analyzes users' long-term learning behavior patterns, participation, and learning motivation, identifies users' learning tendencies, frequent operations, and preferences, and provides data support for personalized profiling;

[0016] The learning track management unit uniformly manages the progress, path and achievement changes of users during the learning process, and provides a standardized learning track data interface for other modules;

[0017] The user portrait generation unit generates a personalized user learning portrait based on the collected data and behavior analysis, depicting the user's learning ability, preference and difficulty adaptability characteristics;

[0018] System feedback processing unit, which centrally manages all system-level user feedback data, ensures consistent processing of feedback information, and distributes processing results to relevant modules;

[0019] Data storage and management unit, which centrally manages and stores all user behavior data and learning profile information to ensure data security, reliability and efficient query;

[0020] The learning needs analysis unit predicts users’ learning needs and goals based on user portraits, and dynamically adjusts the recommendations of learning resources and learning strategies.

[0021] Furthermore, the personalized user learning profile is quantified using the following formula:profile =w1·L A +w2·P+w3·D A , where U profile Score the user's comprehensive learning profile, which represents the user's personalized learning characteristics; w1, w2, and w3 are the weights of learning ability, preference, and difficulty adaptability, respectively, satisfying w1+w2+w3=1; L A is the learning ability score, which is expressed as: Where N is the number of learning tasks that the user participates in; S i The user score for the i-th task; M i The full score of the ith task; C i is the challenge coefficient of the ith task, calculated based on the difficulty of the task and the user's performance; N is the number of tasks used to calculate the learning ability score, indicating the total number of learning tasks in which the user participates; Among them, T k is the learning time of the user on the kth type of learning resources; T total is the total learning time of the user; F k is the attractiveness coefficient of the first k types of resources, which reflects the attractiveness of a certain type of resources to users based on the user's historical feedback; K is the number of resource categories used to calculate the learning preference score, indicating the number of different types of learning resources; Among them, D i Score the difficulty of the i-th task; A i Score the user's adaptability in the i-th task, indicating the user's ability to cope with challenges in the task. The higher the score, the more adaptable the user is to difficult tasks.

[0022] Furthermore, the language proficiency assessment module includes the following components:

[0023] The language ability assessment unit uses natural language processing technology to conduct a comprehensive assessment of the user's language ability in four aspects: listening, speaking, reading and writing;

[0024] Ability level classification unit, which divides users into ability levels according to language ability assessment results and generates a standardized ability level report;

[0025] The speech and pronunciation analysis unit uses speech recognition and speech analysis technology to evaluate the user's speaking and pronunciation skills, identify errors in speech, and provide suggestions for improvement;

[0026] The grammar and syntax analysis unit analyzes the grammatical structure in the text or speech input by the user, identifies grammatical errors, and helps users improve their language expression skills;

[0027] The vocabulary and expression ability analysis unit evaluates the user's vocabulary, expression diversity, and vocabulary accuracy to help users improve their language fluency and diversity;

[0028] The learning diagnostic report unit generates standardized language proficiency diagnostic reports, provides detailed assessment results, and helps users identify areas for improvement.

[0029] Furthermore, the intelligent content service module includes the following components:

[0030] The learning resource recommendation unit works with the learning path planning module to recommend matching learning content based on user profiles and learning paths;

[0031] Personalized content generation unit, which automatically generates personalized learning resources based on user learning trajectories and feedback to meet the needs of different learning stages;

[0032] The difficulty adaptation unit receives the strategy instructions from the learning path planning module and performs difficulty adjustment of the learning content;

[0033] The learning content optimization unit continuously optimizes content recommendation strategies and learning resource quality based on user feedback and learning effect data to ensure that the recommended learning content is consistent with the user's long-term learning goals;

[0034] The content diversity control unit ensures that the recommended learning content is diverse in form and content to increase interest and participation in learning.

[0035] Furthermore, the following formula is used to ensure that the recommended learning content is consistent with the user's long-term learning goals: Optimization goal = Among them, R(C t ,G u ) is recommended content C t With the user's long-term learning goal G u The matching degree of L u (t) is the learning effect score of the user at time t, representing the learning effect of the recommended content; F u (t) is the user feedback score, which reflects the user's interest and feedback satisfaction; D(C t ) is the diversity score of the recommended content; α is the weight coefficient used to adjust the consistency between content recommendation and the user's long-term learning goal; β is the weight coefficient used to adjust the importance of learning effect in optimization; γ is the feedback weight coefficient used to adjust the user feedback F u (t) is the impact on the optimization target; δ is the weight coefficient of diversity control; T is the time span of the optimization process, indicating the cycle or step of optimization.

