Adaptive intelligent teaching content generation system for multi-modal large model reinforcement learning

Abstract

The invention discloses a self-adaptive intelligent teaching content generation system for multi-modal large model reinforcement learning, and belongs to the technical field of intelligent education. The objective of the invention is to solve the problems of one-sided perception, rigid decision and insufficient safety compatibility of a traditional teaching content generation system. The system comprises a multi-modal precise sensing layer, a multi-algorithm hybrid reinforcement learning decision engine, a multi-modal dynamic generator, a closed-loop iterative optimization layer and a safety and compatibility module, and the multi-modal precise sensing layer integrates texts, audios and videos, physiological signals and knowledge data and combines cross-modal collaborative detection and Bayesian self-calibration to obtain a multi-modal precise sensing result. And comprehensive dynamic learner characterization is generated. According to the method, the teaching content is upgraded from passive matching to active matching and from static solidification to dynamic optimization, the learner state detection accuracy and the teaching content adaptability are improved, multi-scene application of K12 education, higher education, vocational training and the like is supported, and the method has wide practicability and popularization value.

Description

Technical Field

[0001] This invention relates to the field of intelligent education technology, specifically to an adaptive intelligent teaching content generation system based on multimodal large-scale model reinforcement learning. Background Technology

[0002] As is well known, with the development of intelligent education technology, traditional teaching content generation systems have gradually exposed many limitations that cannot be ignored, and can no longer meet the needs of personalized teaching and diverse learning scenarios. At the perception level, traditional systems often rely on single text-based answer data to judge learner status, frequently neglecting implicit information such as learners' behavioral characteristics, emotional tendencies, and physiological states. This results in an incomplete capture of learners' true needs, making it difficult to accurately grasp learners' knowledge acquisition and cognitive characteristics, thus affecting the effectiveness of subsequent teaching content adaptation. Regarding decision-making mechanisms, traditional systems typically use a single algorithm for matching and pushing teaching content, lacking the ability to respond quickly to immediate feedback. Furthermore, they do not consider the planning of long-term learning paths, resulting in a rigid decision-making process that cannot dynamically adjust adaptation strategies based on learners' real-time status, making it difficult to achieve truly individualized instruction.

[0003] Traditional systems mostly assemble content based on pre-set templates, resulting in monotonous teaching content with insufficient multimodal presentation, making it difficult to adapt to the cognitive habits of different learners. Furthermore, the connections between knowledge points are weak, lacking integration and connection of interdisciplinary knowledge, and the content has low flexibility and personalization, failing to meet the diverse learning needs of learners. Regarding system evolution capabilities, traditional systems lack effective closed-loop iterative optimization mechanisms. Once teaching content and decision-making strategies are determined, it is difficult to adjust them according to actual usage, and system performance cannot continuously improve with the accumulation of usage scenarios. In addition, cross-scenario adaptation often requires a large amount of labeled data, leading to high implementation costs and difficulty in quickly covering different educational stages and subject areas.

[0004] Traditional systems lack effective protection measures for learners' physiological and behavioral data, posing a risk of privacy breaches. Furthermore, these systems are poorly adaptable to different devices and learning scenarios, struggling to be compatible with various terminal devices such as PCs, tablets, and mobile phones, and unable to quickly adapt to different teaching scenarios including K-12 education, higher education, and vocational training, thus limiting their application scope and practicality. These problems severely restrict the development and promotion of intelligent teaching content generation systems, necessitating an innovative technological solution that can overcome these limitations. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an adaptive intelligent teaching content generation system based on multimodal large-scale model reinforcement learning.

[0006] (II) Technical Solution To achieve the above objectives, this invention provides the following technical solution: an adaptive intelligent teaching content generation system based on multimodal large-scale model reinforcement learning, comprising a multimodal precise perception layer, a multi-algorithm hybrid reinforcement learning decision engine, a multimodal dynamic generator, a closed-loop iterative optimization layer, and security and compatibility modules respectively connected to the above layers; wherein: The multimodal precision perception layer is used to collect multi-source learning data and generate dynamic learner representations; the multi-algorithm hybrid reinforcement learning decision engine outputs teaching content adaptation decision instructions based on the dynamic learner representations; The multimodal dynamic generator generates personalized teaching content based on the adaptation decision instructions; The closed-loop iterative optimization layer updates the parameters of the multimodal accurate perception layer, the multi-algorithm hybrid reinforcement learning decision engine, and the multimodal dynamic generator based on learner interaction feedback; The security and compatibility module is used to ensure data security and system adaptability in multiple scenarios.

[0007] Furthermore, the multimodal precision perception layer includes: a multi-source data acquisition unit, used to collect text interaction data, video behavior data, audio feedback data, physiological signal data, and knowledge mastery data through cameras, microphones, EEG sensors, heart rate monitoring devices, and learning terminals; a cross-modal collaborative detection unit, including a cross-modal attention Transformer submodule and a neural symbol reasoning submodule, wherein the cross-modal attention Transformer submodule encodes the multi-source data and achieves cross-modal semantic alignment, and the neural symbol reasoning submodule parses the logical connections between knowledge points to correct data-driven detection bias; and a four-dimensional profile generation unit, used to fuse knowledge state, behavioral characteristics, emotional tendencies, and cognitive load data to output a real-time updated dynamic learner representation.

[0008] Furthermore, the multi-algorithm hybrid reinforcement learning decision engine includes: a state space enhancement unit, used to define the learner's four-dimensional representation, current teaching progress, knowledge point association graph, cognitive load threshold, and long-term learning goal as the decision state space; a hybrid decision algorithm module, including a basic decision layer and a long-term planning layer, wherein the basic decision layer uses the PPO algorithm to ensure decision stability and the DQN algorithm to improve the speed of immediate feedback response, and the long-term planning layer uses the Monte Carlo Tree Search (MCTS) algorithm to traverse the knowledge point dependency path and generate a 3-5 step teaching content plan; a reward dynamic optimization unit, used to adjust the weight ratio of immediate mastery, cognitive load fit, long-term transfer effect, and user satisfaction in real time through the entropy weight method, and output a comprehensive reward signal; and a decision optimization unit, used to introduce a simulated annealing algorithm to avoid the decision from getting trapped in local optima.

