1422 results about "Coursework" patented technology

The invention discloses an aerospace knowledge large model construction method based on progressive knowledge injection and retrieval enhancement generation, which comprises the following steps: collecting and preprocessing multi-source knowledge data, and constructing an aerospace field knowledge base; the method comprises the following steps: based on a DeepSeek-R1-8B model, carrying out continuous pre-training by adopting a three-stage progressive mixed course learning framework; performing supervision fine tuning through instruction data and a knowledge guiding mechanism to complete basic construction of a large model; a retrieval enhancement generation module is constructed, a'retrieval-filtering-generation 'process is formed, and the knowledge coverage and fact consistency of the model are improved through deep fusion of real-time retrieval and generation reasoning; and through a multi-dimensional quantitative index and a dynamic test mechanism, the performance of the model is evaluated, and a complete evaluation ecology from data construction to feedback optimization is formed. According to the method provided by the invention, deep integration and efficient application of knowledge in the aerospace field are realized through multi-stage collaborative optimization.

The invention discloses an online course learning management method and system based on a knowledge graph, and the method comprises the steps: S1, analyzing course data, extracting knowledge points and an association relationship, and generating an initial knowledge graph signal containing knowledge point nodes and association edges; s2, collecting user interaction behavior data and extracting behavior features to generate behavior feature vector signals associated with knowledge point nodes; s3, detecting an abnormal learning mode, triggering a knowledge graph weight updating signal, and dynamically adjusting the associated edge weight; s4, updating the signal according to the initial knowledge graph signal and the knowledge graph weight; and S5, responding to the path optimization signal, and generating an interface sorting adjustment signal and a content push signal. According to the air quality intelligent monitoring system based on sensing data feedback, the problem that a traditional online course management system is single in data acquisition dimension and static and solidified in knowledge dependency relationship can be solved.

The invention discloses a teaching data management method and system based on artificial intelligence, and the method comprises the steps: obtaining a standardized time series data stream according to a heterogeneous data stream generated by a multi-source teaching platform in real time; based on the standardized time sequence data stream, performing classified encryption on the teaching data through a dynamic hierarchical storage strategy based on attribute-based encryption to obtain a security hierarchical storage topological structure; according to a user query request and a teaching scene label, extracting a target data set from the security hierarchical storage topological structure to obtain an enhanced multi-modal teaching data set; based on the enhanced multi-modal teaching data set, generating an interpretable teaching mode graph through a dynamic sub-graph evolution algorithm; and according to the teaching mode map and the real-time teaching feedback data, generating a personalized teaching recommendation strategy through a course-learner dual-channel adaptive recommendation model. According to the embodiment of the invention, the utilization efficiency of teaching resources can be improved, and personalized and intelligent teaching recommendation and decision can be realized.

The invention relates to the technical field of online teaching management, and discloses an online teaching optimization method and system based on emotion recognition, and the method comprises the steps: collecting the real-time emotion data of a learner through a multi-modal sensor, carrying out the fusion of a graph convolutional neural network to generate a feature vector, and optimizing a teaching strategy parameter through a meta-reinforcement learning model, the dynamic course generation model combines an improved genetic algorithm and a knowledge graph to optimize a teaching content sequence, and the hierarchical teaching control model realizes knowledge path planning, interactive adjustment and learning state evaluation, and generates a teaching control instruction. According to the method, personalized online teaching optimization is realized, the state of a learner can be accurately grasped, the teaching interaction mode is optimized, the course content is dynamically adjusted, the learning effect is comprehensively and accurately evaluated, the problems that traditional online teaching lacks personalization, the state of the learner is difficult to grasp and the like are effectively solved, the online teaching quality and the learning experience are improved, and the learning experience is improved. And the online education development is promoted.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention relates to a method for constructing a subject textbook knowledge graph by using a large language model, and the method is realized through six steps: firstly, introducing a self-prompt framework, generating relation synonyms, synthesizing samples and sentence variants through three rounds of dialogues, and providing rich semantic guidance for subsequent relation extraction; secondly, guiding a large language model to accurately extract core knowledge point entities from teaching materials, exercises and PPT texts by means of a professional field instruction template; then, respectively extracting an attribute triple and a relation triple of the knowledge points by applying a multi-round dialogue mode and combining with a synthetic sample prompt; then, inputting the extracted triad into a verification module, and ensuring the accuracy through iterative verification; and finally, generating an entity embedding vector by utilizing an MPNet model subjected to subject knowledge fine adjustment, calculating entity similarity through a dynamic weighted pooling mechanism, judging entity pairs with high similarity, performing knowledge fusion if the entity pairs represent the same concept, and otherwise, reasoning a potential missing relationship and complementing the knowledge graph. According to the method, the knowledge graph of the course of the specific subject can be automatically constructed from the unstructured text efficiently and accurately, and powerful support is provided for teaching and learning of related subjects.

