100 results about "Ai education" patented technology

The invention provides an AI agent process automation method and system for an education scene, and relates to the technical field of AI education, and the method comprises the steps: constructing a hierarchical knowledge graph through semantic segmentation, and constructing an agent encoder model through a graph attention network and comparative learning. Based on agent operation data and a feedback mechanism, operation parameters are dynamically adjusted, task decomposition is performed, sub-tasks are processed by using a meta-learner, and a knowledge cache module is established. And semantic analysis and association network construction are performed by using the knowledge cache module, a decision model is trained, and an execution process is optimized. The workflow evolution trend is predicted according to the agent state data, sub-task configuration is optimized, closed-loop optimization is formed, and therefore the automatic processing efficiency and adaptability of the AI agent in an education scene are improved.

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 belongs to the technical field of artificial intelligence education, and particularly relates to a student learning behavior prediction method based on artificial intelligence, and the method comprises the steps: obtaining student learning data and teacher teaching task data; constructing a student agent according to the student learning data, and constructing a teacher agent according to the teacher teaching task data; a dynamic teaching strategy generated by the teacher agent is input into the student agent, and the student agent predicts possible learning behavior change of the student based on a learned behavior mode in combination with a strategy-behavior causal model; the prediction result is linked with a teaching management mechanism, a dynamic learning file is generated, the teacher intelligent agent matches personalized learning resources through an intelligent recommendation algorithm according to the dynamic learning file, and the student intelligent agent receives feedback of students on the learning resources and performs targeted intervention actions in combination with feedback information. Therefore, the problems of insufficient causal reasoning ability, weak calibration ability, insufficient data fusion ability and the like in the prior art are solved.

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 discloses a content interaction method for AI online education, which is applied to a content interaction system for online AI education, and the content interaction system for online AI education comprises a teacher end, a server end and a student end. According to the AI online education content interaction method, through interaction among the teacher terminal, the server terminal and the student terminal, students can play back at any time point through the student terminal, timely and repeatedly listen to important knowledge points, and can also join in a live course again without missing follow-up real-time explanation content; meanwhile, the playback efficiency is improved through the judgment of the wrong playback operation at the student end. According to the playback dynamic state of the student end, the teacher end generates a message prompt of a playback hotspot interval to assist a teacher to adjust an explanation strategy in a targeted manner, so that the content is fully understood and mastered, and the teaching effect is effectively improved; and meanwhile, the repeated content explanation is identified and the classroom rest time period is reminded, so that the time is effectively saved when the student side plays back.

The invention discloses an intelligent agent-based large model education risk problem generation method, which is characterized in that a dynamic student intelligent agent is constructed to simulate a real learning behavior, and a potential risk problem in an education scene is automatically identified and generated in combination with the generation capability of a large language model; the method specifically comprises the steps of constructing a multi-dimensional student agent, constructing a risk problem generation rule base, generating potential risk problems and risk grade classification, optimizing risk problem generation quality, establishing an educational risk problem database, continuously updating and iterating and the like. Compared with the prior art, the method has the advantages that the safety and reliability of an AI education product are remarkably improved, the risk identification accuracy is remarkably improved along with the use time, an original solution is provided for safe development of the education science and technology field, healthy development of artificial intelligence in education application is guaranteed from the technical source, and the method is worthy of popularization and application. The method is widely applied to the fields of education content security auditing, self-adaptive learning system risk prevention and control and the like, and has good application prospects.

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, and discloses an AI intelligent education tutoring system based on a thinking chain and a retrieval enhancement generation technology. Transparent generation and dynamic verification of a problem solving logic chain are realized through a CoT-RAG fusion architecture, and the interdisciplinary confusion rate is reduced in combination with a knowledge graph technology of subject boundary isolation; the non-question-bank-dependent generation engine creates questions in real time based on semantic association, and the coverage rate is increased; federated learning and differential privacy technologies are integrated, and the safety of user data is ensured while the generalization ability of the model is improved; the learning behavior analysis unit dynamically optimizes a knowledge recommendation path through time sequence modeling, and cooperates with the interactive logic correction module to form a self-evolution closed loop. Finally, an intelligent education system which is high in interpretability, high in professional precision and compliant in privacy is constructed, and the learning efficiency and the interdisciplinary problem solving capability are remarkably improved.

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 invention relates to the technical field of programming teaching, in particular to a graphical programming system based on a big language model and hardware constraints and a use method, and aims to realize automatic generation from a natural language to a graphical code by integrating the big language model and a Blockly graphical platform, and dynamically adapt in combination with hardware constraints of a master control development board (such as Raspberry Pi). The system comprises a client program, a knowledge base and an interactive programming interface, supports a user to control external hardware equipment through a natural language instruction, and simultaneously provides functions of code real-time mapping, dynamic debugging and teaching connection. The method reduces the programming threshold of software and hardware combination, is suitable for the fields of artificial intelligence education, Internet of Things development and robot control, and significantly improves the development efficiency and user experience.