[0036] Furthermore, the learning path planning module includes the following components:

[0037] Learning goal management unit, which uniformly manages users' long-term and short-term learning goals and provides goal reference and content docking services for all modules;

[0038] The path planning engine unit develops a preliminary personalized learning path based on the user's learning goals, ability assessment, and needs;

[0039] Strategy adjustment unit, responsible for dynamic optimization of learning path, difficulty and learning method;

[0040] The resource planning unit provides resource requirements to the intelligent content service module based on the learning path to ensure the coordination of content recommendations and learning progress;

[0041] The path evaluation unit evaluates the effectiveness of the learning path based on the data from the unified feedback processing unit and guides future path optimization.

[0042] Furthermore, the real-time monitoring and diagnosis module includes the following components:

[0043] The error detection and diagnosis unit analyzes the types of errors made by users during the learning process in real time, provides accurate error diagnosis, and makes targeted improvement suggestions;

[0044] The learning effect evaluation unit evaluates the user's learning effect in real time and determines whether the user has achieved the expected learning goal by analyzing the error pattern, answering time and learning task completion;

[0045] The learning feedback generation unit generates instant feedback based on the user's learning progress and errors, guides the user to make improvements, and helps them overcome learning obstacles;

[0046] The execution strategy adaptation unit executes corresponding adjustment measures in the real-time learning process according to the instructions of the strategy adjustment unit;

[0047] The real-time behavior monitoring unit focuses on monitoring and analyzing real-time learning behaviors, providing instant data support for strategy adjustments.

[0048] Furthermore, the user's learning effect is expressed as: Among them, L is the user's learning effect; E is the user's error rate, ranging from 0 to 1; G is the time it takes for the user to complete the task, G ideal is the ideal time to answer the task; C is the degree of completion of the learning task, indicating the progress of task completion, with a value range of [0,1]; S is the difficulty coefficient of the task, with a value range of [0,1]; S target is the expected difficulty of the task, the ideal difficulty coefficient.

[0049] Furthermore, the multimodal interaction module includes the following components:

[0050] The input processing unit is responsible for receiving and parsing various forms of input signals to achieve a unified processing flow of speech recognition, text processing, and image analysis;

[0051] Adaptive interface unit dynamically adjusts interface layout and theme style according to different scenarios and device characteristics to ensure the best visual experience;

[0052] The interaction management unit coordinates the interaction processes between various functional modules, handles event distribution, and ensures the consistency of operation responses;

[0053] The device optimization unit adapts and optimizes the performance according to the hardware characteristics of different terminal devices to provide a smooth interactive experience;

[0054] Output synthesis unit, which integrates and generates multimodal feedback output, including visual and auditory feedback, to provide unified information presentation;

[0055] The modality control unit manages the switching and synchronization between different modes to ensure seamless connection of multiple interaction methods;

[0056] The interaction state management unit focuses on maintaining the context information and state data of the interaction interface to ensure the continuity and consistency of the interaction process.

[0057] Compared with the prior art, this application has the following beneficial effects:

[0058] This application significantly improves the accuracy and efficiency of foreign language learning through AI-driven personalized content generation and dynamic adjustment, while enhancing users' learning experience and engagement through multimodal interaction and real-time feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a schematic diagram of the structure of the AI-driven personalized foreign language learning content generation platform disclosed in the embodiments of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments.

[0061] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0062] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0063] like Figure 1 As shown in the figure, the AI-driven personalized foreign language learning content generation platform includes the following modules:

[0064] The data center and user portrait module collects user behavior data, learning trajectories and feedback information in real time, builds a dynamically updated user learning portrait, and fully presents personalized learning characteristics and needs;

[0065] The language ability assessment module uses natural language processing technology to accurately assess the user's language ability level, combines historical learning data to predict learning ability, and generates a standardized ability assessment report;

[0066] Intelligent content service module, based on user profile and learning goals, prioritizes and recommends matching existing content, automatically generates personalized learning resources, and dynamically adjusts content difficulty to meet learning needs;

[0067] The learning path planning module formulates personalized learning paths based on user portraits, continuously optimizes the path planning algorithm through a feedback loop, and realizes the long-term formulation and dynamic adjustment of learning strategies;

[0068] Real-time monitoring and diagnosis module: monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes learning effects and improves learning efficiency based on data-driven;

[0069] The multimodal interaction module supports input modes including voice, text, and images, optimizes the interaction experience for different terminal devices, provides scenario-based adaptive interface display, and ensures the consistency and smoothness of the interaction experience between various functional modules.