The invention provides a wisdom education method and system based on student behavior sequence recommendation, and relates to the technical field of wisdom education. According to the method and the system, through an AI autonomous identification knowledge graph construction and innovative knowledge fusion method, a course knowledge system is structurally and hierarchically displayed, and a knowledge framework is clearly presented, so that knowledge graph framework construction of all-platform courses is facilitated, pre-repaired courses and subsequent courses are efficiently associated, and the construction efficiency is improved. And a clear and complete course learning path is provided for students. By adopting a recommendation algorithm based on a student behavior sequence, combining SASRec and introducing a self-attention mechanism of a sparse attention module, behavior data of students can be deeply analyzed, a learning path is adjusted according to real-time learning data, and personalized learning suggestions are provided for the students. According to the method and the system, multi-dimensional functions such as knowledge graph construction, a student behavior sequence recommendation algorithm and intelligent learning path optimization are integrated, and the method and the system have wide coverage and strong comprehensiveness.

The invention provides a talent cultivation recommendation method based on federal learning and natural language processing, and the method comprises the following steps: processing multi-source heterogeneous data, and obtaining standard data; constructing a talent knowledge graph capable of performing incremental updating; performing anonymization and desensitization processing on the sensitive data, and performing joint modeling by using a federated learning framework on the premise of not sharing original data to obtain a local model; talent portraits and post portraits are generated, and a talent matching model is obtained in combination with multiple target factors and a dynamic weight preset matching algorithm; continuously monitoring the similarity difference between the talent portrait and the post portrait, and dynamically generating a course optimization scheme and a post optimization scheme; and performing visual explanation or keyword description on the recommendation process. According to the method, matching precision and efficiency can be improved, data islands can be broken, privacy security can be protected, market demand changes can be dynamically adapted, personalized demands of multiple parties can be met, and accurate connection between education and employment can be promoted.

A virtual reality (VR)-based system for improving students' reading comprehension and writing skills. The system includes: a VR headset with an integrated eye-tracking system, high-resolution stereoscopic displays and spatial audio output, where the eye-tracking system detects reading patterns, fixation points and saccadic movements to assess reading engagement; a pair of motion tracking gloves with embedded pressure-sensitive capacitive sensors and haptic feedback actuators, allowing students to work with virtual reading material, perform handwriting exercises, and receive tactile reinforcement of text-based interactions; an adaptive learning module based on artificial intelligence (AI) that is configured to dynamically adjust reading complexity and writing exercises, responding to real-time metrics for interaction with students, with the AI ​​module using deep learning-based natural language processing (NLP) models to analyze reading fluency, written coherence, and syntactic complexity; a speech recognition and synthesis subsystem comprising a neural network-based acoustic model and a phoneme segmentation processor, wherein the subsystem assesses pronunciation accuracy, fluency, and articulation deviations by analyzing phonetic waveforms of spoken responses; a multimodal interactive interface that enables students to interact with virtual reading environments via a combination of hand tracking, voice commands and eye-controlled text selection, with the interface allowing students to highlight, comment on and orally summarize text content; A real-time dashboard for teacher monitoring, which is communicatively connected to the VR system. The dashboard offers educators detailed analyses of student comprehension patterns, writing progress, and AI-generated adaptive learning recommendations; and a cloud-integrated content management system (CMS) configured to store AI-curated reading materials, personalized writing assignments, and NLP-processed insights into student performance, with the CMS enabling adaptive curriculum adjustments based on students' learning trajectories.

