63 results about "Personalized education" patented technology

The invention provides a family-school interaction classroom note analysis system based on OCR and a large language model. The family-school interaction classroom note analysis system comprises a note scanning module, an OCR recognition module, a note analysis module and a family-school pushing module. Through linkage of all the modules, automatic analysis of classroom notes, efficient cooperation of families and schools and personalized learning closed loop are achieved, and transformation of education from unified teaching to personalized cultivation is promoted.

The invention belongs to the technical field of education data mining and personalized learning, discloses a learner knowledge cognition level diagnosis method and system based on cross-scale learning performance dynamic modeling, and has higher accuracy in the aspects of learner cognition state prediction and knowledge point difficulty assessment. Through a selective state space modeling mechanism and cross-scale historical learning income feature engineering, cognitive change tracks of students in various learning scenes can be accurately captured; and the robustness, convergence efficiency and long sequence processing capability of the model in learner performance prediction are improved. The method can be widely applied to a personalized education platform, a self-adaptive learning system and an intelligent teaching auxiliary tool, provides accurate student learning state analysis for teachers, optimizes learning path design, and improves the teaching effect.

The invention discloses a personalized education examination management method based on artificial intelligence, relates to the technical field of education examination management, and introduces and fuses teaching program data and teaching calendar data, dynamically calculates teaching progress offset, accurately constructs a time sequence embedding space reflecting an actual teaching progress, and provides a personalized education examination management method based on artificial intelligence. The problem of proposition deviation caused by different teaching progresses of areas, schools or classes is fundamentally solved; the system can automatically sense and adapt to teaching plan changes such as course adjustment and course halt, and the generated test questions are strictly limited in a knowledge point range taught at the current stage, so that the examination evaluation result can truly and effectively reflect the mastering condition of students on the currently learned content, and the reliability and validity of the examination are greatly improved; the dynamically constructed teaching time sequence embedding space is used as a core constraint condition and is deeply integrated into a prompt project and decoding generation process of a large language model, so that accurate and intelligent control on test question generation content is realized.

The invention discloses a nursing health education method and system based on a rule engine, and a medium, belongs to the technical field of medical data processing and analysis, and is used for solving the technical problems that an existing health education scheme is not formulated as a personalized education scheme, and is lack of interactivity with a patient and long-term education effect evaluation and follow-up. The method comprises the steps of determining a health propaganda and education material set of each specialized disease under a corresponding nursing path based on medical health information; extracting a propaganda and education inference rule from the health propaganda and education material set, and building a propaganda and education rule engine according to the propaganda and education inference rule; obtaining full-time health data of a patient to be propagandized and educated, and segmenting the full-time health data through the sliding window to obtain target health data; performing propaganda and education reasoning rule matching on the target health data through a propaganda and education rule engine, and determining a health propaganda and education scheme of the patient to be propagandized and educated; and pushing the health propaganda and education scheme and the corresponding test scheme to the patient to be propagandized and educated.

The invention discloses an adaptive education textbook generation method and device based on a large model, and a medium, and relates to the technical field of intelligent education, and the method comprises the steps: determining a knowledge point set which needs to be expanded currently by a learner through a knowledge graph retrieval and course outline matching algorithm based on a learner portrait data set, and generating a teaching task list; according to the individualized evidence Prompt script, generating individualized teaching content by using a controlled decoding mode of a large language model, performing content consistency verification, and rearranging the content into a customized teaching material; carrying out conflict discovery-retrieval supplement-local regeneration-format recombination on the customized textbook through a ReAct reasoning framework to obtain a high-adaptability education textbook; the teaching material can be continuously corrected and optimized in the use process, so that the generation of the high-adaptability personalized education teaching material which is correct and smooth in teaching, fit and sustainable in evolution is realized.

An apparatus and method for personalization of educational machine learning models. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive input data comprising one or more of a user profile and a request datum, generate, using a return generator, return output based on the request datum, identify, using a machine learning model, attribute data of the input data, generate, using a classifier, one or more classifications for the input data as a function of the attribute data associated with the input data, modify, using a natural language processor, the return output as a function of the user profile and one or more classifications assigned to the input data to generate user specific output, and display, using a downstream device, the user specific output.