[0070] The data center and user portrait module are the core components of the AI-driven personalized foreign language learning content generation platform. It builds a dynamically updated user learning portrait by analyzing the interaction data between users and the platform, including learning time, learning frequency, test results, word memory curve, grammar mastery, etc. The portrait comprehensively presents personalized learning characteristics and needs, reflecting the user's language learning habits, weak areas, and learning preferences. As users continue to participate in learning activities, the portrait will be updated according to new data, so as to provide personalized data support for subsequent modules more accurately. Through in-depth analysis of this information, the platform can not only identify users' learning progress, difficulties, and changes in needs, but also predict their future learning goals and development potential. This module uses highly integrated data analysis technology to establish an accurate, real-time, and dynamic learning model, which enables the platform to adapt and respond to user changes at any time, and improve the pertinence and effectiveness of learning.

[0071] By analyzing the voice, text and online interaction data submitted by users, the language ability assessment module can identify the user's pronunciation accuracy, grammar mastery, vocabulary richness and other dimensions of ability. These assessments are not only based on immediate learning activities, but also combined with the user's historical learning data to dynamically predict the development trend of their language ability. For example, it can identify the type of grammatical errors through the analysis of the user's previous error patterns, evaluate their learning progress, and provide users with level-based feedback and suggestions. By generating standardized ability assessment reports, it can provide users with clear language ability feedback to help them understand their strengths and weaknesses. Furthermore, this module can work closely with the intelligent content service module to accurately recommend learning content or adjust the difficulty through comprehensive evaluation results to promote gradual learning. By continuously and accurately evaluating the user's language ability, this module provides an important basis for achieving personalized learning.

[0072] Based on user portraits and learning goals, the intelligent content service module undertakes the task of dynamically recommending and generating personalized learning resources. By deeply analyzing the user's learning behavior and needs, the platform can prioritize the content that matches the user's existing abilities, interests, and learning progress, ensuring that the recommended learning materials are highly relevant and appropriate. At the same time, the module will automatically adjust the difficulty of the content based on the user's feedback and learning progress. For example, when the user is more proficient in a certain knowledge point, more difficult content will be automatically recommended; when the user encounters difficulties, the difficulty will be automatically reduced or more relevant basic content will be recommended. The adaptive ability of this module greatly improves the learning experience, so that the learning content is always matched with the user's ability level, thus avoiding the problem of excessive challenges or insufficient learning motivation. Intelligent content services not only improve learning results, but also make the learning process smoother and more interesting, reducing anxiety and frustration in learning. Finally, by dynamically adjusting the difficulty of the content, the module enables users to find the best balance between challenge and sense of achievement, promoting the continuity and motivation of their long-term learning.

[0073] The learning path planning module is responsible for developing personalized learning paths based on user portraits, helping users to achieve orderly advancement from basic to advanced levels during the learning process. By analyzing the user's current language proficiency level, learning history, and target needs, the most suitable learning path for the user is developed, and dynamically adjusted according to the user's learning progress and feedback. The uniqueness of this module is that it can form a "feedback closed loop": as the user's learning progresses, the platform will collect learning feedback in real time and adjust the content and order of the learning path based on this feedback. For example, if a user performs well in a certain module, it can automatically recommend more challenging learning content or adjust the learning order to make the learning path more personalized. In the long run, the learning path planning module ensures that the user's learning strategy and content always meet their personalized learning needs by continuously optimizing its path planning algorithm. This module can help users avoid getting lost in a huge amount of learning resources and ensure that their learning direction is always clear and targeted.

[0074] The real-time monitoring and diagnosis module plays a vital role in the AI-driven learning platform. It can monitor and analyze the user's learning behavior in real time, provide instant feedback and accurate error diagnosis. By analyzing the user's learning trajectory, the results of each exercise, the progress of learning and other multi-dimensional data, it can timely identify the errors in learning and provide personalized correction solutions. Error diagnosis is not limited to grammatical and vocabulary errors, but also includes problems in learning strategies. For example, if it is detected that the user frequently makes the same mistake within a certain period of time, it can issue an alarm and provide remedial measures, such as adding relevant exercises, or providing different types of explanations and examples. Through this personalized real-time feedback mechanism, learning efficiency can be greatly improved, and learners can understand their shortcomings in time and improve them. In addition, based on data-driven optimization methods, strategies can be continuously adjusted during the learning process to improve learning effects, so that users can effectively master foreign language skills in the shortest time.