[0009] Furthermore, the multimodal dynamic generator includes: a dynamic knowledge atom library based on graph neural networks (GNNs) for storing the smallest granularity knowledge point units, and updating the association weights between knowledge points in real time through GNNs to support automatic association of cross-disciplinary knowledge points; a generative adversarial network (GAN) multimodal component generation unit for generating personalized text explanations, dynamic animations, interactive simulation scenarios, and interactive exercises; a modality adaptation algorithm module for matching the optimal modality combination based on the learner's cognitive load and behavioral characteristics using the K-nearest neighbor algorithm; and an intelligent assembly engine for integrating the adaptation decision instructions output by the multi-algorithm hybrid reinforcement learning decision engine with the GNN knowledge paths to generate logically coherent, modally adapted, and progressively challenging structured teaching content.

[0010] Furthermore, the closed-loop iterative optimization layer includes: a feedback acquisition enhancement unit, used to collect passive feedback data and active feedback data, wherein the passive feedback data includes content viewing time, number of repeated viewings, and incorrect question markings, and the active feedback data includes content satisfaction ratings, difficulty feedback, and subjective cognitive load ratings; an online self-calibration module, used to use a Bayesian algorithm to correct the detection parameters of the multimodal accurate perception layer and the decision parameters of the multi-algorithm hybrid reinforcement learning decision engine in real time; a meta-learning transfer module, used to use a model-independent meta-learning (MAML) algorithm to achieve rapid adaptation of the system to new teaching scenarios based on a small amount of new scenario data; and a generation model fine-tuning unit, used to use the feedback data collected by the feedback acquisition enhancement unit as adversarial examples for GAN to continuously optimize the generation quality of multimodal components.

[0011] Furthermore, the cross-modal collaborative detection unit also includes a Bayesian self-calibration submodule, which is used to fuse the detection results of the cross-modal attention Transformer submodule and the neural symbol reasoning submodule in real time, dynamically adjust the weight ratio of each modality data to reduce the detection bias caused by environmental interference, so that the learner state detection accuracy is ≥95% and the cognitive load detection error is ≤5%.

[0012] Furthermore, in the Reward dynamic optimization unit, the initial weight of immediate mastery is 30%, the initial weight of cognitive load fit is 25%, the initial weight of long-term transfer effect is 35%, and the initial weight of user satisfaction is 10%. Each weight is dynamically adjusted in real time based on learner interaction feedback collected by the closed-loop iterative optimization layer using the entropy weight method.

[0013] Furthermore, the intelligent assembly engine supports full-granularity teaching content generation, including single knowledge point explanations, cross-chapter comprehensive courseware, online interactive courses, and short video micro-lessons, and the generated teaching content can automatically adapt to PDF, online course, and short video output formats.

[0014] Furthermore, the security and compatibility module includes a federated learning data encryption unit, which uses a federated learning framework to encrypt learners' physiological data, behavioral data, and privacy data, achieving data usability without visibility to ensure data privacy and security.

[0015] Furthermore, the security and compatibility module also includes a multi-scenario and multi-device adaptation unit. This unit supports access from PCs, tablets, mobile phones, and smart learning terminals, and can automatically adapt to the screen size and interaction methods of different devices. It can also quickly adapt to various teaching scenarios such as K-12 education, higher education, and vocational training.

[0016] (III) Beneficial Effects Compared with existing technologies, this invention provides an adaptive intelligent teaching content generation system for multimodal large-model reinforcement learning, which has the following beneficial effects: This multimodal large-scale model reinforcement learning-based adaptive intelligent teaching content generation system effectively addresses many shortcomings of traditional teaching content generation systems by constructing a multi-module collaborative end-to-end architecture, demonstrating significant technical advantages and application value. Regarding perception accuracy, this invention integrates multiple data sources, including text, audio / video, physiological signals, and knowledge mastery, through a multi-source data acquisition unit. Combined with cross-modal collaborative detection and a Bayesian self-calibration mechanism, it achieves comprehensive capture and precise characterization of learners' states. This allows for in-depth analysis of learners' explicit knowledge states and implicit characteristics, providing high-quality data support for subsequent decision-making and content generation, ensuring the accuracy of teaching content adaptation.

[0017] This invention employs a multi-algorithm hybrid reinforcement learning decision engine, integrating the advantages of various algorithms. This ensures the stability of decisions while improving the response speed to immediate feedback. Simultaneously, it achieves a balance between short-term adaptation and long-term learning effects through long-term path planning. The dynamic reward mechanism can adjust the weights of each evaluation dimension in real time based on learner feedback, making decision instructions more aligned with learners' actual needs and realizing dynamic optimization of the teaching content adaptation strategy.

[0018] This invention, based on the GNN dynamic knowledge atom library and the GAN multimodal component generation unit, breaks through the limitations of traditional template-based content generation, supports full-granularity, multimodal teaching content generation, and can realize the logical connection and interdisciplinary integration between knowledge points. The generated teaching content is logically coherent and diverse in form, which can be adapted to the cognitive characteristics of different learners and diverse learning scenarios, significantly improving the personalization and flexibility of teaching content.

[0019] The closed-loop iterative optimization layer and meta-learning transfer module constructed in this invention enable the system to correct parameters in real time based on learner interaction feedback, continuously optimizing perception accuracy, decision-making strategies, and content quality. At the same time, it can quickly adapt to different teaching scenarios with only a small amount of new scenario data, without the need for a large amount of labeled data, reducing the implementation cost and realizing the continuous evolution of system performance and rapid cross-scenario coverage.

