307 results about "Educational content" patented technology

A system for implementing blended learning, including: • a central processing unit (CPU) configured to receive, process and store data from a range of user interfaces, learning management software modules, classroom interaction devices and cloud-based data storage; • an AI engine integrated into the CPU, configured to analyze students' interactions with educational content, classroom activities, and performance data in real time, with the AI ​​engine applying machine learning techniques to create personalized learning paths for each student based on their individual learning style, past performance, and engagement patterns; • A range of devices for classroom interaction, including at least one smartboard, tablet, or interactive projector, each configured to capture real-time data on classroom activities such as student participation, oral responses, and collaborative tasks. The captured data is then transferred to the CPU for analysis by the AI ​​engine to refine and dynamically adapt learning paths for each student; • a student user interface, wherein the student interface displays dynamically generated content tailored to the student's progress and performance, and wherein the content includes multimedia materials such as videos, tests and reading material adapted based on the student's interaction history and learning needs; • a CPU-connected teacher interface, wherein the teacher interface displays real-time analytics and insights from student data, including performance trends, engagement levels, and potential learning gaps, and wherein the AI ​​engine provides the teacher with actionable recommendations for adapting teaching strategies, resources, and interventions based on the individual needs of the students and their performance in class; • a cloud-based storage system, whereby the system securely stores student profiles, historical performance data and learning content, and whereby the CPU retrieves, updates and stores data from the cloud-based system in real time to ensure continuous adaptation of the learning experience.

The invention discloses a touch and talk pen intelligent children education method based on emotion recognition. The method comprises the steps that S1, facial expression data, voice and intonation data and touch behavior data of children are collected; s2, multi-modal high-quality emotion data matched with the actual emotion state is generated; s3, generating deep emotional features related to facial expressions, voices and tones and touch behaviors; s4, generating an emotional state label corresponding to the current emotional state of the child; s5, the generation model generates voice feedback content, image animation content and interactive game content which are adaptive to the current emotional state; s6, generating a personalized recommendation content list conforming to the emotional state of the child; and S7, displaying the education content in the personalized recommendation content list through a display screen, a voice output unit and a touch interaction unit of the touch and talk pen. According to the method, the fuzzy classification problem of the emotional state is solved in a touch and talk pen emotion recognition scene, and the adaptability of the model to the complex emotional state is remarkably improved.

The invention relates to a personalized learning resource recommendation method based on a subject knowledge graph and a graph neural network, and belongs to the field of education content recommendation. The method comprises the following steps: extracting key entities in a textbook and a relationship between the key entities, and constructing a subject knowledge graph; establishing a preliminary learning record table according to the behavior data of the student in the learning process; through knowledge tracking model integration, predicting mastery degree distribution of students for each knowledge point in the learning record sheet, and constructing student portraits; embedding the knowledge points in the subject knowledge graph and the association relationship thereof into the student portrait, and updating the knowledge embedding representation in the student portrait through a graph neural network; and constructing a personalized recommendation model, and pushing learning resources conforming to the current learning state of the student by adopting a mixed recommendation method of collaborative filtering and content recommendation. According to the method, personalized demands of students can be accurately captured in multiple dimensions, and the accuracy, generalization and interpretability of recommended contents are effectively improved.

The invention relates to the technical field of AI dialogue generation, and discloses a child AI dialogue generation method and system based on IP role personalization, and a storage medium. The method comprises the following steps: acquiring IP role personal setting data and constructing a character feature sub-vector, a style feature sub-vector and a knowledge feature sub-vector; performing man-set weight attention calculation on the text input by the user to obtain an attention weight distribution matrix; joint optimization is carried out through a multi-target man-set constraint loss function, and intermediate dialogue content meeting an IP role man-set constraint condition is obtained; and carrying out education content adaptation and role identity consistency verification based on the intermediate dialogue content to obtain a pre-generated dialogue text, and carrying out real-time man-set deviation correction to obtain a target dialogue generation result. The method can continuously monitor and correct the human setting deviation phenomenon in the dialogue generation process, ensures that the generated content always conforms to the IP role setting, and effectively solves the technical problem of role identity drift in multiple rounds of dialogues.