[0075] The multimodal interaction module greatly improves the interactive experience between the platform and users by supporting multiple input modes such as voice, text, and images. By combining the characteristics of different terminal devices (such as mobile phones, tablets, PCs, etc.), the module provides an adaptive interface display to ensure that users have a consistent and smooth experience on various devices. Users can choose different learning methods according to their personal preferences or needs, such as practicing pronunciation through voice input, or enhancing the interactivity of learning content through image recognition functions. The multimodal interaction module not only improves the fun of learning, but also optimizes the learning effect, especially in voice recognition and pronunciation correction, which can help users master foreign language pronunciation more accurately and reduce accent errors. The module intelligently identifies the user's interaction mode and preferences, and automatically adjusts the interaction method to ensure that every learner can find the most comfortable and efficient way to learn.

[0076] In summary, this AI-driven personalized foreign language learning content generation platform achieves accurate response and optimization to users' personalized needs through multiple efficient and collaborative modules. The data center and user portrait modules provide the platform with comprehensive user behavior data, and the language ability assessment module ensures that the learning needs of each learner can be accurately identified and met. The intelligent content service module and the learning path planning module help users improve their language skills in the best way by dynamically adjusting the content difficulty and learning path. The real-time monitoring and diagnosis module ensures that every link in the learning process achieves the best effect through instant feedback and correction, while the multimodal interaction module provides a smooth and rich user experience. The close combination of these modules makes the learning process not only personalized, but also efficiently and continuously drives learners to achieve their language learning goals, greatly improving the learning experience and results.

[0077] Furthermore, the data center and user portrait module includes the following components:

[0078] Unified data collection unit to collect users’ learning behavior data, interaction logs and feedback information in real time, including learning time, learning frequency, course selection and error records;

[0079] User behavior analysis unit, which analyzes users' long-term learning behavior patterns, participation, and learning motivation, identifies users' learning tendencies, frequent operations, and preferences, and provides data support for personalized profiling;

[0080] The learning track management unit uniformly manages the progress, path and achievement changes of users during the learning process, and provides a standardized learning track data interface for other modules;

[0081] The user portrait generation unit generates a personalized user learning portrait based on the collected data and behavior analysis, depicting the user's learning ability, preference and difficulty adaptability characteristics;

[0082] System feedback processing unit, which centrally manages all system-level user feedback data, ensures consistent processing of feedback information, and distributes processing results to relevant modules;

[0083] Data storage and management unit, which centrally manages and stores all user behavior data and learning profile information to ensure data security, reliability and efficient query;

[0084] The learning needs analysis unit predicts users’ learning needs and goals based on user portraits, and dynamically adjusts the recommendations of learning resources and learning strategies.

[0085] In summary, the data center and user portrait module provide solid data support and intelligent decision-making basis for the AI-driven personalized foreign language learning platform by integrating various components. The unified data collection unit ensures the timely collection of data, the user behavior analysis unit deeply explores learning habits and preferences, the learning trajectory management unit provides standardized learning data for other modules, and the user portrait generation unit realizes the core function of personalized learning experience. The system feedback processing unit and the data storage and management unit ensure the efficient processing of user feedback and the stable storage of data. The learning needs analysis unit optimizes the allocation of learning resources through accurate prediction and recommendation. The collaborative work of the entire module system enables the platform to continuously adjust the learning content and path, tailor the most suitable learning plan for each user, and greatly improve learning efficiency and user satisfaction.

[0086] Furthermore, the personalized user learning profile is quantified using the following formula: profile =w1·L A +w2·P+w3·D A , where U profileScore the user's comprehensive learning profile, which represents the user's personalized learning characteristics; w1, w2, and w3 are the weights of learning ability, preference, and difficulty adaptability, respectively, satisfying w1+w2+w3=1; L A is the learning ability score, which is expressed as: Where N is the number of learning tasks that the user participates in; S i The user score for the i-th task; M i The full score of the ith task; C i is the challenge coefficient of the ith task, calculated based on the difficulty of the task and the user's performance; N is the number of tasks used to calculate the learning ability score, indicating the total number of learning tasks in which the user participates; Among them, T k is the learning time of the user on the kth type of learning resources; T total is the total learning time of the user; F k is the attractiveness coefficient of the first k types of resources, which reflects the attractiveness of a certain type of resources to users based on the user's historical feedback; K is the number of resource categories used to calculate the learning preference score, indicating the number of different types of learning resources; Among them, D i Score the difficulty of the i-th task; A i Score the user's adaptability in the i-th task, indicating the user's ability to cope with challenges in the task. The higher the score, the more adaptable the user is to difficult tasks.

