203 results about "Educational content" patented technology

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 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.

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.

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.

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 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 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.

A response generation method in which a real-time tutor is integrated into the user's browser to guide an AI engine to generate real-time responses, enabling user interaction with a real-time tutor integrated within a browser extension is disclosed. The method involves receiving user input, which can be text or spoken queries. If the input is audio, it is converted to text. Prompts are then generated based on user input, educational standards, real-time tutor details, educational content from the browser, and internal educational content. These prompts guide the AI engine, which is pre-trained on educational standards, to generate a relevant response. The response is converted to audio using text-to-speech synthesis, aligning with the real-time tutor. The audio is synchronized with the video to create an educational video featuring the real-time tutor. Finally, the real-time generated video is streamed back to the user, enhancing engagement through integrated visual and auditory feedback.

To properly evaluate training for a worker who has been trained in the past, while considering practical work.SOLUTION: A training evaluation apparatus 1 includes: a training recognition unit 13 which acquires leaning tendency of a trainee from a history of training that the trainee received; a work recognition unit 14 which acquires work achievement of the trainee from a history of work that the trainee performed; an effect prediction unit 16 which predicts training effect to be obtained by re-training the trainee, by proceeding with a training level of re-training in accordance with the learning tendency of the trainee, the training level according to the work achievement of the trainee being defined as a training level at the beginning of the retraining of the trainee; and a display control unit 18 which displays a result predicted by the effect prediction unit 16 on a screen.SELECTED DRAWING: Figure 1

To reduce time and labor required for registration / update of job seeker information about a job seeker.SOLUTION: An information processing device includes: a reception unit which receives input of job seeker information including acquisition skill information acquired by a job seeker; an acquisition unit which outputs the job seeker information to a job opening content provision server, and acquires a matching result obtained by matching a job opening source with the job seeker in the job opening content provision server; a determination unit which identifies insufficient skill information lacked in the job seeker, on the basis of request skill information and acquisition skill information, and determines recommendation information about an educational content for acquiring insufficient skill, managed by an educational content provision server, on the basis of the insufficient skill information; a transmission unit which transmits the recommendation information, to a terminal used by the job seeker together with the matching result; and a processing unit which makes the job seeker take a lecture about the educational content according to the recommendation information, and reflects additionally acquired addition skill information in the acquisition skill information.SELECTED DRAWING: Figure 1

The invention relates to the technical field of data processing, and discloses an IPTV education content intelligent navigation method and system based on voice recognition. The method comprises the following steps: constructing a knowledge point-course-subject three-level semantic tree through an educational knowledge graph neural network; performing cognitive modeling on the learning behaviors of the user to generate a user cognitive file; semantic analysis is performed on the voice data, and a query vector is generated in combination with a historical track; matching the query vector with the semantic tree, and screening candidate contents in combination with the cognitive archive; and performing continuity evaluation based on the session state to generate a navigation result. According to the method and the device, the problem that the recommended content is lack of systematicness and individuation due to the fact that an existing IPTV education platform cannot understand the education content knowledge hierarchical structure and the user cognitive development law is solved. And the education content recommendation accuracy and the learning path continuity are improved.

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

A matching system that matches requesters with applicants by displaying educational content associated with posted projects. The system includes a first applicant device operated by a first applicant and a computing device.) in communication with the first applicant device and a database. The database stores first disclosure information defining a disclosure range for posted projects and second disclosure information defining a separate, independent disclosure range for associated educational content. In response to a request from the first applicant device, the computing device executes a filtering process that identifies a subset of posted projects permitted for disclosure based on the first disclosure information and identifies a subset of educational content permitted for disclosure based on the second disclosure information. The computing device generates for display a customized user interface presenting only the permitted projects and their corresponding permitted educational content to be displayed to the first applicant device.

Techniques for providing contextual educational content for a content receiver using artificial intelligence are disclosed. A determination is made to provide educational content regarding a content receiver to a user. Contextual information associated with content currently displayed to the user using the content receiver is obtained. A prompt for an educational artificial intelligence model is created based on the contextual information and provided to the educational artificial intelligence model. The educational content is displayed to the user based on output from the educational artificial intelligence model. The educational artificial intelligence model is trained based on user input received in response to displaying the educational content.