[0020] This invention effectively protects learners' privacy data through a federated learning data encryption unit, achieving data usability without visibility and ensuring data security and compliance. The multi-scenario, multi-device adaptation unit supports access from various terminal devices and rapid adaptation to different teaching scenarios, expanding the system's application scope and enhancing its practicality and promotional value. This invention achieves a leapfrog upgrade in teaching content generation, from passive matching to active adaptation, and from static solidification to dynamic optimization. It can provide personalized, high-quality teaching content for learners at different educational stages and in different subject areas, while simultaneously ensuring data security and multi-scenario adaptation, demonstrating broad application prospects and practical value. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall workflow of the system of the present invention; Figure 2 This is a schematic diagram of the multimodal precise sensing process of the present invention; Figure 3 This is a schematic diagram of the hybrid reinforcement learning decision-making process of the present invention; Figure 4 This is a schematic diagram of the multimodal content generation process of the present invention; Figure 5 This is a schematic diagram of the closed-loop iterative optimization and security compatibility process of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figures 1 to 5 This invention is an adaptive intelligent teaching content generation system based on multimodal large-scale model reinforcement learning, comprising a multimodal accurate perception layer, a multi-algorithm hybrid reinforcement learning decision engine, a multimodal dynamic generator, a closed-loop iterative optimization layer, and security and compatibility modules connected to each of the above layers, working sequentially and collaboratively; wherein: The multimodal precision perception layer is used to collect multi-source learning data and generate dynamic learner representations; the multi-algorithm hybrid reinforcement learning decision engine outputs teaching content adaptation decision instructions based on the dynamic learner representations; The multimodal dynamic generator generates personalized teaching content based on the adaptation decision instructions; The closed-loop iterative optimization layer updates the parameters of the multimodal accurate perception layer, the multi-algorithm hybrid reinforcement learning decision engine, and the multimodal dynamic generator based on learner interaction feedback; The security and compatibility module is used to ensure data security and system adaptability in multiple scenarios. Breaking through the limitations of isolated and static matching modules in traditional teaching content generation systems, it upgrades teaching content from passive response to active adaptation through end-to-end collaboration. This ensures the system can dynamically adjust teaching content based on learners' real-time status, while simultaneously considering data security and multi-scenario adaptability. It provides overall architectural support for the subsequent implementation of core technologies, enhancing the system's practicality and completeness.

[0024] In this solution, the multimodal precision perception layer includes: a multi-source data acquisition unit, used to collect text interaction data, video behavior data, audio feedback data, physiological signal data, and knowledge mastery data through cameras, microphones, EEG sensors, heart rate monitoring devices, and learning terminals; a cross-modal collaborative detection unit, including a cross-modal attention Transformer submodule and a neural symbol reasoning submodule. The cross-modal attention Transformer submodule encodes the multi-source data and achieves cross-modal semantic alignment, while the neural symbol reasoning submodule parses the logical connections between knowledge points to correct data-driven detection biases; and a four-dimensional profile generation unit, used to fuse knowledge state, behavioral characteristics, emotional tendencies, and cognitive load data to output a real-time updated dynamic learner representation. This solution addresses the pain points of traditional single-modal perception's incomplete information and ambiguous detection. Multi-source data acquisition ensures the integrity of the perception dimensions, cross-modal collaborative detection improves the accuracy of feature extraction, and four-dimensional dynamic representation enables a refined depiction of the learner's state, providing high-value data input for the subsequent decision engine. This allows the system to capture learners' implicit needs (such as cognitive load and emotional changes), laying the foundation for precise adaptation.

[0025] In this scheme, the multi-algorithm hybrid reinforcement learning decision engine includes: a state space enhancement unit, used to define the learner's four-dimensional representation, current teaching progress, knowledge point association graph, cognitive load threshold, and long-term learning goal as the decision state space; a hybrid decision algorithm module, including a basic decision layer and a long-term planning layer, wherein the basic decision layer uses the PPO algorithm to ensure decision stability and the DQN algorithm to improve the speed of immediate feedback response, and the long-term planning layer uses the Monte Carlo Tree Search (MCTS) algorithm to traverse the knowledge point dependency path and generate a 3-5 step teaching content plan; a reward dynamic optimization unit, used to adjust the weight ratio of immediate mastery, cognitive load fit, long-term transfer effect, and user satisfaction in real time through the entropy weight method, and output a comprehensive reward signal; and a decision optimization unit, used to introduce a simulated annealing algorithm to avoid the decision from getting trapped in local optima. This paper defines the core structure of a multi-algorithm hybrid reinforcement learning decision engine. The state space augmentation unit ensures comprehensive decision-making by expanding decision dimensions (covering learner state, teaching progress, knowledge associations, etc.). The hybrid decision algorithm module adopts a layered design of PPO+DQN+MCTS. PPO ensures decision stability, DQN improves immediate response speed, and MCTS realizes long-term teaching path planning. The reward dynamic optimization unit dynamically adjusts the multi-dimensional reward weights using the entropy weight method to avoid the one-sidedness of decisions caused by single rewards. The decision optimization unit introduces the simulated annealing algorithm to overcome the limitations of local optima. Overcoming the shortcomings of traditional single reinforcement learning algorithms, such as one-sided decision-making and lack of long-term planning, the hybrid algorithm design balances decision stability, immediacy, and foresight. The dynamic reward mechanism ensures that the reward signal comprehensively reflects the learning effect, and the simulated annealing algorithm improves the decision adaptation accuracy in complex scenarios, enabling the system to achieve a balance between immediate adaptation to the learner's current state and long-term optimization of knowledge transfer effects.

[0026] In this scheme, the multimodal dynamic generator includes: a dynamic knowledge atom library based on graph neural networks (GNNs) for storing the smallest granularity knowledge point units, and updating the association weights between knowledge points in real time through GNNs to support automatic association of cross-disciplinary knowledge points; a generative adversarial network (GAN) multimodal component generation unit for generating personalized text explanations, dynamic animations, interactive simulation scenarios, and interactive exercises; a modality adaptation algorithm module for matching the optimal modality combination based on the learner's cognitive load and behavioral characteristics using the K-nearest neighbor algorithm; and an intelligent assembly engine for integrating the adaptation decision instructions output by the multi-algorithm hybrid reinforcement learning decision engine with the GNN knowledge paths to generate logically coherent, modally adapted, and progressively challenging structured teaching content. The GNN-based dynamic knowledge atom base supports intelligent cross-disciplinary knowledge association by updating knowledge point association weights in real time. The GAN multimodal component generation unit breaks through traditional template limitations, generating highly realistic and personalized multimodal teaching components. The modality adaptation algorithm module matches the optimal modality combination based on the learner's state using the K-nearest neighbor algorithm. The intelligent assembly engine integrates decision instructions and knowledge paths to achieve structured generation of knowledge point logic, modality adaptation, and progressive difficulty. This addresses the problems of rigid templates, insufficient relevance, and single modality in traditional teaching content generation. The GNN dynamic knowledge base enhances content logic and cross-disciplinary adaptability, the GAN generation module strengthens content personalization and realism, the modality adaptation algorithm ensures content presentation matches the learner's state, and the intelligent assembly engine supports full-granularity content generation, adapting to different learning scenarios (preview, review, and comprehensive application), thus improving the learning experience and knowledge acquisition efficiency.

