74 results about "Ai education" patented technology

The invention relates to the technical field of artificial intelligence education, in particular to an intelligent homework tutoring system and method based on multi-modal interaction and adaptive learning, and the system comprises a multi-modal input analysis module, a cognitive state dynamic evaluation module, an intelligent decision explanation engine, and a learning effect visualization closed-loop module. The method has the beneficial effects that a three-dimensional adaptive system of explanation granularity-presentation form-interaction frequency is used, and teaching strategies such as visual derivation, concept metaphor and the like can be automatically matched according to cognitive styles of students. Secondly, a'backtracking reinforcement-lateral expansion 'double-intervention mechanism is innovatively used, and the recurrence rate of similar errors is effectively reduced through error real-time detection and correlation knowledge contrast teaching. And finally, constructing a dynamic knowledge graph and an interactive learning report, realizing visual tracking of a learning path, and helping students to establish a systematic knowledge framework.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

The invention relates to the technical field of artificial intelligence education, and discloses a personalized online education system and method based on artificial intelligence, and the system comprises a data collection and perception module which is used for collecting the dynamic behavior data and multi-mode perception data of a learner; the knowledge processing and constructing module is used for processing teaching contents to construct a structured knowledge graph; the user modeling module is used for constructing a dynamic learner portrait comprising a knowledge state model and a cognitive-emotional state model for the learner based on the collected data and the constructed knowledge graph; and the intelligent decision and intervention module is used for generating and executing personalized learning intervention based on the dynamic learner portrait. According to the method, by fusing multi-mode cognitive state perception, reinforcement learning intervention decision and a knowledge graph dynamic evolution mechanism, accurate and timely personalized intervention for learners and intelligent guidance of optimal learning strategies are realized.

The invention relates to the technical field of artificial intelligence education, in particular to a Russian spoken language practice personalized recommendation method and system based on artificial intelligence, and the method comprises the steps: 1, outputting a phoneme sequence with a timestamp through Russian automatic voice recognition; 2, collecting an exercise interruption position and repeated read-after behavior data; 3, generating a dynamic learner portrait; 4, mapping high-frequency errors in the learner portrait into abnormal path weights of map nodes; 5, a lattice tail error option and a non-matching body verb interference item are injected; 6, when the voice fluency attenuation of the learner exceeds a dynamic threshold value, the sentence complexity is reduced; and 7, calculating an error rate descent gradient based on the exercise completion data, and dynamically adjusting the abnormal path weight of the knowledge graph. Through audio stream analysis and syntax tree construction, the system can accurately identify errors of the learner in grammar, pronunciation and other aspects, and the learning efficiency is improved.

The embodiment of the invention provides a development method and a display system based on an artificial intelligence education service platform, and the method comprises the following steps: receiving the interaction operation of a user on the content through the display system; according to the role and historical interaction data of the user, payment is authorized according to the activity state of the user and terms of a contract or a protocol, data interaction is realized through a network, and resource management and monitoring functions of the user are supported. Relates to the field of information technology services. Automatic generation of contracts or protocols is realized, and learning service protocols, curriculum subscription contracts or resource use permissions and the like are dynamically generated by adopting a templating method in combination with user role requirements and input parameters. Compared with a traditional manual contract generation mode, the method greatly shortens the generation time, reduces manual intervention, and improves the response speed and accuracy.

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 belongs to the technical field of artificial intelligence education application and computing resource management, and discloses a course flexible construction and computing power elastic allocation method and system oriented to an education large model. According to the method, course contents are disassembled into knowledge unit plug-ins which can be independently deployed, and lightweight consistent deployment of course instances in different terminal environments is realized based on a dependency relationship between directed acyclic graph modeling plug-ins; by taking a course target as a constraint, analyzing a knowledge unit reasoning path on the dependency graph, only reserving components with weights higher than a threshold value, and cutting non-key sub-model components and a redundant prompt chain to generate a dynamically cut course instance; distributing an independent container operation environment for the course instance, and collecting an operation resource state through kernel-level monitoring; according to the method, tasks are divided into different types of task queues in combination with calculation characteristics of resource states and teaching tasks, resource demands are predicted based on historical resource use behaviors of users, and calculation resources are dynamically allocated or recycled.

