93 results about "Personalized education" patented technology

In accordance with the present disclosure, systems and methods are provided for providing personalized education to users. the systems and methods may collect, process, and analyze various user information including data from compatible IoT devices using a composite AI model. The systems and methods may generate a personalized education profile for the user using the composite AI model based on the analyzed user information. The systems and methods may generate and deliver personalized education content aided by the use of the composite AI model. the system may also monitor and receive user feedback in real-time for updating the composite AI model, the personalized education profile, and the personalized education content.

The invention provides a student growth archive management method, which comprises the following steps of: performing structured processing on multi-source heterogeneous data of paper archives, electronic documents and audio and video data by adopting a data acquisition and conversion technology according to heterogeneous data characteristics of student growth archive management, performing data cleaning, conversion and integration operation to obtain unified structured digital information, and storing the structured digital information into a database; a foundation is laid for subsequent big data analysis; based on a result of machine learning analysis, a student portrait and a knowledge graph are constructed, a comprehensive and dynamic student personal file is formed through multi-dimensional description of learning tracks, ability characteristics and psychological characteristics of students, entities such as students, courses, teachers and the like and relationships thereof are visualized by utilizing a knowledge graph technology, and a knowledge graph is created. The association relationship and influence factors among the students are mined, and a basis is provided for personalized education and accurate teaching.

The invention belongs to the technical field of graph neural networks, and discloses a personalized education resource recommendation method and system based on a graph neural network, and the method comprises the steps: extracting first coding information of a learner and second coding information of a learning resource from a coding information base; inputting the first coding information and the second coding information into a multi-interest contrast graph attention network to obtain a first feature vector corresponding to the learner and a second feature vector corresponding to the learning resource; multiplying the first feature vector by the second feature vector to obtain the preference degree of the learner and the learning resources for educational resource recommendation; the multi-interest contrast graph attention network is obtained by training sample data of historical learning records and learning resources of the learner as a training set, and the multi-interest contrast graph attention network is used for capturing and understanding interest preferences of the learner by fusing various interest information of the learner and attribute information of the learning resources. The educational resource recommendation accuracy and interpretation can be 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 provides a global college information education consultation method and system based on an AI large model, and relates to the technical field of data processing and artificial intelligence, and the method comprises the steps: collecting global college multi-language official information, carrying out the preprocessing, and constructing a knowledge graph; utilizing the multi-language corpus to finely adjust the pre-training large model; performing intention recognition through natural language consultation, and calling a knowledge graph to retrieve a college list, enrollment standards and associated information; and accurate and personalized education information consultation service is provided through visual display and formatted recommendation. According to the method, the multi-language knowledge graph, the pre-training large model and the artificial intelligence inference engine are combined, so that the intelligent level of global college information query is remarkably improved.

An adaptive learning system for personalized education based on AI, comprehensive: • A real-time engagement analysis unit that analyzes students' cognitive load and attention span as well as their engagement level using biometric and behavioral data, the engagement analysis unit consisting of: • Eye-tracking sensors for monitoring gaze direction, pupil dilation, and blink rate. • Monitoring keystroke dynamics to explore typing speed, pressure, and pauses. • Behavioral interaction analysis - recording cursor movements, timing of responses, patterns in interaction.

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 relates to the field of health management, and discloses a health propaganda and education method and system based on an intelligent system, and the method comprises the steps: collecting environment data and multi-dimensional health data of a service object, and creating a scene perception layer; generating a causal relationship chain by using a preset Bayesian network optimization algorithm; determining an adaptive propaganda and education opportunity of the service object based on the health state and the scene type; constructing a self-management scoring device of the service object, and generating personalized propaganda and education contents of the service object; and creating a multi-scene collaborative propaganda and education platform of the service object and a corresponding effect evaluation system thereof. According to the invention, multi-scene collaborative propaganda and education can be realized, and targeted health propaganda and education contents are provided.

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.

Described herein are systems and methods for modifying educational content. Educational content may be provided to a user. The user's reaction to the educational content may be analyzed. This may be used to determine a content modification model, which may be used to modify the currently displayed educational content, and / or subsequent educational content. In some embodiments, a content modification model includes a taxonomy preference model. In some embodiments, a content modification model includes a toxicity reduction model.