The invention relates to a knowledge tracking model research method integrating difficulty perception and memory enhancement. According to the method, a graph attention network coding exercise-knowledge point topological relation is constructed, and a difficulty embedding layer and a gating fusion mechanism are combined to realize joint characterization of difficulty features and a topological structure. The difficulty coefficient is innovatively introduced as a dynamic regulation factor of attention weight in memory network updating, the memory intensity of high-difficulty exercises is enhanced, and the discrimination and topological consistency of knowledge representation are improved through a multi-task optimization framework integrating graph structure loss and contrast loss. According to the method, challenges of exercise difficulty perception and knowledge point topological relation modeling are effectively solved, experimental results show that the AUC of the model reaches 0.93 (improved by 7.8% compared with traditional BKT) on an ASSIST2009 data set, the AUC of the model reaches 0.90 (improved by 0.21 compared with KSGAN) on an EdNet data set, objective indexes and teaching scene verification show that the method has remarkable advantages in the aspects of knowledge point correlation modeling and knowledge tracking, and the method is suitable for popularization and application. The method provides an innovative solution for personalized education, and has technical breakthrough and industrial application values.

The invention discloses a risk early warning monitoring and personalized AI education intervention recommendation system for student spoofing and campus violence, and relates to the technical field of campus safety prevention and control. According to the invention, early warning and dynamic tracking of student spoofing and campus violence risks are realized, and the timeliness and accuracy of risk identification are improved; a multi-level risk summarization and visual report generation function is adopted, so that an education manager can quickly master the overall campus safety situation and formulate personalized intervention measures according to different risk levels, thereby effectively reducing the occurrence rate of deception and violence events and creating a safer campus environment; through cooperative operation of AI education intervention strategy generation and a Web push module, real-time identification and accurate intervention of campus spoofing risks are realized, multi-dimensional data such as student individual risk scores, behavior history and environmental factors are integrated, and a highly personalized intervention plan is generated.

The invention discloses a children personalized education recommendation method based on multi-modal data analysis. The method comprises the following steps: step 1, data acquisition; step 2, a cross-modal attention mechanism; step 3, dynamic recommendation; based on multi-modal data fusion and an advanced algorithm model, the limitation that traditional education recommendation depends on subjective judgment is avoided, and a scientific and quantitative basis is provided for children education recommendation. Through a dynamic classification mechanism and a hierarchical recommendation strategy, individual differences and interest tendencies of children can be accurately matched, and waste of educational resources and depression of children's interests are effectively avoided. The characteristics of the children and the family environment factors are integrated, comprehensive consideration of the growth of the children is realized, and the applicability and the effectiveness of the recommendation scheme are improved.

A system and method for guiding and constraining an artificial intelligence (AI) engine to create and utilize a pre-generated content pool to provide adaptive and personalized learning to users is disclosed. Parsing a user request to identify content requirements and applying an adaptive content selection algorithm that evaluates multiple parameters, including user ID, curriculum standards, content types, and user data. An automated content pool management system maintains a dynamic repository of content aligned with these parameters. Machine learning algorithms enhance and personalize the content pool based on evolving user needs. A large language model (LLM) is employed to generate a guiding prompt that directs the AI engine to retrieve relevant content from the pool. This prompt-driven interaction enables accurate delivery of personalized educational content aligned with user-specific learning goals. The system supports real-time adaptability and individualized content delivery, thereby enhancing the efficacy and responsiveness of AI-powered learning environments.

The invention discloses a personalized education method and system based on artificial intelligence, and relates to the technical field of artificial intelligence and intelligent personalized education, and the method comprises the following steps: 1, carrying out the cognitive breakpoint modeling, and outputting a student cognitive breakpoint three-dimensional model; 2, dynamic cognitive coupling is carried out, and a qualified cooperation unit is output; according to the invention, through cognitive breakpoint three-dimensional modeling, breakpoints and complementary advantages of students are accurately captured, and dynamic cognitive coupling is combined to establish an advantage-based artificial intelligence personalized education method and system, namely a weak point complementary cooperation unit of the artificial intelligence-based personalized education method and system, so that the weakness compensation efficiency is improved, and the intervention period is shortened; the cooperation process is converted into reusable cognitive bridge resources through multi-modal knowledge distillation, and the adaptation degree is marked, so that repeated generation of resources is reduced, and the computing power is saved; and through a symbiotic task, collaborative real-time calibration and a reflection task, knowledge breakpoints are supplemented, the collaborative and knowledge migration ability is cultivated at the same time, and a core literacy cultivation target is conformed.

