249 results about "Educational content" patented technology

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.

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.

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.

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.

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.

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 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.

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.

The invention discloses an AI support interactive education system based on digital situation simulation, which comprises a digital situation simulation module based on system architecture, an AI interactive teaching module and a data management analysis module, and is characterized in that the digital situation simulation module is a comprehensive module integrating situation construction and dynamic adjustment functions; the education content is presented to a learner in a highly real and interactive digital situation form by using graphic rendering and artificial intelligence technologies; the AI interactive teaching module is a comprehensive module integrating intelligent guiding and interactive feedback functions, and natural language processing and machine learning technologies in the artificial intelligence technology are utilized to provide personalized learning paths, resource recommendation and instant learning feedback for learners. Compared with the prior art, the method has the advantages that a highly real and interactive digital situation is achieved; intelligent adjustment and content personalization are realized; and comprehensive data acquisition and analysis are realized.

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 education content recommendation, in particular to a higher vocational course content recommendation method and system based on big data analysis, and the method comprises the following steps: obtaining course task labels and matching knowledge points, extracting task-type knowledge points, analyzing post responsibility information, and delimiting post capability levels. Learning behavior records are recognized, silent learning behaviors are detected, capability attenuation is evaluated, content pushing priority is adjusted, operation intensity is extracted, a pushing period is adjusted, and a resource putting adjustment result is generated. According to the method, the matching relation between the task labels and the knowledge points is constructed, post responsibility grade division and the learning behavior response state are combined, the adaptation accuracy between the course content and the ability requirement is improved, the push rhythm and the content priority ranking of the recommended content are optimized by utilizing an ability attenuation evaluation and learning load adjustment mechanism, and the recommendation efficiency is improved. Course resource distribution better fits learning rhythm and post guidance, and the dynamic response capability of a recommended path to an individual state is enhanced.

The invention relates to the technical field of teaching platforms, in particular to a teaching platform video management method and system, and the method comprises the following steps: extracting a subtitle text or audio transcription in a video based on a video data index, carrying out the semantic content processing, evaluating the similarity with the existing teaching video content, and analyzing the timeliness of the teaching video; and obtaining a video demand index. According to the method, deep semantic analysis and real-time updating monitoring are carried out on the teaching video content, the timeliness and correlation of the education content are greatly improved, the similarity and timeliness of the teaching video and the existing content are accurately evaluated, it is ensured that video data closely correspond to education requirements, the latest dynamic state of the education field is reflected in time, and the education efficiency is improved. The function of monitoring the video uploading and transcoding progress in real time not only improves the management efficiency, but also enhances the transparency of the process, and the quality and adaptability of teaching materials are effectively ensured through automatic video quality detection and feedback-based content optimization.

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 computerized method for automated educational simulation creation comprising: retrieving and ingesting a set of audio recordings and associated metadata; converting the audio recordings to a plurality of digital assets; discovering a trending issues based on a search of the metadata; identifying a set of key assets from the set of audio recordings and associated metadata; automatically creating a customized training simulation based on the set of key assets; and automatically publishing the customized training simulation by publishing and distributing customized training simulations to a plurality of agents

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.