The invention provides a multimodal reasoning method based on a Monte Carlo tree and dynamic retrieval. The method is applied to an artificial intelligence assistant-assisted teacher paper judgment scene. Specifically, the framework comprises a unified retrieval module, wherein the retrieval module comprises a mixed modal retrieval corpus and a multi-modal retrieval module; the multi-modal retrieval module comprises two retrieval modules, namely a text retrieval module and a cross-modal retrieval module, key problem solving insights are selected by adopting knowledge concept filtering, and progressive multi-modal reasoning annotations of multi-modal reasoning are automatically obtained by utilizing Monte Carlo tree search and an active retrieval mechanism for data annotation; and gradually aligning a process reward model through course process reward modeling in two stages by utilizing the annotation data so as to realize fine-grained verification of multi-modal large model reasoning. An MCTS algorithm and an active retrieval strategy are combined to improve multi-modal reasoning. The framework can automatically obtain high-quality step-by-step reasoning annotations, so that a process reward model is aligned step by step, and finally reliable automatic multi-modal reasoning verification is realized.

The invention discloses an intelligent course arrangement method based on multi-objective optimization and deep learning, and relates to the technical field of intelligent course arrangement, and the method comprises the steps: collecting multi-source data, and constructing a spatial-temporal feature matrix; mining a historical class schedule conflict mode by using a knowledge graph and constructing an association relationship model; newly-added course constraints are converted into vectors, and a comprehensive constraint model is formed through BERT model analysis. A graph neural network is utilized to capture association of courses, teachers, classrooms and time periods, a comprehensive constraint model is combined to generate candidate schemes meeting hard constraints such as teacher time conflicts and classroom capacity and partial soft constraints such as pre-repair relations and student course selection conflicts, and an optimal course arrangement scheme is obtained through optimization. The problems that an existing system is insufficient in multi-source data application, difficult in association construction, inaccurate in constraint analysis, difficult in complex contact capture, insufficient in multi-target consideration and the like are solved.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

The present invention discloses an online evolutionary algorithm and training platform for intelligent agents based on self-game, including: a task and environment module, an intelligent agent training module, a strategy evaluation module, a data management module and a user interaction module. The task and environment module dynamically defines training tasks and environmental parameters through a task generation unit and an environment simulation unit. The intelligent agent training module includes an initial strategy generation unit, a course learning unit and a self-game optimization unit to work together to strengthen the learning framework. The strategy evaluation module tests the advantages and disadvantages of intelligent agent strategies through performance evaluation, diversity analysis and comparative analysis. The data management module records and analyzes training data to manage the historical strategy library. The user interaction module provides task configuration, training monitoring and result visualization functions. The present invention improves the adaptability and robustness of intelligent agents by introducing a self-game mechanism and dynamic opponent generation, and is suitable for strategy optimization and training in complex collaboration and confrontation scenarios.

The invention discloses an intelligent personalized learning path recommendation system, and relates to the technical field of learning systems, and the system comprises a multi-source data collection module which is used for synchronizing behavior data of students in a cross-platform learning scene in real time; the cognitive feature analysis engine comprises a style recognition sub-engine and a demand prediction sub-engine to predict the potential learning demand intensity of students for unmastered knowledge points and generate a priority list comprising knowledge gaps; the personalized path generator generates a three-dimensional path plan; the proportion of guided questioning, example demonstration and autonomous exploration is automatically configured according to knowledge difficulty; the dynamic self-adaptive adjustment unit is used for designing a reward function including short-term progress speed and long-term ability growth potential by taking real-time performance of students as a state space and taking path adjustment action as a decision space based on a reinforcement learning framework; and a double-loop feedback mechanism is realized. According to the method, the dimension limitation of traditional learning analysis is broken through, the difficulty of stiffness of a static course template is broken through, and meanwhile, the semantic gap of subject cognition is broken through.

The invention provides a personalized teaching method and system based on generative artificial intelligence. The method comprises the following steps: constructing a knowledge graph and a teaching strategy template library of a target course; constructing a feature enhancement matrix; calculating node activation intensity, generating a plurality of candidate learning paths through a beam search algorithm, and screening an optimal learning path in combination with a preset teaching strategy template; using the generative model to generate standardized teaching content adaptive to the teaching strategy; dynamically optimizing a teaching scheme through a neuroplasticity model; and carrying out teaching adjustment based on the optimized teaching scheme. Standardized teaching content is generated by utilizing the generative model, the teaching content quality is improved, the teaching content is closely matched with a learning path and a teaching strategy of a learner, meanwhile, a teaching scheme is optimized through the neuroplasticity model, teaching parameters and cognitive resource allocation are adjusted in time according to real-time feedback and cognitive states of the learner, and the teaching efficiency is improved. Limited cognitive resources are reasonably distributed to key parts of teaching contents, and the teaching effect is improved.