The invention relates to the technical field of artificial intelligence education, and discloses a personalized English grammar learning system based on artificial intelligence. Comprising a multi-modal data acquisition module, a deep user portrait construction module, a dynamic association knowledge graph module, a multi-modal intelligent diagnosis module, a meta-cognitive guide type learning path planning module, a personalized content generation module, an emotional real-time feedback module, a meta-cognitive training module, a social cooperation module and a closed-loop optimization module. The multi-modal data acquisition module is used for acquiring multi-dimensional and multi-modal data of a learner, and covering related data such as basic information. According to the personalized English grammar learning system and method based on artificial intelligence, by setting a multi-modal data acquisition module and a deep user portrait construction module, basic information, cognitive features, meta-cognitive levels and emotional preferences of learners are captured in all directions; and constructing a dynamically updated deep user portrait by means of a multi-dimensional data fusion algorithm and a time sequence analysis algorithm.

The invention belongs to the technical field of artificial intelligence education, and relates to an intelligent teaching assistance method and system based on a large language model. The method comprises the steps that text data, voice data and eye movement track data of students are acquired, feature extraction is carried out, and a three-dimensional cognitive state tensor model is constructed; constructing a cognitive state tensor decomposition constraint optimization function, performing tensor decomposition, and outputting a core tensor and a factor matrix; generating a teaching strategy, and defining a cross-modal kernel function to realize feature association; tensor decomposition parameters are dynamically adjusted; and outputting the updated teaching strategy. According to the method, the cognitive change trajectory of a learner can be captured from multiple dimensions, and the accuracy and comprehensiveness of cognitive state characterization are remarkably improved through a cross-modal kernel function and a gradient coupling mechanism; constructing a cognitive state tensor decomposition constraint optimization function to describe an evolution law of a cognitive state, and capturing a continuous change characteristic of the cognitive state along with time through a dynamic constraint condition of a time factor matrix.

The invention relates to the technical field of artificial intelligence education, in particular to a heuristic teacher observation and evaluation AI support system based on Prompt engineering and RAG collaborative enhancement. According to the system, infant observation records are subjected to standardization processing through a data processing module, then large language models are called in sequence through four professional Prompt templates, and heuristic thinking question generation, deep inquiry analysis, behavior disassembly and target matching based on the RAG technology and synchronous generation of educational strategies and parent suggestions are achieved respectively. And a complete observation-reflection-evaluation-strategy closed-loop processing flow is formed. Through cooperative enhancement of Prompt engineering and RAG technology, automatic analysis and deep professional reflection guidance of observation records are realized, system processing efficiency and analysis depth are improved, error propagation is avoided by adopting an independent parallel architecture, and continuous improvement of system performance is ensured through a continuous optimization mechanism.

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.

This invention relates to the field of artificial intelligence education technology, specifically to a method and system for reviewing and providing feedback on physics, chemistry, and biology lab reports based on a multimodal large-scale model. The method includes: acquiring lab report question data, student answer data, and scoring standard data; vectorizing the question data and retrieving scoring criteria from a scoring knowledge base; selecting either a text-based or image-based scoring process based on the answer type, and calling a fine-tuned multimodal large-scale language model for scoring reasoning; calling an error identification module and a teaching suggestion generation module based on the scoring results; summarizing the complete review results and calculating the joint confidence level; and determining whether to push the results for manual review based on the confidence level. This invention can improve the accuracy, fine-grained understanding, and quality of teaching feedback in lab report review, enabling the self-evolution of the review system.

We provide AI education tools, server devices, and AI education support devices that can help promote the use of AI in various activities. [Solution] One aspect of the AI ​​educational tool disclosed herein comprises a worksheet formed with a plurality of activity cards, each having an activity content defined thereon; a plurality of AI cards, each having a task that can be supported by AI (Artificial Intelligence) defined thereon; an activity card placement area in which any one of the plurality of activity cards is placed; and an AI card placement area in which a first AI card, from the plurality of AI cards, is placed, the first AI card having a task defined thereon that is applicable to a task that constitutes a first activity content, which is the activity content defined on any one of the plurality of activity cards.