The invention discloses a student score prediction method and system based on deep learning, and relates to the technical field of education, and the system comprises a data collection module, a data preprocessing module, a model training and optimization module, a prediction and analysis module, a user interface (UI) module, and a feedback and iteration improvement module. And a plurality of deep learning models are used to learn a complex relationship between features and scores, and accurate prediction of future academic scores of students is realized. According to the student score prediction method and system based on deep learning, a set of student score prediction system which is high in precision, explainable and dynamically evolved is constructed by fusing multi-source education data and a deep learning technology, so that the problems of low prediction precision and insufficient data utilization of a traditional method are solved; in addition, timeliness and accuracy of academic prediction are effectively improved, personalized education and educational resource optimization configuration are assisted, and data driving and scientization of educational decision are promoted.

The invention discloses an active multi-mode education agent system and method, and belongs to the technical field of artificial intelligence education. The system adopts an end-cloud collaborative architecture and comprises two parts, namely end-side equipment and a cloud micro-service system. The end side device is responsible for collecting multi-modal data (emotion data, behavior data and content data) of a learner in real time and carrying out preprocessing and feature extraction. The cloud micro-service system comprises an API gateway service, a state reasoning service, an active response generation service, a multi-modal content generation service and a TTS service, and is used for processing and analyzing the multi-modal data and generating a personalized active response. According to the method, the confusion state of a learner can be actively perceived, the cognitive state is inferred based on a multi-modal data fusion algorithm, personalized education content is generated according to the cognitive state, a perfect service degradation strategy is provided, and the reliability and learning effect of the system are improved.

The invention discloses an educational guidance scheme generation method and device based on causal inference, relates to the technical field of data mining, can be applied to the educational field, and mainly aims to solve the problem that a personalized educational guidance scheme lacks pertinence and effectiveness. The method mainly comprises the steps of constructing a feature marker causal relationship matrix; obtaining a target feature matrix, a target learning state marking matrix and real-time learning feedback data of the target education object; performing high-dimensional embedding representation learning on the target feature matrix to obtain a high-dimensional embedding representation matrix, and fusing the high-dimensional embedding representation matrix with the target learning state marking matrix and the target feature matrix to obtain a feature marking mapping matrix; generating an initial education guidance scheme through a high-dimensional geometric variational generation network according to the high-dimensional embedded representation matrix and the feature mark mapping matrix; and optimizing the initial education guidance scheme according to the real-time learning feedback data to obtain a target education guidance scheme. The method is mainly used for generating education guidance schemes.

The invention relates to the technical field of personalized education, and discloses a personalized learning monitoring system based on user behavior data, and the system comprises a data collection module which is used for collecting the learning behavior data and physiological data of a target user, and integrating the interaction log data in a learning platform; the learning target formulating module is used for formulating a personalized learning target system containing short-term and long-term learning; the self-evaluation and feedback module is used for guiding the target user to perform self-evaluation regularly and assisting the target user in knowing the current learning progress; the emotion monitoring module identifies the current emotion fluctuation of the target user; and the dynamic task adjustment module is used for dynamically adjusting the difficulty, the content and the rhythm of the learning task based on the personalized learning target system, the current learning progress and the current emotion fluctuation, and generating a real-time adaptive learning task flow. The state change is automatically identified, the matching task is pushed, and the cognitive burden and the management cost are greatly reduced.

The invention provides an educational information classification system based on big data, and relates to the technical field of educational information classification, which is characterized in that keywords of educational information are extracted through a natural language processing technology, preliminary labeling is carried out in combination with a teaching outline, a label set is expanded by utilizing an educational domain knowledge graph, and a label association network is generated; and classifying the education information into matched knowledge point categories through semantic similarity analysis. Therefore, the accuracy and scientificity of education information classification can be improved, the hyponymy and semantic association between knowledge points can be fully mined, the optimization of the organization and distribution of teaching resources is facilitated, and the implementation of personalized education and efficient teaching practice is supported.

A personalized content generation method and system to guide and constrain an AI engine to generate personalized educational content for accelerating test preparation of a user based on the performance of a user in a mock test on an online learning platform is disclosed. The method starts with presenting a mock test related to a specific curriculum. User performance data, including mastery levels on various topics, is collected and analyzed. The data is mapped to historical exam data, identifying weak areas of the user. The system then determines the importance of these weak topics based on their frequency in past exams and their relevance to curriculum standards. The system generates prompts for the AI engine, guiding and constraining to create personalized educational content focused on these areas. The personalized content is delivered to the user in real-time, targeting topics where user's mastery level is low but is significant for exam.