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

The present invention provides a machine learning-based system designed to automatically generate and associate classes or videos offered on an educational platform with topic tags. The machine learning-based system leverages both extractive and predictive topic tagging techniques: extractive tagging identifies relevant topics directly from content, while predictive tagging uses machine learning models trained on pre-tagged content to predict relevant topics for new content. This dual approach ensures both accuracy and comprehensiveness in tagging.

This invention provides an artificial intelligence-based method and system for automatically generating online educational content, relating to the fields of internet big data services and cloud computing technology. By constructing a semantic energy envelope interference engine, this invention achieves deep coupling of online educational resources in terms of temporal activity and spatial logic. This not only enhances the logical continuity and academic freshness of interdisciplinary generated content but also effectively suppresses redundant computation of invalid resources through energy coherence judgment logic. While ensuring content purification, it significantly reduces the invalid load on the cloud search engine, enhances operational robustness under conditions of interruption or fluctuation in massive information flow, and provides a highly stable technical foundation for next-generation intelligent search of massive information and cloud services for educational content.

The application relates to the technical field of data management control, and particularly discloses a data management control method and system based on a maternal and infant one-stop learning cloud platform, which comprises the following steps: based on real-time collected maternal and infant physiological data, maternal and infant historical health records and user learning behavior data, a multi-modal maternal and infant user portrait containing dynamic health state labels and dynamic learning ability labels is constructed; based on multi-modal maternal and infant user portraits of all maternal and infant users, multi-dimensional health horizontal comparison factors of pregnant women and fetuses or infants in each maternal and infant user under each division attribute and each division level are determined, and maternal and infant health deviations of each maternal and infant user are analyzed; based on the multi-dimensional health horizontal comparison factors of the pregnant women and the fetuses or the infants in each maternal and infant user under each division attribute and each division level and the maternal and infant health deviations, intelligent adaptation of multiple services is driven; and a closed loop from health deviation to education content, health plan and deviation improvement is formed.

This invention relates to a personalized learning resource recommendation method based on subject knowledge graphs and graph neural networks, belonging to the field of educational content recommendation. The method includes: extracting key entities and relationships between them from textbooks to construct a subject knowledge graph; establishing a preliminary learning record table based on students' behavioral data during the learning process; integrating and predicting the distribution of students' mastery of each knowledge point in the learning record table through a knowledge tracking model to construct a student profile; embedding knowledge points and their relationships from the subject knowledge graph into the student profile and updating the knowledge embedding representation in the student profile through a graph neural network; constructing a personalized recommendation model and using a hybrid recommendation method of collaborative filtering and content recommendation to push learning resources that match the student's current learning status. This invention can accurately capture students' personalized needs in multiple dimensions, effectively improving the accuracy, generalization, and interpretability of recommended content.

A system and method are disclosed for generating personalized motivational, wellness, educational, and entertainment multimedia content using multimodal artificial intelligence. The system constructs an Emotion State Vector (ESV) from user inputs, diaries, biometric data, and context. A Modality Script Generator (MSG) defines time-coded orchestration across music, narration, captions, and visuals. Scripts execute via a Model-Agnostic Adapter Layer (MAAL) across heterogeneous Al engines. A Synchronization Graph (SyncGraph) maintains acceptable inter-modal drift, while a Dynamic Media Formatter (DMF) applies bounded repair policies and packages synchronized outputs with timed metadata. A Policy / Constraint Graph enforces rights and provenance, logged in a Transform Manifest.

The present disclosure provides a system and a method for authoring and automatically digitizing the printed and well as handwritten educational content inside an input image with a plurality of input characters. The method comprises the steps of receiving and processing an input image by a pre-processing module to identify and obtain a plurality of input characters from the input image, digitizing the plurality of input characters into the output string representations by a digitizing module, combining the plurality of output string representations using a post-processing module which is outputted in a computer-readable format, delineative text, and a markup language. Further, the system is based on the Convolution Neural Network (CNN) that may provide an embedded editing module allowing one or more users to make corrections and create new content. The CNN system may be retrained iteratively based on the editing performed by the users on the output strings representations to ensure high accuracy, better performance, and to avoid / reduce the error rate.