Processing circuitry of a learning platform may be configured to maintain a graph database describing student learners. Processing circuitry may obtain new student learner data and load the data into the graph database. Processing circuitry may receive an engagement or interaction from the new student learner and responsively extract new learnings about the new student learner which are loaded into the graph database. Processing circuitry may receive an inquiry from the new student learner and in response, extract the new student learner data and the new learnings from the graph database and contextualize, using a large language model, a learning unit from the educational content provided by the learning platform as a response to the inquiry using the new student learner data and the new learnings. Processing circuitry may further return the learning unit contextualized by the large language model to the new student learner.

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

The invention relates to the technical field of knowledge graph and content management, in particular to an intelligent education content management method and system based on a knowledge graph, and the method comprises the steps: obtaining multi-modal education content data which comprises text data and audio data; based on the multi-modal education content data, executing multi-modal tensor decomposition to obtain unified feature representation; according to the unified feature representation, topological continuous coherence feature extraction is executed to obtain topological features; based on the topological features, quantum entanglement feature enhancement is executed, and enhanced features are obtained; executing non-exchangeable geometric feature fusion according to the enhanced features to obtain fusion features; based on the fusion features, chaotic dynamics knowledge extraction is executed, and extracted knowledge is obtained; according to the method, the extracted knowledge is constructed into the knowledge graph, different forms of education contents such as texts and audios are integrated through a multi-modal tensor decomposition technology, complementary information among different modals can be captured, and comprehensiveness and accuracy of knowledge representation are improved.

Processing circuitry of a learning platform may be configured to maintain a graph database describing student learners. Processing circuitry may obtain new student learner data and load the data into the graph database. Processing circuitry may receive an engagement or interaction from the new student learner and responsively extract new learnings about the new student learner which are loaded into the graph database. Processing circuitry may receive an inquiry from the new student learner and in response, extract the new student learner data and the new learnings from the graph database and contextualize, using a large language model, a learning unit from the educational content provided by the learning platform as a response to the inquiry using the new student learner data and the new learnings. Processing circuitry may further return the learning unit contextualized by the large language model to the new student learner.

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 discloses an intelligent interaction method for a patient in a radiotherapy process, computing equipment and a storage medium, and the method comprises the steps: carrying out the natural language interaction between the patient and an interaction agent in the radiotherapy process; collecting multi-modal and multi-dimensional patient real-time feedback data and carrying out preprocessing; performing real-time fine tuning optimization on a plurality of pre-training basic models in the interactive agent according to the collected real-time feedback data; the finely-adjusted and optimized interaction agent generates targeted education content, health suggestions and emotion support according to the real-time feedback data of the patient, and outputs the education content, the health suggestions and the emotion support in a natural language mode; and repeating the steps until the radiotherapy is finished. In the radiotherapy process, the interaction agent capable of interacting with the patient is introduced, real-time dynamic personalized health education, treatment suggestions and emotion support services are provided for the patient, and emotion problems such as anxiety of the patient are relieved.

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.

In a first aspect, an apparatus for delivering adaptive educational content using an automated tutoring model includes a processor and a memory communicatively connected to the processor is presented. The memory contains instructions configuring the processor to receive a prompt from a user. The processor, extract linguistic data from the prompt through an automated tutoring model. The processor is configured to determine, through the automated tutoring model, relevant educational data for the user based on the linguistic data of the prompt. The automated tutoring model is configured to classify the user to a learner group based on the prompt. The automated tutoring model is in communication with and selects the relevant educational data from an educational database based at least on the learner group. The processor is configured to present the selected relevant educational data to the user through a display device in communication with the processor.

The invention relates to the field of artificial intelligence and disaster knowledge science popularization, and discloses a multi-disaster scene teenager science popularization education dynamic adaptation system based on artificial intelligence, and the system comprises a data collection module which is used for constructing a multi-dimensional disaster knowledge base and collecting student behavior data in real time; the ability evaluation module is used for enhancing the historical state memory ability through an index gating mechanism according to the student behavior data, and dynamically outputting an evaluation vector containing question type adaptation weights, content level scores and comprehensive ability values by combining normalized state variable stable gradient updating; the scene adaptation module is used for establishing a state transition model and an agency award function through dictionary function combination and generating an optimal adaptation strategy matched with the student state; and the feedback execution module is used for dynamically adjusting task parameters based on the evaluation result of the comprehensive capability value and the optimal adaptation strategy, and feeding back protection operation guidance in a multimedia form. According to the invention, the accuracy of the disaster education content and the intelligent degree of the teaching form are improved.