This invention discloses a method for supporting individual educational development based on innate biometric assessment. The method first collects fingerprint image data, which is considered lifelong and unchanging, from individuals. Second, it preprocesses the fingerprint images and extracts quantified feature vectors. Then, it inputs these feature vectors into a pre-trained biometric-educational trait association model to obtain quantified assessment scores for the individual across multiple preset educational trait dimensions. Next, combining the assessment scores, individual age, and developmental goals, a personalized initial educational development plan is generated by matching rules through an educational plan generation rule engine. This invention uses stable biometrics as an objective benchmark and, through a computable model and rule engine, achieves a complete technical process from innate trait assessment to the generation and optimization of long-term, dynamic, and personalized educational support plans, solving the technical problems of traditional methods that rely on unstable acquired assessments and lack a long-term reference system.

Provide a system. 【Solution means】 Means for collecting children's personality, interests, and past behavior data, Means for analyzing the collected data and selecting the most suitable activities for children, Means for proposing the selected activities to the user, Means for analyzing the feedback obtained after the implementation of the activities, Means for updating activity proposals based on the feedback, Means for proposing an individualized education plan, Means for further improving the proposed content based on the execution results of the plan, A system including the above.

The invention relates to the technical field of medical treatment and health information, in particular to a visual interaction-based self-injection success rate evaluation method for elderly diabetics, which comprises the following steps of: constructing an aging-suitable structured video teaching module, presenting an injection process in stages and embedding a standardized question node; a closed loop type Teach-back interaction feedback process is deployed; analyzing the multi-modal behavior data by using a computer vision and natural language processing technology, and extracting key operation parameters and cognitive integrity indexes; establishing a dynamic scoring model, and quantitatively evaluating the self-injection capacity in combination with the link weight and a standard threshold value; and personalized education intervention and post-discharge periodic follow-up visit management are implemented based on an evaluation result. Objective quantification and continuous education of the self-injection skill of the elderly patient are realized, the education effect is improved, and the operation related risk is reduced.

The invention discloses a data processing method and device for personalized education learning, which comprises the steps of entering an artificial intelligence and curriculum teaching analysis application platform through a preset account and starting personalized education learning tutoring operation of corresponding contents, and is characterized in that the method comprises the following steps: selecting in a learning content selection interface according to own requirements; inputting keyword information, and generating teaching script information of teaching tutoring; and displaying dialogue information of personalized education learning according to a preset standard database. The device comprises a learning content response module, a keyword response module, a script response module, an intelligent learning guide response module and a dialogue response module. Compared with the prior art, the method has the advantages that the knowledge display content is adjusted according to the requirements of the students, personalized education learning guidance corresponding to the requirements of the students is generated to meet the actual requirements of the students, and the method is simple, convenient, high in practicability and good in application prospect.

An object of a system according to an embodiment is to provide individualized education adapted to a learner and to realize efficient learning support.SOLUTION: A system according to an embodiment includes a AI tutor, a question answering unit, a teaching material providing unit, a teaching material development supporting unit, and a visualization providing unit. The AI tutor is loaded with the generated AI. The question answering part performs question answering adapted to the learner. A teaching material providing part provides a teaching material adapted to the learner. A teaching material development support part supports teaching material development. The visualization providing unit provides the visualized teaching material.SELECTED DRAWING: Figure 1

A generative artificial intelligence (GenAI) driven education platform provides personalized learning experiences by leveraging large language models (LLMs) and secure data storage. The platform refines training objectives, generates context-aware scenarios, and adjusts difficulty based on user performance. User data—such as past performance, roles, and preferences—remains on-premises for privacy, supporting dynamic content adaptation without exposing sensitive information to external servers. Contextual materials, including news articles or internal documents, enhance scenario realism. Real-time feedback pinpoints knowledge gaps, while iterative updates to test cases continually evolve training modules. Applications include phishing simulations, compliance training, professional exam preparation, and more. Interactive components, such as multimedia quizzes and immersive simulations, are rendered through a customizable interface. This hyper-personalized approach boosts engagement, retention, and relevance by incorporating granular user data and ongoing performance metrics. Ultimately, organizations gain a robust, adaptive framework to deliver secure, high-impact learning across academic, corporate, and professional environments.

The invention discloses an education content personalized recommendation system based on deep learning, and relates to the field of education technology and deep learning, and the system comprises a multi-modal data processing module which collects and processes four types of data, and forms a standardized vector; the target disassembling and mapping module is used for disassembling a macroscopic target into microscopic sub-targets and generating a list and a mapping table; the ability and interest modeling module evaluates ability and constructs an interest model; the resource recombination recommendation module screens and pushes combined resources; the feedback iterative optimization module collects feedback, updates data, optimizes parameters and reversely transmits the parameters; according to the method, a personalized learning path is constructed through a multi-modal data processing and target dynamic disassembly technology, and the requirements of learners are accurately evaluated in combination with multi-dimensional capability quantification and interest modeling; and meanwhile, a resource intelligent recombination recommendation mechanism is adopted, a comprehensive and high-timeliness resource list is generated based on an adaptation degree algorithm, and a feedback closed loop is formed to continuously optimize system parameters, so that the learning experience and effect are remarkably improved.