The invention discloses a large language model progressive field fine tuning and knowledge fusion method for shield engineering. The method comprises the following steps: constructing a layered shield training course containing a wide-area academic theory and a proprietary enterprise construction method; parallelly training a plurality of physically isolated parameter efficient adapters based on the frozen base; performing singular value decomposition on the adapter, extracting a geometric feature subspace representing knowledge distribution, and calculating a conflict correlation degree; based on this, a uniform adaptation mechanism of resource awareness is constructed. The mechanism not only can generate a static fusion model for conflict removal, but also can dynamically activate a specific rank slice of an adapter through a routing network based on real-time hardware resource budget (video memory / FLOPs) and geometry-resource signature. According to the method, multi-source knowledge is reserved, and adaptive dynamic scheduling of edge hardware resources by model reasoning is realized.

The invention discloses a course session recommendation method and device, equipment, a storage medium and a program product, and the method comprises the steps: carrying out the channel splicing of a semantic embedding matrix of course text description data and a user behavior feature matrix, and generating a multi-dimensional feature matrix; constructing an adjacent graph and a hypergraph between courses according to the multi-dimensional feature matrix, performing graph convolution operation based on the adjacent graph and the hypergraph, and generating a target embedded representation vector of each course node; generating an attention weight of each course node, performing weighted aggregation on the target embedded representation vector based on the attention weight, and performing course session recommendation based on the target embedded representation vector after weighted aggregation; according to the method, the curriculum semantic features and the user behavior features are fused, so that the feature expression ability of curriculum nodes is effectively improved, local neighborhood features between curriculums and global features in a hypergraph structure are captured, dynamic changes of user interests are accurately captured, and accurate curriculum session recommendation is achieved.

Systems and methods for developing personalized instructional content include a computing device using software modules which capture data about subjects, teachers, and learners, train artificial intelligence models based on the acquired data, use the artificial intelligence models to generate instructional content personalized to an instructor and / or learner based their input, compare the generated instructional content to vetted sources and correct errors, and cause the processor to output a personalized instructional course including the updated generated instructional materials in the format capable of display to an individual learner.

The invention discloses a progressive topic generation method based on semantic analysis and a knowledge graph. Performing preprocessing and semantic analysis on the question setting demand text, and extracting a necessary keyword set corresponding to the core knowledge points and an optional determiner set corresponding to the additional conditions; carrying out concept mapping in a college professional knowledge graph and associating with a course outline, constructing a hierarchical semantic constraint framework containing hard constraint and soft constraint, and carrying out consistency detection; adopting reverse index hard matching recall and knowledge graph soft extension recall to obtain candidate materials, and inputting the candidate materials into a field fine-tuning large language model to generate candidate questions; reordering is performed through multi-target learning ordering, teaching logic verification and quality evaluation are executed, and final questions are output; user feedback is received to form an incremental sample, and the generation model and the sorting model are updated, so that the accuracy, diversity and controllability of question generation are improved, and closed-loop optimization is supported.

The invention discloses a course resource recommendation method and device based on an improved state space model, equipment and a medium, and relates to the technical field of learning resource recommendation, and the method comprises the steps: collecting user course resource data, obtaining and preprocessing an interaction sequence, mapping the user interaction sequence to a low-dimensional vector space, and inputting the low-dimensional vector space into the improved state space model; the target interest course is matched with the candidate course resource set, the recommendation score is calculated, and the recommendation list is generated, so that the calculation complexity is reduced, the user interest is accurately captured, and the efficiency and accuracy of course resource recommendation are improved.