The invention relates to the technical field of intelligent education, in particular to an artificial intelligence general identification education method and system based on bimodal closed-loop feedback. The method comprises the steps that a project task is acquired, students are guided to acquire situational data through an entity AI teaching aid, and a terminal provides real-time feedback for the acquisition process; entity verification is carried out on an entity AI teaching aid in a real environment, if a task is judged to be failed, the intelligent routing unit classifies problems based on multi-dimensional classification, and a feedback mode is dynamically selected; if the rule feedback mode is selected, matching a predefined rule base to generate a structured instruction, pushing the structured instruction to the terminal, guiding the student to take additional data and performing incremental training; if an Agent collaboration mode is selected, calling a cloud large model API to generate a Scotch bottom type guide dialogue, guiding students to analyze problems and select solutions, and performing entity verification again after data supplementary shooting and incremental training are completed; according to the invention, the practice efficiency and thinking cultivation depth of K12 artificial intelligence education are improved.

The application relates to the technical field of AI education interaction, and discloses an AI education interaction system based on motion capture. The system comprises four modules, namely a motion data acquisition module, a motion quality analysis module, a teaching parameter configuration module and an interaction result generation module. The motion data acquisition module acquires a historical motion monitoring data set of a target user in a preset historical time period through a motion sensor; the motion quality analysis module traverses the data set, analyzes and determines a motion stability factor and a motion accuracy factor; the teaching parameter configuration module indexes the two factors, optimizes search in a teaching configuration space, and determines a target teaching parameter; and the interaction result generation module carries out interaction with the user according to a preset teaching item based on the target teaching parameter, acquires a real-time interaction data set, and obtains a teaching target interaction result after AI recognizer identification. The system can accurately monitor motion, scientifically analyze quality and provide personalized guidance, and improve the teaching experience.

The invention discloses a student academic early warning system and method based on multi-modal behavior analysis, and belongs to the technical field of artificial intelligence education, and the system comprises a non-intrusive classroom behavior collection terminal, a multi-modal student evaluation engine and a visual early warning application. The acquisition terminal locally processes acquired classroom behavior videos of students into time sequence behavior feature vectors, the evaluation engine receives the feature vectors and analyzes the feature vectors through a built-in academic risk prediction model, and the evaluation engine further comprises a large language model interpretation module. According to the system and the method, non-intrusive quantification is carried out on student behaviors through edge calculation of an acquisition terminal to protect privacy, deep analysis is carried out through a sequence model in an evaluation engine, operable teaching intervention suggestions are generated by utilizing a large language model interpretation module, and objectivity, real-time performance and operability of teaching evaluation are improved.

The application belongs to the field of intelligent education, and particularly relates to an artificial intelligence education system based on education big data, which comprises a student terminal and a server, and is characterized in that a user verification module, a learning module, a handwork module, an entertainment module, a feedback module and an analysis module are arranged in the student terminal; the student terminal comprises a main body and a time module; the time module is used for controlling the working time of the learning module, the handwork module and the entertainment module; the learning module and the entertainment module are arranged to allow students to have proper rest during the learning process, so as to achieve the combination of work and rest; the handwork module is arranged to improve the attention and the hand-on ability of the students, so as to improve the learning ability of the students; the learning result, the attention and the hand-on ability of the students are tested to measure the personal comprehensive level of the students, and the personal comprehensive level of the students is compared with the level of the students of the same age, so that the personal characteristics of the students can be better judged, and the teaching plan can be formulated in a targeted manner, so as to improve the education effect.

The application discloses a method and a server for generating ancient poetry video based on voice interaction video model, and relates to the technical field of artificial intelligence education, comprising: receiving a user's ancient poetry learning voice instruction and converting it into an instruction text; obtaining context information of the current user conversation; identifying whether the current intention is to generate an ancient poetry video according to the recognized instruction text and context information; when the intention is to generate an ancient poetry video, searching for corresponding author and original text information according to the recognized ancient poetry title; generating an ancient poetry meaning interpretation text by receiving the ancient poetry title, author and original text information through a text generation large model; and generating a video depicting the expression of ancient text or poetry by receiving the ancient poetry title, author, original text information and the ancient poetry meaning interpretation text through a preset video generation large model in real time. The application aims to solve the problem that users cannot generate ancient poetry learning videos in real time through voice interaction in the prior art.