[0027] In this scheme, the closed-loop iterative optimization layer includes: a feedback acquisition enhancement unit, used to collect passive feedback data and active feedback data, wherein the passive feedback data includes content viewing time, number of repeated viewings, and incorrect question markings, and the active feedback data includes content satisfaction ratings, difficulty feedback, and subjective cognitive load ratings; an online self-calibration module, used to use a Bayesian algorithm to correct the detection parameters of the multimodal accurate perception layer and the decision parameters of the multi-algorithm hybrid reinforcement learning decision engine in real time; a meta-learning transfer module, used to use a model-independent meta-learning (MAML) algorithm to achieve rapid adaptation of the system to new teaching scenarios based on a small amount of new scenario data; and a generation model fine-tuning unit, used to use the feedback data collected by the feedback acquisition enhancement unit as adversarial examples for GAN to continuously optimize the generation quality of multimodal components. The feedback acquisition enhancement unit integrates passive feedback (behavioral data) and active feedback (subjective rating) to ensure the comprehensiveness and effectiveness of the feedback data. The online self-calibration module uses a Bayesian algorithm to correct perception and decision parameters in real time, reducing accuracy loss caused by environmental interference (such as device differences and lighting changes). The meta-learning transfer module uses the MAML algorithm to achieve rapid adaptation based on a small amount of new scene data. The generative model fine-tuning unit uses feedback data as adversarial examples to continuously optimize the quality of GAN generation. This achieves the system's self-optimization capability across the entire detection, decision-making, and generation chain. Passive and active feedback ensure the accuracy of the iteration basis, the online self-calibration module improves the system's anti-interference capability, the meta-learning transfer module reduces the dataset dependence for new scene implementation (no need for a large amount of labeled data), and the generative model fine-tuning unit continuously improves content adaptability, enabling the system to continuously evolve with the accumulation of usage scenarios and adapt to the learning needs of different disciplines and age groups.

[0028] In this scheme, the cross-modal collaborative detection unit also includes a Bayesian self-calibration submodule. This submodule is used to fuse the detection results of the cross-modal attention Transformer submodule and the neural symbolic reasoning submodule in real time, dynamically adjusting the weight ratio of each modality data to reduce detection bias caused by environmental interference, ensuring a learner state detection accuracy of ≥95% and a cognitive load detection error of ≤5%. By fusing the feature detection results of the cross-modal attention Transformer and the logical correction results of neural symbolic reasoning in real time using a Bayesian probabilistic model, the weight ratio of each modality data is dynamically adjusted (e.g., reducing the weight of the video modality and increasing the weight of the physiological + text modality when there is light interference), thereby correcting the detection bias caused by environmental interference. This solution specifically addresses the key problem of decreased accuracy due to environmental interference in multimodal detection. Through dynamic weight adjustment, it achieves complementary advantages of each modality data, stabilizing the learner state detection accuracy above 95% and controlling the cognitive load detection error within 5%, far exceeding the detection accuracy of approximately 80% in traditional systems, providing highly reliable input data for subsequent decision-making and generation modules.

[0029] In this scheme, within the Reward dynamic optimization unit, the initial weighting percentages for immediate mastery are 30%, cognitive load fit is 25%, long-term transfer effect is 35%, and user satisfaction is 10%. Each weight is dynamically adjusted in real-time based on learner interaction feedback collected by the closed-loop iterative optimization layer using the entropy weighting method. The initial weights are set as follows: 30% for immediate mastery + 25% for cognitive load fit + 35% for long-term transfer effect + 10% for user satisfaction, balancing immediate learning effectiveness, cognitive load, long-term knowledge transfer, and user experience. Simultaneously, the entropy weighting method adjusts the weighting percentages in real-time based on learner interaction feedback (such as error types and satisfaction ratings) collected by the closed-loop iterative optimization layer, ensuring that the reward signal matches the learner's actual needs. To avoid the limitations of traditional single-dimensional reward decisions that focus only on the present and ignore the long term, the initial weight allocation achieves a balance between immediate effects and long-term value. The dynamic adjustment mechanism enables the reward function to adapt to the learning characteristics of different learners (such as high-achieving students focusing more on long-term transfer, and those with weak foundations focusing more on immediate mastery), thereby improving the accuracy of reinforcement learning decisions and making teaching content more suitable for learners' personalized needs.

[0030] In this solution, the intelligent assembly engine supports full-granularity teaching content generation, including single-knowledge-point explanations, cross-chapter comprehensive courseware, online interactive courses, and short video micro-lessons. The generated teaching content can automatically adapt to PDF, online course, and short video output formats. By integrating GNN knowledge paths and reinforcement learning decision instructions, it supports full-granularity generation from single-knowledge-point explanations (such as single formula derivations) to cross-chapter comprehensive courseware (such as whole-unit review materials), online interactive courses (such as live-streaming content), and short video micro-lessons (such as fragmented learning content). Simultaneously, a built-in format conversion module automatically adapts the assembled content to multiple output formats such as PDF, online courses, and short videos. This overcomes the limitations of traditional systems with their single-granularity content generation and poor format compatibility. The full-granularity generation capability adapts to all learning scenarios, including pre-class preparation, in-class explanation, post-class review, and fragmented learning. Multi-format adaptation supports usage scenarios on different terminals such as PCs, tablets, and mobile phones, improving the system's versatility and convenience, reducing teachers' secondary editing costs, and expanding the coverage of learning scenarios.

[0031] In this solution, the security and compatibility module includes a federated learning data encryption unit. This unit uses a federated learning framework to encrypt learners' physiological and behavioral data, ensuring data is usable but not visible, thus guaranteeing data privacy and security. Using the federated learning framework, learners' physiological data (EEG, heart rate), behavioral data (facial expressions, viewing time), and other private data are stored on the local terminal. Only model parameters, not the raw data, are uploaded. Encryption algorithms enable collaborative training of data from multiple terminals, ensuring data is usable but not visible. This addresses the core pain point of learner privacy data leakage in teaching systems. The federated learning framework maximizes learner privacy and security without sacrificing the value of data collaboration, complies with relevant data security laws and regulations, eliminates user concerns about privacy leaks, improves system compliance and user acceptance, and provides security guarantees for the large-scale deployment of the system in educational scenarios.