The invention discloses a teaching management method and system based on electronic technology courses, and relates to the technical field of teaching management, and the method comprises the steps: collecting and preprocessing multi-source data, constructing an interactive network diagram of students in courses, and carrying out the classification of student groups; constructing a knowledge graph in combination with the course data and the learning behavior data, and initializing a personalized learning path based on the knowledge graph and student group features; and learning resources of different paths are recommended through a multi-view attention mechanism. The multi-level learning path constructed through the knowledge graph ensures that the students grasp knowledge step by step, can adapt to individual differences of the students, performs resource recommendation by using a multi-view attention mechanism, and can realize high-matching-degree resource pushing on the basis of capturing individual features of the students, thereby effectively improving learning efficiency and resource utilization rate, and improving learning efficiency. The dynamic optimization of the learning path is realized, and the adaptability and pertinence of teaching management are remarkably improved.

According to the student personalized learning path generation method based on the neural network, a data acquisition module is deployed on a teaching platform, a learning terminal and an interactive interface, and multi-dimensional data such as student learning duration, interactive frequency and wrong question records are acquired in real time. A knowledge graph is constructed based on a course outline and a prior textbook structure, and a graph neural network GNN is utilized to dynamically adjust association weights of nodes and edges of the graph neural network GNN to reflect weak knowledge areas of students. A Bi-LSTM encoder is constructed for the collected data to generate a behavior feature vector, and the behavior feature vector and a knowledge graph node embedded vector are aligned and fused through an attention mechanism to obtain student personalized multi-modal representation. And taking the representation and the knowledge graph node weight as an environment state, selecting a next knowledge point to be learned as an action space, designing a reward function by integrating multiple indexes, updating a target by using a deep Q network, and training a strategy network to generate a personalized learning path. The trained strategy is deployed as micro-service, a path is displayed according to knowledge module sorting, and related parameters are iteratively updated according to student feedback to improve the recommendation effect.

The invention relates to the technical field of education management, in particular to a learning intelligent course arrangement method and system based on time parameters, and the method comprises the following steps: obtaining student task records, analyzing task concentration degree and behavior fluctuation, obtaining cognitive load distribution, counting teacher teaching frequency and time matching degree, screening preference time periods, and constructing an adaptation table. Extracting curriculum attributes and front nodes, calculating priorities, generating a score table, identifying conflicting curriculums, matching available time periods, obtaining curriculum distribution, comparing student loads with teacher preferences, and screening abnormal curriculum arrangement time periods. According to the method, cognitive load distribution is constructed by analyzing multi-cycle task behaviors of students, time period preference is identified in combination with teaching activeness of teachers, resource matching precision is improved, curriculum arrangement fusion priority weight and neutral position intensity cross scoring is performed, time periods are accurately screened, conflicts are avoided, and cognitive load and preference deviation curriculums are identified. High matching of courses and behavior rhythms is realized, and adaptability and course arrangement scientificity are enhanced.

The invention belongs to the field of artificial intelligence and video anomaly detection, and discloses a weak supervision video anomaly detection method based on curriculum learning and contrast learning strategies. A course learning strategy is designed by simulating a human learning mode, an anomaly detection model is trained through a contrast learning method based on a normal prototype, and the method comprises the following steps: preprocessing a video and extracting an optical flow image; video appearance and motion features are extracted and fused; inputting into a pre-classifier to obtain a pseudo label; judging the anomaly detection difficulty according to the false label, and dividing a simple set and a difficult set; initializing a model by using a simple set; and performing two-stage training by using the difficult set, and updating model parameters through normal prototype contrast learning. According to the invention, the discrimination capability of a weak supervision video anomaly detection model can be effectively improved, the problems of model deviation, fuzzy positive and abnormal boundaries and insufficient model discrimination capability under the influence of noise in the prior art are solved to a great extent, and the accuracy of video anomaly detection is remarkably improved.

The invention provides a course reconstruction method and system based on a knowledge graph, and relates to the technical field of intelligent education. The method comprises the following steps: collecting course resource data and student learning behavior data; constructing a knowledge graph fusing multiple courses; generating a personalized learning path based on the atlas and the behavior data; arranging experiment training tasks according to a path and collecting feedback data to update a graph state; and converting the student training result into a technical manuscript in a structured manner and accessing the technical manuscript to a management platform. Closed-loop optimization of teaching resource organization, path recommendation and achievement management is realized.