[0087] Furthermore, the language proficiency assessment module includes the following components:

[0088] The language ability assessment unit uses natural language processing technology to conduct a comprehensive assessment of the user's language ability in four aspects: listening, speaking, reading and writing;

[0089] Ability level classification unit, which divides users into ability levels according to language ability assessment results and generates a standardized ability level report;

[0090] The speech and pronunciation analysis unit uses speech recognition and speech analysis technology to evaluate the user's speaking and pronunciation skills, identify errors in speech, and provide suggestions for improvement;

[0091] The grammar and syntax analysis unit analyzes the grammatical structure in the text or speech input by the user, identifies grammatical errors, and helps users improve their language expression skills;

[0092] The vocabulary and expression ability analysis unit evaluates the user's vocabulary, expression diversity, and vocabulary accuracy to help users improve their language fluency and diversity;

[0093] The learning diagnostic report unit generates standardized language proficiency diagnostic reports, provides detailed assessment results, and helps users identify areas for improvement.

[0094] In summary, the language ability assessment module provides a comprehensive and accurate assessment tool for foreign language learning by integrating multiple advanced technologies. The language ability assessment unit uses natural language processing technology to accurately assess the user's ability in listening, speaking, reading and writing; the ability level classification unit uses standardized assessment reports to define the ability level of users and make personalized learning suggestions; the speech and pronunciation analysis unit, grammar and syntax analysis unit, and vocabulary and expression ability analysis unit provide detailed improvement plans for users' pronunciation, grammar and vocabulary use; the learning diagnosis report unit integrates all assessment results into easy-to-understand reports to help users develop more targeted learning plans. Through the implementation of this module, the foreign language learning platform can more accurately identify the user's ability level, provide personalized feedback and guidance, and thus greatly improve the user's learning effect and learning experience.

[0095] Furthermore, the intelligent content service module includes the following components:

[0096] The learning resource recommendation unit works with the learning path planning module to recommend matching learning content based on user profiles and learning paths;

[0097] Personalized content generation unit, which automatically generates personalized learning resources based on user learning trajectories and feedback to meet the needs of different learning stages;

[0098] The difficulty adaptation unit receives the strategy instructions from the learning path planning module and performs difficulty adjustment of the learning content;

[0099] The learning content optimization unit continuously optimizes content recommendation strategies and learning resource quality based on user feedback and learning effect data to ensure that the recommended learning content is consistent with the user's long-term learning goals;

[0100] The content diversity control unit ensures that the recommended learning content is diverse in form and content to increase interest and participation in learning.

[0101] In summary, the intelligent content service module provides users with personalized and dynamically adjusted learning resources through a series of sophisticated components, ensuring that the learning content not only meets the user's current needs, but can also be adjusted at any time according to the learning progress. These components include learning resource recommendations, personalized content generation, difficulty adaptation, learning content optimization, and content diversity control. Together, they enable each user to learn efficiently in an environment that is both challenging and appropriate. By utilizing user portraits and feedback information, the intelligent content service module can flexibly adapt to the different learning stages of users and provide optimization solutions based on real-time data, thereby improving learning efficiency and long-term results. In addition, the module also enhances users' interest in learning and sense of participation through the diversification of content forms, further improving the user experience of the platform.

[0102] Furthermore, the following formula is used to ensure that the recommended learning content is consistent with the user's long-term learning goals: Optimization goal = Among them, R(C t ,G u ) is recommended content C t With the user's long-term learning goal G u The matching degree of L u (t) is the learning effect score of the user at time t, representing the learning effect of the recommended content; F u (t) is the user feedback score, which reflects the user's interest and feedback satisfaction; D(C t ) is the diversity score of the recommended content; α is the weight coefficient used to adjust the consistency between content recommendation and the user's long-term learning goal; β is the weight coefficient used to adjust the importance of learning effect in optimization; γ is the feedback weight coefficient used to adjust the user feedback F u (t) is the impact on the optimization target; δ is the weight coefficient of diversity control; T is the time span of the optimization process, indicating the cycle or step of optimization.

[0103] Furthermore, the learning path planning module includes the following components:

[0104] Learning goal management unit, which uniformly manages users' long-term and short-term learning goals and provides goal reference and content docking services for all modules;

[0105] The path planning engine unit develops a preliminary personalized learning path based on the user's learning goals, ability assessment, and needs;

[0106] Strategy adjustment unit, responsible for dynamic optimization of learning path, difficulty and learning method;

[0107] The resource planning unit provides resource requirements to the intelligent content service module based on the learning path to ensure the coordination of content recommendations and learning progress;

[0108] The path evaluation unit evaluates the effectiveness of the learning path based on the data from the unified feedback processing unit and guides future path optimization.