A system and method for stimulating the activation of neural mirroring mechanisms through personalized avatars to enhance learning and skill development. The system creates hyper-realistic or stylized digital representations of users and animates these avatars to present educational content or demonstrate skills, stimulating mirror neuron activation when users observe themselves demonstrating mastery. Applications include academic learning, language acquisition, professional training, personal development, athletic performance enhancement, and therapeutic interventions including for Autism Spectrum Disorder.

An example for presenting educational content including converting a multimedia document into a text representation of the multimedia document, partitioning the text representation into multiple portions of text based on a text characteristic of the text representation; determining educational concepts associated with portions of text. Then, generating at least a first cluster and a second cluster where the first and second clusters include portions of text of the multiple portions of text, and each portion of respective text in the first cluster is associated with a first educational concept of the one or more educational concepts and each portion of respective text in the second cluster is associated with a second educational concept of the one or more educational concepts. From the clusters, generating a first educational content item based on the first cluster and a second educational content item based on the second cluster.

The invention provides a value-oriented content analysis and intervention method in ideological and political education, and belongs to the technical field of education and teaching. A pre-trained multi-modal value understanding model is combined with a recurrent neural network to extract dominant value features and capture context dependence, implicit value expressions are recognized through an attention mechanism and comparative learning, and a tendency index is generated based on the similarity between feature vector calculation and core value. A reinforcement learning training content intervention agent is utilized to generate an education intervention strategy, and finally anti-fact reasoning is adopted to evaluate different intervention strategy effects and optimize an intervention scheme, so that a complete technical closed loop from content analysis to intervention implementation to effect evaluation is formed. The technical problem that the implicit value in the ideological and political education content is difficult to quantify to accurately identify and implement effective intervention is solved.

An artificial intelligence-powered gamified learning management system is disclosed, designed to enhance financial literacy and stock trading education. The system integrates adaptive learning, real-time market analytics, and interactive simulations, including a Fantasy Stock Trading League and Paper Trading Engine. The AI-powered Adaptive Learning Engine dynamically adjusts educational content and trade recommendations based on user performance, utilizing reinforcement learning and collaborative filtering models. The Market Insights Engine provides predictive analytics, sentiment analysis, and risk assessment using advanced AI models. Users engage in competitive trading simulations, sentiment-based challenges, and community-driven learning forums, fostering practical investment skills. The system offers personalized learning paths, real-time feedback, and post-trade analysis to improve decision-making. Principal uses include stock trading education, financial literacy enhancement, and gamified investment simulations. This system addresses the limitations of static financial education platforms by creating an interactive, adaptive, and engaging learning environment.

The invention discloses an education content recommendation method and system based on group intelligence, and the method comprises the steps: constructing a user dynamic cognition knowledge graph, and carrying out the dynamic attenuation of the memory intensity attribute of a knowledge point node based on a cognition forgetting model in combination with the memory stability; defining utilization pheromones on edges of the knowledge graph, and strategically injecting exploration pheromones according to forgetting states of users or knowledge breadth indexes; guiding a swarm intelligence algorithm to construct a learning path according to a rule jointly influenced by bimodal pheromones; and finally determining an optimal path and generating recommendation. According to the method, accurate intervention on a learning process and intelligent guidance on knowledge exploration are realized through deep fusion of a recognition scientific model and a swarm intelligence algorithm of bimodal pheromones, the learning stability is remarkably improved, information cocoon houses are effectively broken, and the interpretability of recommendation is enhanced through path intention analysis.

An AI-based computing system and method for accreditation of industrial professionals is disclosed. The AI-based computing system includes plurality of subsystems that includes a profile generation subsystem configured to create user profiles for registered users using user credentials. The plurality of subsystems includes an event manager subsystem configured to (a) provide access to continued industrial education content to registered users based on created user profiles, (b) provide AI-based personalized recommendations on the continuing industrial education content in priority using AI model, and (c) manage tasks associated with user profile relating to continuing industrial education content responsive to providing the access. The plurality of subsystems includes a credit generation subsystem configured to generate credit scores for the user profiles based on the tasks completed by registered users, using the AI model. The plurality of subsystems includes a report generation subsystem configurate to generate and publish a report on tasks.