We provide the system. [Solution] An information processing device that analyzes user registration information and automatically screens the suitability of membership, A recommendation device that analyzes the user's learning history and recommends relevant educational content, An access control device for a company to access the learning history of the aforementioned user and conduct recruitment activities, A system that includes this.

This invention belongs to the field of educational artificial intelligence technology. It discloses a large-scale educational model architecture for personalized learning. Through a "knowledge graph-model joint fine-tuning-knowledge distillation" framework, it addresses problems in educational AI applications such as insufficient knowledge accuracy, high deployment costs, and inadequate personalized support. This invention first constructs an educational knowledge graph, encoding structured knowledge through a relation-type-aware graph neural network. Then, it fuses this knowledge with a large language model through cross-attention, optimizing the model's understanding of educational content through a joint loss function. Finally, it generates lightweight vertical category models through multi-level knowledge distillation. This invention combines user learning profiles and knowledge forgetting patterns to provide learners with personalized content and learning paths. This invention significantly improves the reliability of educational content, effectively reduces model illusions, greatly lowers deployment costs, and achieves accurate identification of knowledge gaps and scientific learning path planning.

An object of a system according to an embodiment is to effectively improve the financial literacy of young people.SOLUTION: A system in accordance with an embodiment includes a micro-learning financial app, a AI guided virtual stock trading game app, and a community platform for financial literacy improvement. The micro-learning financial application provides financial education content that can be learned in a short time. The virtual-guided virtual stock trading game app assists users in learning practical financial knowledge through AI stock trading. A community platform for improving financial literacy provides a place for users to share information and learn from each other.SELECTED DRAWING: Figure 1

An electronic device and method for providing parent education content regarding children's problematic behaviors, according to one embodiment of the present invention, may comprise: a memory; a display; and a control unit that executes a digital therapeutic application in response to a user's execution request, controls the display to display a screen of the digital therapeutic application, controls the memory to store record information inputted by the user and including daily behavior records and observation records for a specific child, analyzes the stored record information to output education content for reducing a specific problematic behavior in a predetermined order, compares additional record information inputted after the education content is outputted with the stored record information, and controls the display to display an analysis result of the record information. Various other embodiments are possible.

A system and method combine programmatic control and a guided and constrained Artificial Intelligence (AI) engine generate background images aligned with educational content in an online learning platform is disclosed. The integrated programmatically controlled system and guided and constrained AI engine perform operations including collecting educational content such as questions, correct answers, educational standards, and curricula associated with a user's online learning session. The collected content is analyzed to integrate relevant information and construct a detailed narrative. This narrative is then used to generate and refine text prompts via natural language processing techniques, which guide the AI engine in producing realistic background images. These images are designed to be visually appealing, contextually relevant, and to enhance user engagement and learning experience.

To further improve the comfort when using digital content data for education. [Solution] An education support system according to an embodiment includes a server device, a terminal device, and a communication management device. The server device has a database storing educational content data, including digital textbooks. The terminal device is connectable to an on-premise network of an educational facility and is configured to make the content data available. The communication management device manages communications between the on-premise network and the server device. The communication management device also has a cache function that acquires at least a portion of the content data from the server device and temporarily stores it as cache data. When a request to use the content data is received from a terminal device connected to the on-premise network, the communication management device causes the terminal device to use the cached data of the content data in response to the request.

The invention relates to the technical field of medical health information, and particularly discloses an esophageal cancer postoperative rehabilitation education strategy construction method and system based on a knowledge graph. A field ontology model fused with a diet tendency entity is constructed, a structured postoperative rehabilitation knowledge graph is created, a patient rehabilitation state model is designed, evaluation and tagging processes of individualized diet tendency jointly influenced by genetic and environmental factors are particularly integrated, and an educational strategy matching engine is used for matching the individualized diet tendency with an educational strategy. And according to the patient multi-dimensional state instance including the diet tendency label, activating the corresponding matching rule, intelligently retrieving and filtering the education content which not only conforms to the current rehabilitation stage and health problem, but also adapts to the specific diet tendency from the knowledge graph, and finally integrating to generate a highly personalized rehabilitation education strategy. According to the invention, the conversion from universal education to precise and individualized guidance is realized, and the pertinence and practicability of rehabilitation education are remarkably improved.