[0109] In summary, the learning path planning module can provide users with personalized and dynamically adjusted learning paths through the collaborative work of multiple core components, and ensure that learning resources are highly consistent with goals. The learning goal management unit, path planning engine unit, strategy adjustment unit, resource planning unit and path evaluation unit work together to help users set clear learning goals, provide appropriate learning paths, adjust learning difficulty and strategies in real time, optimize learning resource allocation, and continuously evaluate and improve the effectiveness of learning paths. These components not only ensure the systematic and personalized learning process, but also can flexibly adapt and provide the best learning experience as the user's abilities change and learning needs adjust. With the support of the learning path planning module, users can study in an orderly manner under the guidance of clear goals, improve learning efficiency and ultimately achieve their language learning goals.

[0110] Furthermore, the real-time monitoring and diagnosis module includes the following components:

[0111] The error detection and diagnosis unit analyzes the types of errors made by users during the learning process in real time, provides accurate error diagnosis, and makes targeted improvement suggestions;

[0112] The learning effect evaluation unit evaluates the user's learning effect in real time and determines whether the user has achieved the expected learning goal by analyzing the error pattern, answering time and learning task completion;

[0113] The learning feedback generation unit generates instant feedback based on the user's learning progress and errors, guides the user to make improvements, and helps them overcome learning obstacles;

[0114] The execution strategy adaptation unit executes corresponding adjustment measures in the real-time learning process according to the instructions of the strategy adjustment unit;

[0115] The real-time behavior monitoring unit focuses on monitoring and analyzing real-time learning behaviors, providing instant data support for strategy adjustments.

[0116] In summary, the real-time monitoring and diagnosis module is a key component of the personalized foreign language learning platform. It works together through multiple complementary units to ensure that problems in the learning process can be discovered, diagnosed and corrected in a timely manner. The error detection and diagnosis unit provides users with accurate error feedback, the learning effect evaluation unit helps users understand their learning progress, the learning feedback generation unit helps users to continuously improve through instant feedback, the execution strategy adaptation unit adjusts the learning path and strategy as needed, and the real-time behavior monitoring unit provides real-time data support for the learning process. The collaborative work of these functional modules can provide all-round support in the learning process, thereby helping users overcome obstacles in learning, improve learning efficiency, and ensure that they can steadily move towards their goals in a constantly adjusted learning path. Through real-time monitoring and diagnosis, the learning platform can tailor the learning experience for each user, improve their language ability, and ultimately achieve better learning outcomes.

[0117] Furthermore, the user's learning effect is expressed as: Among them, L is the user's learning effect; E is the user's error rate, ranging from 0 to 1; G is the time it takes for the user to complete the task, G ideal is the ideal time to answer the task; C is the degree of completion of the learning task, indicating the progress of task completion, with a value range of [0,1]; S is the difficulty coefficient of the task, with a value range of [0,1]; S target is the expected difficulty of the task, the ideal difficulty coefficient.

[0118] Furthermore, the multimodal interaction module includes the following components:

[0119] The input processing unit is responsible for receiving and parsing various forms of input signals to achieve a unified processing flow of speech recognition, text processing, and image analysis;

[0120] Adaptive interface unit dynamically adjusts interface layout and theme style according to different scenarios and device characteristics to ensure the best visual experience;

[0121] The interaction management unit coordinates the interaction processes between various functional modules, handles event distribution, and ensures the consistency of operation responses;

[0122] The device optimization unit adapts and optimizes the performance according to the hardware characteristics of different terminal devices to provide a smooth interactive experience;

[0123] Output synthesis unit, which integrates and generates multimodal feedback output, including visual and auditory feedback, to provide unified information presentation;

[0124] The modality control unit manages the switching and synchronization between different modes to ensure seamless connection of multiple interaction methods;

[0125] The interaction state management unit focuses on maintaining the context information and state data of the interaction interface to ensure the continuity and consistency of the interaction process.

[0126] In summary, the multimodal interaction module can provide users with a flexible and convenient interactive experience by integrating multiple input and output methods such as voice, text and images. Key components such as the input processing unit, output synthesis unit, and device optimization unit work together to ensure that different input methods can be seamlessly connected and uniformly processed. The adaptive interface unit and the interactive state management unit ensure that users can experience consistent and efficient visual and operational effects in various devices and interactive processes. With the support of the modal control unit, the system can intelligently switch the interaction mode according to actual needs to enhance the user's operating experience. Overall, the multimodal interaction module not only enriches the interaction methods between users and the system, but also greatly improves the adaptability and flexibility of the system, providing users with a more intuitive and intelligent learning experience.