[0032] In this solution, the security and compatibility module also includes a multi-scenario, multi-device adaptation unit. This unit supports access from PCs, tablets, mobile phones, and smart learning terminals, automatically adapting to the screen sizes and interaction methods of different devices, and quickly adapting to various teaching scenarios such as K-12 education, higher education, and vocational training. By being compatible with various hardware interfaces such as cameras, microphones, and EEG sensors, it supports access from multiple devices including PCs, tablets, mobile phones, and smart learning terminals. It incorporates a built-in screen size adaptation algorithm and interaction logic adaptation module, automatically adjusting content layout and operation methods. Combined with the rapid adaptation capabilities of the meta-learning transfer module, it achieves rapid coverage of various teaching scenarios such as K-12 education, higher education, and vocational training. This overcomes the problems of poor device compatibility and high scenario adaptation costs in traditional teaching systems. Multi-device access supports the usage needs of different learning environments (home, school, outdoor). The rapid scenario adaptation capability reduces the implementation cost of the system at different educational stages and in different subjects, eliminating the need for extensive customized development for single scenarios, enhancing the system's potential for large-scale application, and expanding the technology's coverage and practical value.

[0033] Multi-dimensional dynamic reward calculation function, algorithm formula:

[0034] The weight calculation satisfies the following:

[0035] No. The comprehensive reward signal at any given moment is the core input of the reinforcement learning decision engine, directly determining the direction and magnitude of the decision strategy update. Its function is to quantify the current adaptation effect of the teaching content, provide a unified reward basis for the PPO+DQN+MCTS hybrid decision-making process, and achieve a balance between immediate adaptation and long-term optimization.

[0036] No. Time of the first The dynamic weights of each evaluation dimension are calculated using the entropy weighting method and updated in real time with learner interaction feedback (the update frequency is consistent with the closed-loop iteration cycle, defaulting to 500ms / time). Function: To solve the problem of one-sidedness in traditional fixed-weight decision-making, making the reward signal adaptable to changes in learner status (e.g., increasing the weight of immediate mastery for learners with weak foundations, and increasing the weight of long-term transfer effects for high-achieving learners).

[0037] No. Time of the first Standardized indicator values ​​for each evaluation dimension (range of values) It comprises four core dimensions, covering all scenarios: immediate, medium-term, long-term, and subjective. : =Instant Mastery Index (calculated based on answer accuracy and time taken to complete knowledge points, e.g., 80% accuracy) ); =Cognitive load fit index (calculated based on EEG signals, heart rate, and facial expressions; when cognitive load is moderate) (When the value is too high or too low, it approaches 0). =Long-term transfer effectiveness metrics (calculated based on the accuracy of subsequent application of related knowledge points and knowledge reuse rate, such as a 75% transfer accuracy rate after 3 steps) ); =User satisfaction index (calculated based on subjective ratings and sentiment analysis of feedback messages, with a score of 4.5 / 5) ).

[0038] No. The information entropy of each evaluation dimension reflects the dispersion of the data in that dimension (the degree of differentiation in learner feedback). Its function: the lower the entropy value, the higher the discriminative power and informational value of the data in that dimension, and the higher the corresponding weight. The higher the value, the better, ensuring that the weight allocation aligns with the principle of prioritizing high-value feedback.

[0039] No. The number of samples for that evaluation dimension at any given time, i.e., the number of feedback data points for that dimension collected by the closed-loop iterative optimization layer (e.g., ... of This represents the number of times you can answer the question for the current knowledge point. of (Number of subjective ratings collected).

[0040] No. Time of the first The first dimension The standardized probability of a sample is given by Calculation yields ( For the first (Original sample data). Purpose: To standardize the original feedback data into a probability distribution, providing a basis for information entropy calculation and eliminating differences in the dimensions of data from different dimensions.

[0041] Information entropy The calculation logic first standardizes the raw feedback data for each evaluation dimension (such as answer accuracy, heart rate, and subjective score) to obtain... This ensures that the data are on the same probability scale. Calculated using the information entropy formula If all learners provide highly consistent feedback on this dimension (e.g., all give a perfect score for satisfaction), then A value approaching 1 indicates low information value in that dimension; if the feedback difference is significant (e.g., the correct answer rate is distributed between 30% and 90%), then... A value close to 0 indicates that this dimension can effectively distinguish the adaptation effect and has high information value.

[0042] Dynamic weights The allocation logic is achieved through... Transform information entropy into weights, satisfying... ; Entropy The lower the value, the higher the information value. The larger the value, the higher the corresponding weight. The higher the value, the more dynamic the reward calculation becomes, enabling a dynamic adaptation where "high-value feedback dimensions dominate reward calculation".

[0043] Comprehensive Rewards The calculation logic uses a weighted summation method to integrate four dimensions of indicators, avoiding decision-making bias caused by a single dimension; Updated with feedback data collected by the closed-loop iterative optimization layer. and Real-time adjustment, enabling It can continuously reflect learners' real needs and learning outcomes.

[0044] Breaking through the limitations of traditional reinforcement learning's "single-dimensional reward": by fully covering "learning effectiveness - cognitive state - long-term value - user experience" in four dimensions, ensuring the comprehensiveness of reward signals; Dynamic weight allocation mechanism: Based on the entropy weight method, it automatically adapts the value of feedback data without human intervention, enabling decisions to be dynamically adjusted according to the learner's state and learning stage; Connecting the core modules of the system: The data comes directly from the multimodal precision perception layer. Updating feedback data from the closed-loop iterative optimization layer is the core algorithm that connects the entire chain of perception, decision-making, and feedback. It provides accurate and dynamic reward basis for hybrid reinforcement learning decision-making, ultimately achieving adaptive optimization of teaching content.

[0045] Example 1: K12 education scenario (teaching knowledge points of circuit connection in junior high school physics).

[0046] To address the challenges faced by second-year junior high school students in learning series / parallel circuit connections, such as difficulty in understanding abstract concepts, high practical risks, and significant individual differences, the system uses multimodal perception of student status to generate interactive and visual teaching content, supporting classroom teaching and after-class independent practice.

[0047] Multimodal data acquisition: Hardware configuration: Students are equipped with tablet learning terminals with built-in cameras / microphones, and teachers are equipped with heart rate monitoring auxiliary devices (optional); Data collection: The camera captures students' facial expressions (frowning, focus, confusion) and practical actions (tablet touch connection operation), the microphone collects the students' voice answers (speech speed, emotion), the tablet records the answer data (correctness of circuit connection, answer time) and interactive behavior (number of times to watch repeatedly, jump operation), and the heart rate device simultaneously collects heart rate change data related to cognitive load.