This invention relates to the field of artificial intelligence-based educational assessment, and discloses a cognitive diagnostic model and method based on knowledge text. The model includes a text feature extraction module for extracting and generating knowledge point association vectors representing the degree of association between questions and preset knowledge points; a knowledge proficiency embedding module for mapping knowledge proficiency vectors; a question attribute embedding module for mapping question knowledge difficulty vectors and question discrimination scalars; a knowledge association attention module for adjusting attention weights and generating weighted knowledge representations; and an adaptive nonlinear prediction module containing a multi-layer KANLinear structure composed of B-spline basis functions and outputting the prediction probability of correct student answers. This invention solves the problem of fragmented internal module functions in existing models and features high interpretability and independence from manual annotation.

The invention discloses a personalized classical Chinese reading disorder diagnosis system based on a knowledge graph, and relates to the technical field of artificial intelligence education, the system comprises a server, the server is in communication connection with a data acquisition unit, a reading cognition analysis unit, a scene association analysis unit, an interaction analysis unit, a comprehensive diagnosis unit, a feedback output unit and a display terminal; by introducing a multi-modal data acquisition mechanism and a modular diagnosis structure, the automation, precision and dynamic response capability of classical Chinese reading disorder recognition can be effectively improved; compared with an existing static diagnosis means which only depends on manual evaluation, the real reading behavior of the user is dynamically collected and quantitatively analyzed from the three dimensions of vocabulary and sentence pattern, cultural association and behavior interaction, and a hierarchical obstacle signal and comprehensive cross judgment mechanism is established, so that the diagnosis result is more multi-dimensional and real-time, and the diagnosis accuracy is improved. And the method has more personalized adaptive capacity.

The invention discloses a teaching dialogue cognition level dynamic classification method and system based on time entropy, and belongs to the field of artificial intelligence education. In order to solve the problems that an existing method depends on static characteristics and ignores dynamic interaction and class imbalance, a new model with better performance on a known reference data set is provided. The scheme comprises the following steps: calculating teacher-student speaking time entropies to quantify interaction and divide teaching styles; semantic richness features are extracted; and constructing a dynamic weighted heterogeneous integrated model, fusing XGBoost and LightGBM, and dynamically adjusting the weight according to category distribution and minority class performance. Compared with a traditional method, the model achieves more accurate and robust classification performance on a reference data set, especially improves the recognition capability of minority classes, and provides an advanced modeling scheme for an intelligent teaching system. The abstract drawing is Figure 1.

This invention relates to the field of artificial intelligence education technology, and more particularly to an AI multimodal private domain knowledge base and intelligent agent management method. This method extracts features from multimodal course resources and maps them to a unified semantic space to construct a cross-modal semantic association index. Based on the index, knowledge units are segmented and metadata is extracted to construct a private domain knowledge graph. By configuring intelligent agent instances, multi-hop reasoning is performed in the graph according to user intent to determine the required combination of knowledge units and the order of invocation, retrieving and fusing multimodal resources to generate response results. This invention achieves deep association and intelligent scheduling of multimodal knowledge, improving the accuracy of knowledge retrieval and the richness of response content.

The invention relates to the technical field of AI education, and discloses an AI education office decision center system based on multi-agent collaboration, which comprises the following steps: acquiring education data in real time based on a distributed data interface, cleaning, aligning and fusing the acquired education data, and constructing a unified education data lake; the education data comprises teaching behavior data, learning achievement data, course content data and management data; a plurality of functional heterogeneous agents are deployed, and the agents construct an education global perception model for education data based on deep learning, knowledge graph and reinforcement learning technologies. According to the method, a real-time education decision knowledge graph is constructed based on an education global perception model, a multi-objective optimization and game theory method is fused, a course adjustment and resource allocation scheme which gives consideration to interests of multiple parties and conforms to actual constraints is generated, executable verification is carried out through the knowledge graph, and it is ensured that the decision is scientific, reasonable and feasible.