The invention discloses a knowledge network construction and recommendation method for artificial intelligence teaching, and relates to the technical field of education informatization, and the method comprises the steps: collecting structured course database data, semi-structured teaching material data and unstructured audio and video resources; executing cross-modal knowledge analysis by adopting a bidirectional sequence labeling model, and extracting an artificial intelligence teaching knowledge subject object and a multi-dimensional feature description thereof; constructing a knowledge unit, and adopting a model of'knowledge subject-feature dimension-feature value 'and'knowledge subject-association relationship-association knowledge subject'; vectorized semantic calibration is realized through an embedded space mapping technology, and heterogeneous semantic unit fusion is completed in combination with a dynamic similarity threshold mechanism; and screening high-quality knowledge units based on a credibility verification engine to construct an artificial intelligence teaching knowledge network. According to the invention, the organization efficiency and personalized service accuracy of artificial intelligence education resources are effectively improved, and the problem of knowledge acquisition efficiency bottleneck caused by discretization of multi-source teaching resources is solved.

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 big data evaluation, in particular to an AI educational training analysis method and system based on big data evaluation, and the method comprises the steps: obtaining a weak link of the operation of a corresponding person from historical operation data, constructing an operation weak link knowledge point graph based on the knowledge content related to the weak link, and obtaining an AI educational training analysis result; performing weak knowledge point association analysis based on the operation weak link knowledge point graph to obtain association conditions of the weak knowledge points, and performing importance weight analysis of the weak knowledge points based on the association conditions of the weak knowledge points and the risk levels of the corresponding weak links obtained through evaluation. The course resource matching analysis is carried out based on the importance weight analysis of the weak knowledge points, the danger degree of weak links is evaluated, and the course resource matching is carried out according to the importance weight of the weak knowledge points, so that personnel can learn and master key knowledge and skills in time, and the risk of accidents caused by misoperation is reduced.

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.

1. The name of the product of this design: Programming teaching machine (artificial intelligence education). 2. Purpose of this design product: Used for artificial intelligence education, a single-board computer equipped with an AI capability development board. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

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 discloses a robot system and method for teaching and training, and relates to the technical field of artificial intelligence education, and the system comprises a data initialization information collection module which is used for enabling a student to contact a robot for the first time, inputting a learning ability test result, hobbies and interests, and basic information of knowledge mastering degree self-evaluation by means of a multi-mode interaction interface of the robot, the basic information of the students is collected through the initialized information acquisition module, and the learning ability of the students is scientifically evaluated by means of the personalized learning planning module and the comprehensive characteristic index construction formula of the students, so that personalized learning paths are customized for the students, the pertinence and efficiency of learning are improved, and the learning ability of the students is improved. Meanwhile, the project type learning guiding module evaluates according to the project difficulty adaptation degree, it is ensured that the project difficulty is matched with the ability of the student, and the learning effect is further improved.

The invention relates to an intelligent education projector system based on an AI large model and an interaction method. The intelligent education projector system comprises a projector body, a data acquisition unit, a core processor, a projection processing module and a voice playing module. According to the invention, the projector is integrated with AI education, so that the projector has an education function, and the teaching experience of a user is improved. According to the method, active input data such as audios, texts and pictures of a user can be collected, and the multi-modal data are coded to generate corresponding feature vectors. The vectors are input into a multi-mode large model which is trained in advance and subjected to education field data fine adjustment, so that an execution strategy aiming at the current problem is obtained. And finally, targeted teaching reply content is generated based on the strategy. According to the design, multi-modal information is fully utilized, students can be guided to solve learning problems, and the students are helped to consolidate and summarize learning contents in a targeted manner.

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 utility model provides an artificial intelligence programming learning mainboard and a programming device used for artificial intelligence education, and the artificial intelligence programming learning mainboard comprises a CPU system, a power supply management module, a power supply management module and a programming module. The RFID card reader interface, the USB interface, the indicating lamp interface, the function key interface, the sensor interface, the analog quantity sensor interface, the LCD interface, the motor driving interface, the WIFI and Bluetooth communication module and the WIFI and Bluetooth antenna are respectively connected to the CPU system. The programming device for artificial intelligence education comprises a main board adopting the artificial intelligence programming learning main board; the program downloading assembly, the intelligent module and the motor assembly are inserted into the corresponding interfaces of the mainboard during use and are electrically connected with the corresponding interfaces of the mainboard; and the intelligent power supply is electrically connected with the power supply management module during use. According to the utility model, a plurality of identification hardware capable of carrying out man-machine interaction are integrated.

The AI education-based teaching-aided education robot is applied to the technical field of education robots and comprises a robot body, a base is fixedly mounted at the bottom of the robot body, two connecting structures are welded to the top of the base, each connecting structure comprises a movable gear, and the movable gears are fixedly mounted on the base. A limiting shell is welded to the top of the movable gear, and an inner cavity of the limiting shell is fixedly connected with a limiting rod. When a user needs to protect a robot body after normal use, the user resets a limiting shell at the moment, a protection plate and a fixing rod are put into an inner cavity of a fixing shell again, then the user drives a first protection shell and the fixing rod to enter the inner cavity of the fixing shell, and the position of the fixing rod is positioned; and then, after the user clamps the position of the fixing rod through an anti-skid rod and an anti-skid sleeve fixed to a second protection shell, through rubber layers arranged on the surfaces of a protection plate, the first protection shell and the second protection shell, protection on the robot body after normal use is completed.

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.