[0048] Multimodal accurate perception: The cross-modal attention Transformer encodes and aligns touch operation data, voice emotion data, and answer data to identify features such as students frequently making mistakes in online operations, speaking faster, and frowning more frequently. The neural symbol reasoning submodule resolves logical connections: the concept of current path in a series circuit was not grasped, leading to reversed positive and negative terminals when connecting wires; Due to the dim lighting at some students' seats, the Bayesian self-calibration submodule adjusted the weighting of text answer data and heart rate data to 70%, ultimately generating a four-dimensional representation: Knowledge status: weak understanding of series circuits, basic grasp of parallel circuits; Behavioral characteristics: low enthusiasm for practical operation, many repetitive operations; Emotional tendency: moderate anxiety; Cognitive load: high (average heart rate 105 beats / minute).

[0049] Hybrid reinforcement learning decision-making: State space input: four-dimensional representation, current teaching progress (basic circuit connection explanation stage), knowledge point map (circuit concept, series connection, parallel connection, comprehensive practice); Hybrid decision-making algorithms: The DQN algorithm responds to anxiety and high cognitive load, outputting immediate decisions to reduce difficulty and switch interaction modalities; MCTS plans a 4-step teaching path including concept simplification, animation demonstration, virtual practice, and review of incorrect questions; the PPO algorithm ensures smooth content switching; Reward Dynamic Optimization: As students are in the basic learning stage, the weight of immediate mastery is adjusted to 40%, and the weight of cognitive load adaptation is adjusted to 30%. The output decision instruction is: the modality is switched to 3D animation and virtual simulation, the difficulty is adjusted to easy, the knowledge points are broken down and explained in series circuit current path, and virtual practical interactive questions are generated.

[0050] Multimodal content generation: GNN dynamic knowledge atom library calls two smallest granular knowledge points: series circuit current path electrode connection rules; The GAN generation unit generates 3D animations (dynamically demonstrating the flow of current from the positive terminal of the power supply, the appliance, and the negative terminal, and annotating key nodes) and virtual simulation components (draggable power supply, light bulb, and wire models, supporting real-time connection verification). The modal adaptation algorithm matches animated demonstrations and virtual hands-on exercises, and the intelligent assembly engine generates structured content: 1 minute of simplified text explanation, 3 minutes of 3D animation, 5 minutes of virtual connection exercises (including real-time error correction prompts), and 2 basic verification questions, all pushed in a tablet-adapted format.

[0051] Closed-loop iterative optimization: Feedback collection: passive feedback (animation viewing time 4 minutes, virtual operation error correction 3 times, final connection accuracy rate 85%), active feedback (satisfaction score 4.2 / 5, cognitive load subjective score 2 / 5, comments animation is very intuitive, operation can correct errors); Iterative adjustments: The online self-calibration module corrected the threshold for recognizing confused expressions; the meta-learning transfer module recorded the high preference of eighth-grade students for 3D animation modalities; and the generation model fine-tuning unit optimized the error correction prompts for subsequent virtual practice. Next round of decision-making: For parallel circuit connections that are not fully mastered, generate advanced content such as animated comparisons of series / parallel connections, group virtual practice, and explanations of the root causes of incorrect questions.

[0052] Security and compatibility: The Federated Learning Encryption Unit stores students' heart rate and facial expression data locally, and only uploads characteristic parameters such as a preference for animations with high cognitive load. The tablet terminal automatically adapts to the content layout, supports landscape operation mode, and is suitable for classroom group use and after-school home self-study scenarios.

[0053] Students' mastery of knowledge points increased from 65% in traditional teaching to 92%, while the average cognitive load decreased by 30%. 89% of students reported that virtual hands-on practice was risk-free and the animations were easy to understand, reducing teachers' workload in answering questions in class by 40%.

[0054] Example 2: Higher Education Scenario (Teaching knowledge points of deep learning CNN convolution operations in computer science majors).

[0055] To address the challenges faced by third-year computer science students learning CNN convolution kernel operations and feature map extraction, such as abstract mathematical principles, weak interdisciplinary knowledge connections, and insufficient integration with scientific research applications, the system generates tiered teaching content encompassing theory, practice, and research cases through cross-modal generation and long-term path planning.

[0056] Multimodal data acquisition: Hardware configuration: Students are equipped with a PC and an external camera / microphone; the laboratory is equipped with an EEG sensor (optional). Data collection: PC records reading time of academic papers, answer data of formula derivation, and correctness of programming experiment code; camera captures focus status; microphone collects group discussion voice (logical coherence, points of doubt); EEG sensor collects EEG waveform data related to cognitive load.

[0057] Multimodal accurate perception: Cross-modal attention Transformer aligns paper reading data, programming code data, and voice discussion data to identify students with low accuracy in deriving mathematical formulas, frequent API calls during programming implementation without understanding the underlying principles, and repeated questions raised in discussions about the relationship between feature maps and the original images. The logic chain of the neural symbolic reasoning submodule was analyzed: lack of proficiency in linear algebra matrix operations led to confusion about the principles of convolution kernel operations, affecting parameter tuning for programming implementation; Due to the stability of the EEG data signal, the weight of the Bayesian self-calibration submodule was increased to 35%, generating a four-dimensional representation: Knowledge status: understanding of the concept of convolution, weak matrix operation, and basic programming implementation; Behavioral characteristics: high focus on reading papers and frequent programming interaction; Emotional tendency: neutral; Cognitive load: medium to high (32% of EEG alpha waves).

[0058] Hybrid reinforcement learning decision-making: State space inputs: four-dimensional representation, teaching progress (CNN basic principles stage), interdisciplinary knowledge point map (linear algebra matrix multiplication, convolution kernel operation, feature map extraction, Python programming implementation, image classification research case); Hybrid Decision Algorithms: A 5-step long-term path including MCTS planning matrix operations review, visualization of convolution principles, programming practice, and research case analysis; PPO ensures a smooth transition between theory and practice; DQN responds to programming errors and adjusts the depth of explanation in real time; Reward Dynamic Optimization: Since higher education focuses on long-term migration, the weight of long-term migration effect is adjusted to 40%, and the output decision instruction is: the modality is switched to dynamic formula derivation, Python interactive programming, and scientific research paper excerpts, the difficulty is adjusted to medium, and interdisciplinary linear algebra knowledge points are linked to generate step-by-step programming tutorials.