The invention relates to the technical field of artificial intelligence education, in particular to a family English enlightenment auxiliary system based on AI, which comprises a voice interaction module used for collecting Chinese voice instructions of parents and voice responses of children through a microphone array, carrying out noise reduction processing, converting the Chinese voice instructions and the voice responses into structured texts, and sending the structured texts to the voice interaction module; wherein the voice of the children is identified by adopting an optimization model trained based on a specific ChildMandarin data set; the translation conversion module is connected with the voice interaction module, and is used for converting Chinese text instructions of parents into English expressions containing concrete vocabularies and clauses of subjects, called objects and guests on the basis of a children cognitive hierarchical corpus; the parent guiding module is connected with the translation conversion module; closed-loop optimization of family English enlightenment is realized by constructing a five-module collaborative system of voice interaction, translation conversion, parent guidance, difficulty adjustment and AR enhancement.

The invention belongs to the technical field of artificial intelligence education, and discloses an intelligent construction site teaching platform and method based on artificial intelligence. The digital twin sand table system comprises a vehicle scheduling management system for an intelligent construction site sand table, an air detection system for the intelligent construction site sand table, a personnel detection and dangerous action recognition system for the intelligent construction site sand table, a material model and material management system for the intelligent construction site sand table, and a building information model and management system for the intelligent construction site sand table. The VR education system comprises an education resource library module, an intelligent evaluation module and a demonstration VR operation module, overall analysis and summarization are carried out through arrangement of information transmitted by each system and analyzed data, and the intelligent construction site sand table management system based on the digits is formed, so that the digits are intelligentized and visualized, and the teaching efficiency is improved. And dangerous situations possibly occurring in the learning process are monitored and prompted, so that the teaching cost and the safety risk are reduced.

The present disclosure provides a teaching strategy processing method and device, and relates to the technical fields of explainable artificial intelligence, educational technology and the like. The specific implementation scheme is as follows: receiving learning information and a portrait model of a teaching object; generating a candidate teaching strategy based on the portrait model; detecting whether the candidate teaching strategy has a teaching strategy bias risk based on the learning information and / or strategy information of the candidate teaching strategy; in response to detecting that the candidate teaching strategy has a teaching strategy bias risk, generating and sending a bias risk root cause report of the candidate teaching strategy to an education terminal; receiving teaching intervention decision information sent by the education terminal, and generating and sending a target teaching strategy to the teaching object based on the teaching intervention decision information.

The invention relates to the technical field of artificial intelligence education, in particular to a student emotion-based learning state analysis method and system, and the method comprises the steps: enabling a plurality of frame images to form an image set according to a time sequence, processing the image set, carrying out the feature analysis of the processed image set, obtaining student emotions corresponding to different time points in the image set, and forming a student emotion matrix; analyzing the student emotion matrix to obtain an emotion change time point, emotion duration and an emotion type; obtaining current course content; obtaining an emotion score according to the course content, the emotion change time point, the emotion type and the emotion duration; and obtaining the learning state of the current course of the student according to the emotion score. Compared with subjective judgment or static classification by a teacher, the method and the device have the advantages that comprehensive judgment can be performed by combining the emotion change, the course content, the emotion type and the duration of the student, a more accurate emotion score is obtained, and the class state of the student is evaluated more accurately.

The invention discloses a programming learning diagnosis method and system based on double-engine verification, and relates to the technical field of artificial intelligence education. The method comprises the following steps: collecting user programming learning data, including code submission records and error information; analyzing the learning data by using a large-model deep inference engine to generate a preliminary diagnosis result; meanwhile, a knowledge graph real-time verification engine is used for verifying the preliminary diagnosis result, and a verified diagnosis result is generated; and on the basis of the verified diagnosis result, a personalized learning path is generated through a multi-agent collaborative decision-making system, and multiple agents comprise a diagnosis agent, a resource agent and a path agent. According to the method, illusion output of a large model in the STEM subject is effectively eliminated through a double-engine verification mechanism, and the diagnosis accuracy and the matching degree of learning path recommendation are improved.