The invention relates to the technical field of knowledge tracking, in particular to a knowledge tracking method based on hybrid convolution, and the method comprises the steps: obtaining the interaction information of a learner and education content; the interaction information is input into a preset knowledge tracking model, the answer correct probability of a target question is predicted, the knowledge tracking model is used for constructing an input sequence based on the interaction information, behavior characteristics in different time ranges in the sequence are extracted through multi-scale causal convolution, and the answer correct probability of the target question is predicted; modeling is carried out in combination with an attention mechanism with a distance penalty term, and finally knowledge tracking is completed in combination with question answering correctness probability prediction of the target question. According to the invention, knowledge tracking is carried out in combination with multi-scale causal convolution and a distance decay attention mechanism, and the ability of modeling the cognitive state of a learner is improved.

A computer-implemented method is disclosed. The method includes receiving, from one or more sensors, one or more measurements associated with a user, where the one or more measurements are received while the user performs a treatment plan, and an electromechanical machine is configured to be manipulated by the user while the user is performing the treatment plan. The method also includes determining, via one or more machine learning models, one or more content items to present to the user, where the determining is based on the one or more measurements and one or more characteristics of the user. A presentation of the one or more content items is provided on an interface while the user performs the treatment plan using the electromechanical machine.

The invention provides an emotion and cognition driven children education interaction system and method, and relates to the technical field of man-machine interaction. Emotion is quantified through an emotion calculation module by using a titer awakening degree two-dimensional continuous model, and an emotion migration slope is predicted in combination with a time sequence convolutional network; accurate description and prospective prediction of the emotion dynamics of children are realized, and the accuracy and initiative of emotion guidance are remarkably improved; secondly, the constructed knowledge graph endows the nodes with three-dimensional dynamic attributes of mastery degree, interest intensity and associated confusion degree, and real-time updating is carried out according to interaction data, so that the cognitive development trajectory of children is dynamically described, and a solid foundation is provided for personalized teaching; and finally, proposals are actively pushed by setting collaborative windows of interest, cognitive correction and the like, and a structured knowledge injection interface is provided, so that parents are converted from passive supervisors to common constructors of education contents, and deep fusion of family intelligence and artificial intelligence is realized.

The invention relates to the technical field of teaching equipment, and discloses an online teaching auxiliary device for oral language teaching of teachers, which comprises a fixed support column used for the structure and installation of all structures, a fixed support column matched with a fixed disc I for fixing a scissor-type support mechanism I, a fixed support column matched with a fixed disc II for fixing a scissor-type support mechanism II, and a fixed support column matched with a fixed disc II for fixing a scissor-type support mechanism II, the two-way transmission mechanism and the operation motor are located at the top of the fixed supporting column and matched for automatic unfolding of the first shear type supporting mechanism, and the first shear type supporting mechanism, the shear type linkage mechanism and the second shear type supporting mechanism are synchronously unfolded through the linkage column. Students can practice and cope with various situations in a safe environment, so that the adaptive capacity and decision-making skills of the students are improved, the system covers multiple aspects of oral teaching, comprehensive education content is provided from reception, reservation to dispute handling, it is ensured that the students can comprehensively understand and master various aspects of oral teaching, and the teaching efficiency is improved. And visualization and multi-sensory learning are realized.

A computer-implemented method for analyzing and quantifying the quality of a first user input in relation to a second user input by comparison to a third user input in a computer system, wherein the second user input defines at least one problem statement, the third user input defines at least one solution statement to the at least one problem statement, the method comprising: - providing a graphical user interface for receiving said first, second and third user inputs; - generating at least one prompt structure based on the first, second and third user inputs, such as a prompt structure for each of the first, second and third user inputs, wherein the at least one prompt structure comprises instructions to quantify the quality of a first user input, wherein the quantified quality at least in part reflects the degree to which the first user input corresponds to the at least one solution statement as defined by the third user, such as in its completeness, and wherein the quantified quality further at least in part reflects its correctness in relation to the at least one problem statement as defined by the second user input; - processing the at least one prompt structure using at least one large language model; and - outputting the quantified quality of the first user input from the at least one large language model, such that it is accessible to a user within the graphical user interface.

The invention discloses a building safety education training system based on interactive experience, relates to the technical field of construction site safety education, and aims to solve the problems that traditional building worker safety education lacks pertinence and is difficult to meet specific safety requirements of various types of work, and the teaching mode is relatively single. The system comprises a database, a personalized learning recommendation module, an interactive learning module and a learning progress and achievement tracking module. The database is used for pre-storing safety knowledge of various construction work types; the personalized learning recommendation module is used for screening and recommending learning content suitable for the worker from a pre-stored database covering safety knowledge of various construction work types according to the work type information and learning history of the construction worker, and the learning content comprises a document and a video course. The method has the advantages that precise education content conforming to the work type and the knowledge level of each worker is provided for the worker, and the safety awareness and the skill level of the building worker are improved.