[0059] Multimodal content generation: This includes cross-disciplinary knowledge points such as GNN dynamic knowledge atom library calls, matrix multiplication rules, convolution kernel operation formulas, Python torch.nn.Conv2d function, ImageNet classification examples, etc. GAN generator units generate dynamic formula derivation animations (step-by-step demonstration of the multiplication and accumulation process of 3×3 convolution kernels and input images), interactive programming templates (including convolution operation code with adjustable parameters, real-time output of feature maps), and scientific research case visualization clips (demonstrating the extraction effect of convolutional layers on image edges and textures). The intelligent assembly engine generates structured content: a 2-minute matrix multiplication review text, a 5-minute animation on convolution principles, a 10-minute interactive programming exercise (including error prompts), an 8-minute research case study (citing excerpts from top conference papers), and 3 advanced thinking questions, adapted to both PC PDF and online programming environment formats.

[0060] Closed-loop iterative optimization: Feedback collection: passive feedback (completion rate of programming practice 90%, accuracy rate of feature map analysis questions 75%, reading time of scientific research cases 12 minutes), active feedback (satisfaction score 4.6 / 5, suggestion to add convolution cases in complex scenarios); Iterative adjustments: The meta-learning transfer module quickly adapts to the knowledge base of computer science students; the model fine-tuning unit adds multi-scale convolution kernel examples; the online self-calibration module corrects the correlation parameters between programming errors and cognitive load. The next round of content will include advanced topics such as generating multi-scale convolution principles, practical exercises in complex image classification, and tutorials on reproducing research projects, aligning with the research needs of graduate students.

[0061] Students' understanding of the principles of convolution operations improved by 60%, and the accuracy of their programming implementations increased from 55% to 88%. 78% of students were able to independently build convolutional layers for simple image classification models, demonstrating a significant improvement in their interdisciplinary knowledge transfer ability.

[0062] Example 3: Vocational training scenario (practical training in troubleshooting low-voltage circuits for electricians).

[0063] To address the training pain points of new employees in enterprise electrician positions, such as insufficient practical experience in troubleshooting low-voltage circuit faults, high safety risks, and diverse fault scenarios, the system achieves integrated theoretical and practical training through multimodal content generation combining virtual and real elements and real-time feedback optimization, thereby reducing practical risks and improving training efficiency.

[0064] Multimodal data acquisition: Hardware configuration: The training site is equipped with tablet terminals, VR devices, and portable cameras, and enterprises deploy federated learning data nodes; Data collection: VR devices capture employees' actual operating actions (multimeter usage posture, circuit contact operation), tablets record troubleshooting answers (fault type judgment, troubleshooting step selection), cameras collect operational standardization (safety protection wearing, operation procedures), microphones collect verbal reports (fault analysis logic), and terminals synchronously record operation duration and number of repeated operations.

[0065] Multimodal Precise Perception: The cross-modal attention Transformer aligns VR practical data, answer data, and oral report data to identify errors such as incorrect multimeter range selection, chaotic troubleshooting steps, and unclear oral report logic. The neural symbol reasoning submodule resolves logical connections: unfamiliarity with low-voltage circuit safety regulations leads to incorrect operation sequence and increases the difficulty of troubleshooting; Due to the high accuracy of VR motion data, the Bayesian self-calibration submodule has its weight adjusted to 50%, generating a four-dimensional representation: Knowledge status: basic fault type identification, unfamiliar with troubleshooting procedures; Behavioral characteristics: non-standard practical actions, many repetitive operations; Emotional tendency: slight tension; Cognitive load: high (operational error rate 35%).

[0066] Hybrid reinforcement learning decision-making: State space input: four-dimensional representation, training objectives (low-voltage circuit fault diagnosis and testing standards), practical knowledge point map (safety specifications, fault type identification, use of troubleshooting tools, step optimization, emergency handling); Hybrid decision-making algorithms: DQN responds instantly to operational errors, outputting instructions to pause practice and push standardized demonstration commands; MCTS plans a 3-step path of safety standard review, tool usage demonstration, step-by-step practice, and fault reproduction; PPO ensures the connection between practice and theory; Reward Dynamic Optimization: Vocational training focuses on practical safety and immediate mastery, adjusting the weight of immediate mastery to 35% and the weight of operational standardization to 30%, and outputting decision instructions: modal switching to VR standardized demonstration, practical guidance, and error tracing, adjusting the difficulty to easy, and focusing on the standardized steps of multimeter use and troubleshooting.

[0067] Multimodal content generation: Practical knowledge points such as calling GNN dynamic knowledge atom library, low-voltage circuit safety operation specifications, multimeter range selection rules, and short-circuit fault troubleshooting steps; The GAN generation unit generates VR hands-on demonstration videos (standard multimeter operation posture, troubleshooting step-by-step actions), interactive flowcharts (drag and drop sorting troubleshooting steps), and fault simulation scenarios (simulating common faults such as short circuits and poor contact). The intelligent assembly engine generates structured training content: a 3-minute safety specification text, a 5-minute VR standard operation demonstration, a 10-minute step-by-step practical exercise (VR scene, including real-time error correction prompts), and 3 fault simulation cases, adapted to the interaction format of VR devices and tablet terminals.

[0068] Closed-loop iterative optimization: Feedback collection: Passive feedback (VR operation standardization rate increased from 60% to 85%, troubleshooting time reduced by 40%, and number of repeated operations reduced by 5), proactive feedback (satisfaction rating 4.3 / 5, suggestion to add complex mixed fault cases); Iterative adjustments: The model fine-tuning unit adds mixed fault scenarios of short circuit and poor contact; the online self-calibration module corrects the correlation threshold between stress and operational errors; the meta-learning transfer module quickly adapts to the training needs of different low-voltage circuit models in enterprises. Assessment content generation: The system automatically generates a comprehensive assessment of three random fault scenarios, practical operation assessment, and theoretical Q&A, and simultaneously generates a training report.

[0069] Security and compatibility: The federated learning data encryption unit locally encrypts and stores employee operation data and personal physiological characteristics (data related to stress), and only uploads aggregated data such as practical operation pass rate and weak knowledge points to the enterprise training platform to ensure privacy and security; The system supports switching between multiple terminals such as VR devices, tablets, and mobile phones, and is suitable for different scenarios such as training sites and self-study in dormitories.

[0070] The pass rate for new employees' practical skills in troubleshooting low-voltage circuit faults increased from 45% to 90%, and operational safety improved by 100% (no violations). The training cycle for enterprises is shortened by 30%, the cost of practical training materials is reduced by 60%, and the efficiency and economy of training are significantly improved.