[0127] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. AI-driven personalized foreign language learning content generation platform, characterized by: Includes the following modules: The data center and user portrait module collects user behavior data, learning trajectories and feedback information in real time, builds a dynamically updated user learning portrait, and fully presents personalized learning characteristics and needs; The language ability assessment module uses natural language processing technology to accurately assess the user's language ability level, combines historical learning data to predict learning ability, and generates a standardized ability assessment report; Intelligent content service module, based on user profile and learning goals, prioritizes and recommends matching existing content, automatically generates personalized learning resources, and dynamically adjusts content difficulty to meet learning needs; The learning path planning module formulates personalized learning paths based on user portraits, continuously optimizes the path planning algorithm through a feedback loop, and realizes the long-term formulation and dynamic adjustment of learning strategies; Real-time monitoring and diagnosis module: monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes learning effects and improves learning efficiency based on data-driven; The multimodal interaction module supports input modes including voice, text, and images, optimizes the interaction experience for different terminal devices, provides scenario-based adaptive interface display, and ensures the consistency and smoothness of the interaction experience between various functional modules.

2. The AI-driven personalized foreign language learning content generation platform according to claim 1, characterized in that: The data center and user portrait module includes the following components: Unified data collection unit to collect users’ learning behavior data, interaction logs and feedback information in real time, including learning time, learning frequency, course selection and error records; User behavior analysis unit, which analyzes users' long-term learning behavior patterns, participation, and learning motivation, identifies users' learning tendencies, frequent operations, and preferences, and provides data support for personalized profiling; The learning track management unit uniformly manages the progress, path and achievement changes of users during the learning process, and provides a standardized learning track data interface for other modules; The user portrait generation unit generates a personalized user learning portrait based on the collected data and behavior analysis, depicting the user's learning ability, preference and difficulty adaptability characteristics; System feedback processing unit, which centrally manages all system-level user feedback data, ensures consistent processing of feedback information, and distributes processing results to relevant modules; Data storage and management unit, which centrally manages and stores all user behavior data and learning profile information to ensure data security, reliability and efficient query; The learning needs analysis unit predicts users’ learning needs and goals based on user portraits, and dynamically adjusts the recommendations of learning resources and learning strategies.

3. The AI-driven personalized foreign language learning content generation platform according to claim 2, characterized in that: The personalized user learning profile is quantified using the following formula: profile =w1·L A +w2·P+w3·D A , where U profile Score the user's comprehensive learning profile, which represents the user's personalized learning characteristics; w1, w2, and w3 are the weights of learning ability, preference, and difficulty adaptability, respectively, satisfying w1+w2+w3=1; L A is the learning ability score, which is expressed as: Where N is the number of learning tasks that the user participates in; S i The user score for the i-th task; M i The full score of the ith task; C i is the challenge coefficient of the ith task, calculated based on the difficulty of the task and the user's performance; N is the number of tasks used to calculate the learning ability score, indicating the total number of learning tasks in which the user participates; Among them, T k is the learning time of the user on the kth type of learning resources; T total is the total learning time of the user; F k is the attractiveness coefficient of the first k types of resources, which reflects the attractiveness of a certain type of resources to users based on the user's historical feedback; K is the number of resource categories used to calculate the learning preference score, indicating the number of different types of learning resources; Among them, D i Score the difficulty of the i-th task; A i Score the user's adaptability in the i-th task, indicating the user's ability to cope with challenges in the task. The higher the score, the more adaptable the user is to difficult tasks.

4. The AI-driven personalized foreign language learning content generation platform according to claim 1, characterized in that: The language proficiency assessment module includes the following components: The language ability assessment unit uses natural language processing technology to conduct a comprehensive assessment of the user's language ability in four aspects: listening, speaking, reading and writing; Ability level classification unit, which divides users into ability levels according to language ability assessment results and generates a standardized ability level report; The speech and pronunciation analysis unit uses speech recognition and speech analysis technology to evaluate the user's speaking and pronunciation skills, identify errors in speech, and provide suggestions for improvement; The grammar and syntax analysis unit analyzes the grammatical structure in the text or speech input by the user, identifies grammatical errors, and helps users improve their language expression skills; The vocabulary and expression ability analysis unit evaluates the user's vocabulary, expression diversity, and vocabulary accuracy to help users improve their language fluency and diversity; The learning diagnostic report unit generates standardized language proficiency diagnostic reports, provides detailed assessment results, and helps users identify areas for improvement.