The invention discloses a family education content recommendation method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: collecting family member multi-source data for preprocessing, carrying out the real-time analysis of the preprocessed data, updating a user file, setting a personalized recommendation target according to an analysis result, and carrying out the recommendation of family education content through the updated user file. The learning progress of a user is predicted by using an LSTM model, and a social relation and an interaction mode among family members are established through a social network analysis algorithm in combination with a robust algorithm and a Pearch ranking algorithm. According to the method, the family member social relation graph is constructed and optimized through social network analysis, the robust algorithm and the Pearch ranking algorithm, the social sub-groups are identified, family education content recommendation is dynamically adjusted in combination with the reinforcement learning algorithm, and the social adaptability and interest matching ability of personalized content recommendation are improved.

The invention relates to the technical field of intelligent recommendation, in particular to a teaching content recommendation method and system driven by online education platform data. The invention discloses a teaching content recommendation system driven by online education platform data. The teaching content recommendation system comprises a knowledge point mastering feature data construction module, a mastering degree analysis module, a gap knowledge point determination module and an education content recommendation module. The knowledge point mastering degree of the user is determined by analyzing the evaluation data of the user, the education content knowledge graph is analyzed through the mastering degree analysis model, the dependency relationship between the knowledge points is researched, the deeper knowledge point mastering degree is analyzed, and then more accurate knowledge point mastering degree judgment is obtained. And then determining notched knowledge points with low knowledge point mastering degree of the user based on the knowledge point deep mastering feature data, and determining an educational content learning path needing to be recommended through the notched knowledge points, so that educational content recommendation for checking and repairing the missing of the user can be realized.

The invention relates to the technical field of ideological and political education, in particular to an ideological and political education interaction system based on social media, which comprises a social media monitoring module, a content analysis and classification module, an interaction mode optimization module and an education content updating module. According to the method, the participation enthusiasm and the core concern of the student are accurately captured by monitoring the online learning community and analyzing the social media data in real time, and the emotional tendency and the related discussion of the student are effectively collected by deeply analyzing the post content, so that the accurate classification of the content and the identification of mainstream and edge viewpoints are realized; the online education platform can adjust an interaction mode and increase simulation discussion according to actual responses of students, teaching is more attractive and targeted, the learning motivation and participation degree of the students are improved by dynamically updating education content and synchronizing social hotspots, the personalized and dynamic teaching process is achieved, and the teaching experience is improved. Therefore, the attraction and interactivity of education are effectively improved.

At least one database stores clinical activity data indicative of clinical activities of medical professionals or maintenance activity by medical equipment servicing personnel. Consumption of educational content units related to one or more medical devices by a medical professional (or maintenance thereof by a servicing person) is tracked. Future clinical (or maintenance) activities to be performed by the medical professional (or servicing person) is predicted based on the clinical (or maintenance) activity data. One or more metrics are calculated related to the medical professional's (or servicing person's) knowledge and / or experience for a future time. The metrics may include a knowledge metric based on the tracked consumption of educational content units, and / or an experience metric based on the clinical (or maintenance) activity data and the predicted future clinical (or servicing) activities. One or more refreshment educational content units are recommended based on the one or more metrics.

The invention discloses a multi-modal large model-based multimedia file generation method, and relates to the technical field of education content generation. Task alignment and evidence readiness, question analysis and question solving trajectory diagram construction, compiling into a unified instruction of a split white board and a parastyle, controlled generation, co-optimization in generation, posterior judgment, traceable release and learning write-back are sequentially completed; according to the scheme, a problem solving track graph is used as a unique true source, a semantic structure which can do a problem is mapped into a shot, blackboard writing and oral playing, and a suitable age target and a cognitive load are synchronously constrained; in the generation stage, the fact and safety scores are calculated according to the shot, local regeneration is triggered, and rework of the whole section is reduced; source credentials are written before publishing, and learning conditions are written back in combination with watching and evaluation, so that a correct, right-age, traceable and self-evolutionary closed loop is formed. And unified numbering, continuous input processing and output are realized, fixed-point backspacing and sampling inspection are supported, uncertainty and cost are reduced, and convenience is brought to campus distribution and home check verification.