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 relates to the technical field of artificial intelligence, in particular to a multi-round dialogue question generation method for computer personalized education. Comprising the following steps: acquiring textbook and examination outline data, and storing the data in a database; specifying a graph normal form to construct a knowledge graph, extracting knowledge point data in the database data, and constructing a knowledge tree; generating question-answer pairs for the knowledge points by utilizing a Self-QA strategy and combining a Bloom classification method; obtaining a current knowledge point range, generating a question of a current learning progress, performing weight incremental processing according to answer data, and generating a guiding question; aiming at the dialogue data, updating a learning progress; by means of the mode, the next related question or guiding question is generated according to the knowledge point mastering situation, and deep learning and ability improvement of students are facilitated.

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 aims to solve the problem that the analysis result is affected due to the single visual angle of video acquisition in the existing autistic child classroom analysis. According to the autism child classroom behavior analysis method and system, multi-view collection, stable key point detection, high-precision multi-view three-dimensional reconstruction and deep behavior analysis modes are organically integrated through the links of projection matrix construction, weighted calculation, abnormal point filtering, algorithm optimization, error elimination and the like; a complete and efficient system is formed, the accuracy of classroom behavior analysis of children with autism is greatly improved, a solid and scientific basis is provided for personalized education intervention, and a more valuable analysis result is brought.

The invention discloses a ward health education robot, and the robot is internally provided with a health education platform, and the health education platform comprises a personalized education module which is responsible for generating a personalized guidance scheme according to the personal information of a patient; the intelligent question and answer module is responsible for intelligently recommending related health knowledge and matters needing attention; the medicine use and treatment nursing management module is responsible for recording the medicine use condition of the patient and generating a schedule about the patient; the intelligent navigation and hospital guide module is responsible for realizing autonomous navigation of the patient from a ward to a designated place; the physical sign monitoring and analyzing module is responsible for collecting vital sign data of a patient and generating a health report and early warning information; according to the system, the emotion recognition and interaction optimization module recognizes the emotional state of the patient and adjusts the communication mode and content according to the emotional state, the anxiety and fear emotion of the patient can be relieved, and the confidence of the patient in fighting the disease is enhanced.

The invention relates to the technical field, and discloses a personalized Chinese reading recommendation system based on semantic perception and an implementation method thereof, and the system comprises a user modeling module, a learner interest label and cognitive feature model construction unit, a semantic analysis module, a matching recommendation module and a feedback optimization module. The method has important innovative significance in the aspects of improving Chinese learning efficiency, enriching cultural connotation understanding and promoting personalized education development. The systematic design concept and technical means of the system not only can meet the individual requirements of different users, but also can continuously adapt to the change of the education environment, has wide popularization and application prospects, and provides a solid technical basis and innovative demonstration for the intelligent development of Chinese education.

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 belongs to the technical field of big data processing, and provides a student personalized education management method based on artificial intelligence, and the method comprises the steps: analyzing a historical learning record through a recommendation algorithm to generate a recommended course video list of a current student; performing knowledge point target detection on the recommended course video list of the current student to obtain personalized screening courses; updating a recommended course video list of the current student according to the personalized screening course; by intelligently screening big data of all students, the problem of data sparsity of collaborative filtering of a common recommendation algorithm based on implicit feedback can be avoided, the problems of false detection and missing detection of target detection are reduced, and the problem of cold start of the recommendation algorithm is eliminated; and the recommendation accuracy is improved by focusing on the positioning of the video segment.

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 child user portrait construction method, system and device. The method comprises the steps of obtaining training data of a child user; obtaining a user portrait construction model according to the training data; acquiring voice information, behavior information and environment information of the child user; performing fusion processing on the voice information, the behavior information and the environment information to generate multi-modal data; and taking the multi-modal data as input data of a user portrait construction model, and outputting a three-dimensional portrait of the child user by the user portrait construction model. Multi-dimensional information such as voice, behaviors and environment is acquired and fused to generate multi-modal data to construct a three-dimensional portrait of a user, and compared with traditional single-dimensional data acquisition, the interest preference, cognitive level and emotional state of a child in different scenes can be captured more accurately and comprehensively, and the user experience is improved. The three-dimensional portrait can reflect the continuously developing learning requirements of the children in real time, so that the intelligent equipment for the children can carry out accurate personalized education content pushing based on the portrait.

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.