[0071]

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive intelligent teaching content generation system for multi-modal large model reinforcement learning, characterized in that, The system comprises a multi-modal precise perception layer, a multi-algorithm hybrid reinforcement learning decision engine, a multi-modal dynamic generator, a closed-loop iterative optimization layer, and a safety and compatibility module connected to each of the above layers. The multi-modal precise perception layer is configured to collect multi-source learning data and generate dynamic learner representations. The multi-algorithm hybrid reinforcement learning decision engine outputs teaching content adaptation decision instructions based on the dynamic learner representations. The multi-modal dynamic generator generates personalized teaching content according to the adaptation decision instructions. The closed-loop iterative optimization layer updates the parameters of the multi-modal precise perception layer, the multi-algorithm hybrid reinforcement learning decision engine, and the multi-modal dynamic generator based on learner interaction feedback. 2.The adaptive intelligent teaching content generation system of multi-modal large model reinforcement learning of claim 1, wherein, The safety and compatibility module is configured to ensure data security and system adaptability in multiple scenarios. 3.The adaptive intelligent teaching content generation system of multi-modal large model reinforcement learning of claim 1, wherein, The multi-modal precise perception layer comprises a multi-source data acquisition unit configured to collect text interaction data, video behavior data, audio feedback data, physiological signal data, and knowledge mastery data through a camera, a microphone, an electroencephalogram sensor, a heart rate monitoring device, and a learning terminal; a cross-modal collaborative detection unit comprising a cross-modal attention Transformer sub-module and a neural-symbol reasoning sub-module, the cross-modal attention Transformer sub-module encodes the multi-source data and realizes cross-modal semantic alignment, and the neural-symbol reasoning sub-module analyzes the logical association between knowledge points to correct data-driven detection bias; and a four-dimensional portrait generation unit configured to fuse knowledge state, behavior characteristics, emotional tendency, and cognitive load data to output real-time updated dynamic learner representations. The multi-algorithm hybrid reinforcement learning decision engine comprises a state space enhancement unit configured to define learner four-dimensional representations, current teaching progress, knowledge point association graphs, cognitive load thresholds, and long-term learning goals as decision state spaces; a hybrid decision algorithm module comprising a basic decision layer and a long-term planning layer, the basic decision layer uses a PPO algorithm to ensure decision stability and a DQN algorithm to improve immediate feedback response speed, and the long-term planning layer uses a Monte Carlo Tree Search (MCTS) algorithm to traverse knowledge point dependency paths and generate 3-5 step teaching content plans; a Reward dynamic optimization unit configured to use an entropy weight method to adjust the weight proportions of immediate mastery, cognitive load adaptation, long-term transfer effect, and user satisfaction in real time and output a comprehensive reward signal; and a decision optimization unit configured to introduce a simulated annealing algorithm to avoid decision-making from falling into local optimum. 4.The adaptive intelligent teaching content generation system of multi-modal large model reinforcement learning of claim 1, wherein, The multi-modal dynamic generator comprises a graph neural network (GNN)-based dynamic knowledge atom library for storing minimum granularity knowledge point units and updating correlation weights between knowledge points in real time through a GNN to support automatic correlation of cross-disciplinary knowledge points; a generative adversarial network (GAN) multi-modal component generation unit for generating personalized text explanations, dynamic animations, interactive simulation scenes and interactive exercises; a modal adaptation algorithm module for matching an optimal modal combination based on a learner's cognitive load and behavior characteristics using a K-nearest neighbor algorithm; and an intelligent assembly engine for fusing an adaptive decision instruction output by the multi-algorithm hybrid reinforcement learning decision engine and a GNN knowledge path to generate structured teaching content that is logically coherent, modal-adapted and progressively difficult. 5.The adaptive intelligent teaching content generation system of multi-modal large model reinforcement learning of claim 1, wherein, The closed-loop iterative optimization layer comprises a feedback collection enhancement unit for collecting passive feedback data and active feedback data, the passive feedback data including content viewing duration, repeated viewing times and error question marking, and the active feedback data including content satisfaction ratings, difficulty feedback and cognitive load subjective ratings; an online self-calibration module for using a Bayesian algorithm to correct detection parameters of the multi-modal precision perception layer and decision parameters of the multi-algorithm hybrid reinforcement learning decision engine in real time; a meta-learning transfer module for using a model-agnostic meta-learning (MAML) algorithm to achieve rapid adaptation of the system to new teaching scenarios based on a small amount of new scenario data; and a generative model fine-tuning unit for using feedback data collected by the feedback collection enhancement unit as adversarial samples of a GAN to continuously optimize the generation quality of multi-modal components. 6.The adaptive intelligent teaching content generation system of multimodal large model reinforcement learning of claim 2, wherein, The cross-modal collaborative detection unit further comprises a Bayesian self-calibration submodule for fusing detection results of the cross-modal attention Transformer submodule and the neural-symbolic reasoning submodule in real time, dynamically adjusting the weight proportion of each modal data to reduce detection bias caused by environmental interference, and achieving a learner state detection accuracy of ≥ 95% and a cognitive load detection error of ≤ 5%.

7. The adaptive intelligent teaching content generation system for multi-modal large model reinforcement learning of claim 3, wherein, In the Reward dynamic optimization unit, the initial weight proportion of the instant mastery degree is 30%, the initial weight proportion of the cognitive load adaptation degree is 25%, the initial weight proportion of the long-term transfer effect is 35%, and the initial weight proportion of the user satisfaction is 10%, and each weight is dynamically adjusted in real time according to learner interaction feedback collected by the closed-loop iterative optimization layer through an entropy weight method. 8.The adaptive intelligent teaching content generation system of multi-modal large model reinforcement learning of claim 4, wherein, The intelligent assembly engine supports full-granularity teaching content generation, including single-knowledge-point explanations, cross-chapter comprehensive courseware, online interactive courses and short video micro-courses, and the generated teaching content can be automatically adapted to PDF, online course and short video output formats. 9.The adaptive intelligent teaching content generation system of multi-modal large model reinforcement learning of claim 1, wherein, The security and compatibility module comprises a federated learning data encryption unit that uses a federated learning framework to encrypt physiological data, behavior data and privacy data of learners, making data available but invisible to ensure data privacy and security. 10.The adaptive intelligent teaching content generation system of multi-modal large model reinforcement learning of claim 1, wherein, The security and compatibility module further comprises a multi-scene and multi-device adaptation unit, which supports the access of PCs, tablets, mobile phones and intelligent learning terminals, can automatically adapt the screen size and interaction mode of different devices, and can quickly adapt to various teaching scenes of K12 education, higher education and vocational training.

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