The invention discloses an online curriculum recommendation method and system based on a large language model, and relates to the technical field of curriculum recommendation. The method is based on multi-level analysis of policy texts, the association between dominant changes and hidden skill requirements is accurately extracted, and the one-sidedness of traditional keyword matching is avoided; curriculum features with attenuation weights are generated through cross-modal alignment, recommendation priorities are dynamically adjusted in combination with policy release time and content update frequency, and recommendation deviation caused by curriculum update lag in the policy transition period is solved; an LLM is used to construct a cross-skill domain incidence matrix, and the secondary influence of policy change is automatically deduced; a virtual interaction data generation module is adopted to simulate a typical user behavior path, the stability of a recommendation system is maintained in the absence of real data, and the model iteration period after policy mutation is shortened.

The invention discloses a personalized course recommendation method and system based on a knowledge graph, and relates to the technical field of course recommendation. The personalized course recommendation method and system based on the knowledge graph comprises the following steps: S1, acquiring user learning data by collecting front-end burying points and background access log data of a learning platform in real time, and preprocessing the user learning data; s2, constructing a behavior data relationship between the user and the knowledge points, evaluating a mastering condition of the user on the knowledge points, and constructing a time sequence knowledge point score set; s3, based on the time sequence knowledge point score set, identifying abnormally mastered knowledge points of the user in the current stage, analyzing whether the courses are worthy of recommendation, and partitioning candidate courses and generating recommended courses; and S4, quantifying the response difference between the recommendation course and the mastering effect, updating the recommendation tag and the user record, and perfecting the recommendation data circulation. The problem that in the prior art, learning effects are difficult to measure, and behavior-driven recommendation circulation is likely to happen is solved.

The invention discloses a dynamic classroom optimization method based on real-time space-time semantic graph tracking, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining first multi-modal data of a current classroom in a classroom teaching process in response to a teaching optimization request; mapping each piece of first multi-modal data into a node in a first space-time semantic graph, generating the first space-time semantic graph based on a preset edge construction condition and a preset edge generation condition, and then inputting the first space-time semantic graph into a preset graph neural network to obtain a first dynamic score vector in the classroom teaching process; and then, based on a preset strategy network, according to the first dynamic score vector and the target score corresponding to the first dynamic score vector, a classroom improvement strategy is generated, real-time sensing and dynamic scoring of the classroom teaching process are realized, a more accurate classroom improvement strategy is obtained, and the classroom teaching efficiency is improved. Therefore, on the basis of improving the real-time performance and comprehensiveness of course teaching evaluation, the teaching scheme can be adjusted in real time, and the classroom teaching quality is improved.

An AI-based system generates personalized educational content tailored to individual students' abilities and needs. The platform analyzes academic standards, curriculum guidelines, and student data including IEP status using natural language processing to build customized profiles. Large language models (LLMs) including GPTs, BERT, and custom models as well as generative adversarial networks (GANs), diffusion models, and other AI algorithms are leveraged to dynamically generate lessons, assignments, recommendations, visual aids, and other educational content adapted for each learner. The system stores output in JSON format for further transformation. Educators can review auto-generated content, provide guiding prompts, and collaborate via social features. Administrative controls enable customization across districts and schools. By leveraging AI to automate personalized content creation, the system reduces teacher workload while improving educational outcomes.

The invention relates to the technical field of educational administration intelligent management scheme design, in particular to an educational administration intelligent management method and system and electronic equipment. Comprising a data acquisition and preprocessing module, an intelligent analysis and decision-making module, a system integration and management module and the like. Data collection is comprehensive, preprocessing is fine, and multiple algorithm mining data values are covered. Intelligent analysis and decision relate to accurate recommendation of student courses, efficient scheduling of courses and classrooms, scientific monitoring of teaching quality and the like. The system can integrate an existing education system and is convenient to manage. According to the scheme, through a series of advanced technologies and innovative designs, many problems of traditional educational administration are effectively solved, and the intelligent level and the comprehensive quality of educational administration are comprehensively improved. In addition, the real-time performance and the accuracy are both considered, the scheme can be adjusted according to real-time data, and it is guaranteed that the management decision always fits the reality. According to the scheme, the educational administration management efficiency and quality are comprehensively improved, and great convenience is brought to educational management.