5. The AI-driven personalized foreign language learning content generation platform according to claim 1, characterized in that: The intelligent content service module includes the following components: The learning resource recommendation unit works with the learning path planning module to recommend matching learning content based on user profiles and learning paths; Personalized content generation unit, which automatically generates personalized learning resources based on user learning trajectories and feedback to meet the needs of different learning stages; The difficulty adaptation unit receives the strategy instructions from the learning path planning module and performs difficulty adjustment of the learning content; The learning content optimization unit continuously optimizes content recommendation strategies and learning resource quality based on user feedback and learning effect data to ensure that the recommended learning content is consistent with the user's long-term learning goals; The content diversity control unit ensures that the recommended learning content is diverse in form and content to increase interest and participation in learning.

6. The AI-driven personalized foreign language learning content generation platform according to claim 5, characterized in that: The following formula is used to ensure that the recommended learning content is consistent with the user's long-term learning goals: Among them, R(C t , G u ) is recommended content C t With the user's long-term learning goal G u The matching degree of L u (t) is the learning effect score of the user at time t, representing the learning effect of the recommended content; F u (t) is the user feedback score, which reflects the user's interest and feedback satisfaction; D(C t ) is the diversity score of the recommended content; α is the weight coefficient used to adjust the consistency between content recommendation and the user's long-term learning goal; β is the weight coefficient used to adjust the importance of learning effect in optimization; γ is the feedback weight coefficient used to adjust the user feedback F u (t) is the impact on the optimization target; δ is the weight coefficient of diversity control; T is the time span of the optimization process, indicating the cycle or step of optimization.

7. The AI-driven personalized foreign language learning content generation platform according to claim 1, characterized in that: The learning path planning module includes the following components: Learning goal management unit, which uniformly manages users' long-term and short-term learning goals and provides goal reference and content docking services for all modules; The path planning engine unit develops a preliminary personalized learning path based on the user's learning goals, ability assessment, and needs; Strategy adjustment unit, responsible for dynamic optimization of learning path, difficulty and learning method; The resource planning unit provides resource requirements to the intelligent content service module based on the learning path to ensure the coordination of content recommendations and learning progress; The path evaluation unit evaluates the effectiveness of the learning path based on the data from the unified feedback processing unit and guides future path optimization.

8. The AI-driven personalized foreign language learning content generation platform according to claim 1, characterized in that: The real-time monitoring and diagnosis module includes the following components: The error detection and diagnosis unit analyzes the types of errors made by users during the learning process in real time, provides accurate error diagnosis, and makes targeted improvement suggestions; The learning effect evaluation unit evaluates the user's learning effect in real time and determines whether the user has achieved the expected learning goal by analyzing the error pattern, answering time and learning task completion; The learning feedback generation unit generates instant feedback based on the user's learning progress and errors, guides the user to make improvements, and helps them overcome learning obstacles; The execution strategy adaptation unit executes corresponding adjustment measures in the real-time learning process according to the instructions of the strategy adjustment unit; The real-time behavior monitoring unit focuses on monitoring and analyzing real-time learning behaviors, providing instant data support for strategy adjustments.

9. The AI-driven personalized foreign language learning content generation platform according to claim 8, characterized in that: The user's learning effect is expressed as: Among them, L is the user's learning effect; E is the user's error rate, ranging from 0 to 1; G is the time it takes for the user to complete the task, G ideal is the ideal time to answer the task; C is the degree of completion of the learning task, indicating the progress of task completion, with a value range of [0, 1]; S is the difficulty coefficient of the task, with a value range of [0, 1]; S target is the expected difficulty of the task, the ideal difficulty coefficient.

10. The AI-driven personalized foreign language learning content generation platform according to claim 1, characterized in that: The multimodal interaction module includes the following components: The input processing unit is responsible for receiving and parsing various forms of input signals to achieve a unified processing flow of speech recognition, text processing, and image analysis; Adaptive interface unit dynamically adjusts interface layout and theme style according to different scenarios and device characteristics to ensure the best visual experience; The interaction management unit coordinates the interaction processes between various functional modules, handles event distribution, and ensures the consistency of operation responses; The device optimization unit adapts and optimizes the performance according to the hardware characteristics of different terminal devices to provide a smooth interactive experience; Output synthesis unit, which integrates and generates multimodal feedback output, including visual and auditory feedback, to provide unified information presentation; The modality control unit manages the switching and synchronization between different modes to ensure seamless connection of multiple interaction methods; The interaction state management unit focuses on maintaining the context information and state data of the interaction interface to ensure the continuity and consistency of the interaction process.

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