The invention discloses a new problem scene-oriented multi-modal large model knowledge tracking method, which is characterized in that modal level fusion is realized by adopting a perception coordination module, vision, text and context input are aligned, and rich problem characterization is constructed; problem-level fusion is realized through a cognitive integration module, multi-modal clues and learner interaction history are dynamically integrated, and a potential knowledge state is deduced under the condition of not depending on a question ID; and through an adaptive generalization module, representation is dynamically generated for unseen problems based on semantic similarity, and zero sample migration is realized. Compared with the prior art, the method has the advantages that zero sample knowledge without problems is tracked, so that the MKT-LLM can truly realize zero sample generalization to open and real world education content, and the limitation that an existing knowledge tracking method cannot be generalized to a new problem is effectively solved; the method can be widely applied to an intelligent education system, a self-adaptive learning platform and an interactive tutoring system, and provides technical support for personalized education in an open learning environment.

The application provides a kind of autism children classroom behavior analysis method and analysis system, and the system will multi-view collection, stable key point detection, high-precision multi-view three-dimensional reconstruction and deep behavior analysis mode, through the construction of projection matrix, weighted calculation, filtering abnormal points, algorithm optimization and error elimination etc. Link realizes organic integration, forms a complete and efficient system, greatly improves the accuracy of autism children classroom behavior analysis, provides solid and scientific basis for individualized education intervention, and brings more valuable analysis results.

The invention relates to a question generation method and device, equipment and a storage medium. According to the main technical scheme, the method comprises the following steps of: obtaining a question text and a question solving path of a seed question, determining a knowledge dimension score, a cognitive dimension score and an operation dimension score of the seed question according to the question text and the question solving path of the seed question, and obtaining a knowledge dimension score, a cognitive dimension score and an operation dimension score of the seed question according to the knowledge dimension score, the cognitive dimension score and the operation dimension score of the seed question; and obtaining a comprehensive difficulty score of the seed question, generating a latest question according to the seed question, the comprehensive difficulty score of the seed question, the knowledge point information of the seed question and the target difficulty of the seed question, and generating a question matched with the target difficulty of the seed question according to the latest question and the target difficulty value of the seed question. According to the invention, the difficulty of the questions can be objectively evaluated, and the questions meeting specific difficulty requirements are generated, so that real personalized education is realized.

The invention relates to the related technical field of machine learning and education data mining, and provides a personalized education resource recommendation method and system, and the method comprises the steps: obtaining an interaction record, carrying out the preprocessing, carrying out the semantic feature extraction for the course description text of each learning resource, and carrying out the frequency domain-spatial domain parallel feature enhancement processing, weighting is carried out based on the self-adaptive gating coefficient weight; and finally, through the trained depth prediction model based on the neural factorization machine, predicting to obtain a recommendation score of the user on the learning resources, and further obtaining a recommendation result of the learning resources. According to the method, a neural factorization machine architecture is combined with innovative designs such as a parallel frequency-space domain fusion module, a self-adaptive gating mechanism and a global-local feature enhancement mechanism, deep modeling of student historical interaction behaviors and course content semantics is realized, and high-precision personalized resource recommendation is provided.

The invention provides a personalized education content dynamic generation method and system based on generative AI, and the method comprises the steps: collecting the learning behavior data of a student, and coding the learning behavior data into a behavior vector; on the basis of the behavior vector, constructing a cognitive state vector containing knowledge point mastery degree and error type cognitive semantics; inputting the cognitive state vector into a pre-constructed multi-target content generation intention function, and generating a structured teaching content generation instruction; and inputting the teaching content generation instruction into a generative AI model, and controlling the generative AI model to output personalized teaching content matched with the instruction. According to the invention, the controllability, responsiveness and teaching guide value of personalized teaching of the generative AI system in an education scene are improved.

We provide the system. [Solution] A means of creating personalized educational programs using generative artificial intelligence, A means of providing learning materials while adapting to multiple languages ​​in real time, A means of presenting supplementary information to deepen understanding through natural language interaction with the user, A means of recording educational progress and providing individual feedback, A means of providing a community feature that allows users to share knowledge with each other, A system that includes this.