Personalized education robot architecture based on large model
The educational robot architecture, which utilizes large-scale pre-trained models and a personalized learning engine, addresses the shortcomings of existing education systems in terms of personalization and interactivity. It enables personalized learning paths and real-time feedback, thereby improving learning effectiveness and efficiency.
Patent Information
- Application Number
- CN202411323335.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2025-01-10
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing intelligent education systems cannot dynamically adjust to individual learners' differences, lack flexibility and interactivity, and struggle to provide personalized learning paths and real-time feedback, resulting in poor learning outcomes.
The system adopts a personalized educational robot architecture based on a large-scale pre-trained model, including a user interface module, a natural language understanding module, a model pre-training module, a personalized learning engine, an interactive dialogue module, and an evaluation and feedback module. Through multi-layer neural networks and self-attention mechanisms, it generates and adjusts learning content in real time, providing a personalized learning experience and in-depth feedback.
It enables personalized analysis based on learners' knowledge level, interests, and styles, providing personalized learning paths and multi-round interactions to improve learning effectiveness and efficiency, maintain learning motivation and interest, adapt to changes in the learning environment, and provide in-depth learning feedback.
Smart Images
Figure CN119294859A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a large model-based personalized educational robot architecture. Background Art
[0002] Existing intelligent education systems are usually based on preset rules or simple machine learning models, and mainly rely on fixed course content and teaching strategies. This "preset" teaching method ignores the individual differences of learners and cannot be dynamically adjusted according to the learners' knowledge background, learning style and progress, resulting in poor learning results. In addition, existing systems often show deficiencies in dealing with complex learning needs and find it difficult to provide personalized learning paths for different learners.
[0003] Most existing education systems show low flexibility and responsiveness in their interactions with learners. Learners' questions often only receive standardized answers, and the system is unable to understand and respond to the specific difficulties and questions that learners encounter during the learning process. This limited interactivity hinders learners' active learning and deep thinking. In addition, current education systems often rely on pre-designed teaching content, which may not be updated and adjusted in time to meet changing learning needs. Especially when dealing with diverse and complex educational content, it is difficult for existing systems to dynamically generate high-quality learning materials that meet learners' needs, which makes learners easily feel bored or uncomfortable during use.
[0004] At the same time, in the existing education system, the real-time learning feedback mechanism is usually manifested as a simple right or wrong judgment or score statistics, lacking in-depth analysis of learners' emotions, psychological states, and dynamic learning behaviors during the learning process. Such a feedback mechanism is too superficial and it is difficult to truly help learners improve their learning methods and efficiency.
[0005] In view of the above-mentioned technical defects, a solution is now proposed. Summary of the invention
[0006] The purpose of the present invention is to utilize a large-scale pre-training model in combination with a personalized learning engine to generate tailored learning content based on the unique needs and learning style of each learner, thereby ensuring that learners learn at the optimal difficulty and content, greatly improving the learning effect. At the same time, by generating and adjusting learning content in real time, learners can obtain timely help and guidance during the learning process, avoiding the problem that fixed content cannot adapt to changing needs, thereby maintaining continuous learning motivation and progress.
[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a personalized educational robot architecture based on a large model, including a user interface module, a natural language understanding module, a model pre-training module, a personalized learning engine, an interactive dialogue module and an evaluation and feedback module, characterized in that the user interface module is bidirectionally connected to the natural language understanding module and the evaluation and feedback module, the connection is to the model pre-training module, the model pre-training module is interconnected with the natural language understanding module and the personalized learning engine, the personalized learning engine is connected to the interactive dialogue module, and the interactive dialogue module is connected to the evaluation and feedback module.
[0008] The user interface module is used to obtain the user's learning needs through the interface, obtain the feedback results of the evaluation and feedback module, and make further choices based on the feedback results;
[0009] The natural language understanding module is used to parse the user's natural language input according to the user's learning needs, and understand the user's learning needs and questions based on the natural language input;
[0010] The model pre-training module is used to generate learning content or answer user questions based on the user's learning needs and questions;
[0011] The personalized learning engine is used to optimize the content generated by the large-scale pre-training model to better match the personalized needs of users;
[0012] The interactive dialogue module ensures that the learning content is effectively delivered and increases the interactivity of learning by conducting multiple rounds of interactions with users.
[0013] The real-time evaluation and feedback module is used to evaluate the user's learning performance, provide real-time learning feedback, and send the feedback results to the user interface module.
[0014] Furthermore, the model pre-training module adopts a large-scale pre-training model, which is specifically trained through massive text data, has powerful natural language understanding and generation capabilities, and captures the complex structure and semantic relationship in the language through multi-layer neural networks and self-attention mechanisms.
[0015] Furthermore, the large-scale pre-trained model is used to dynamically generate educational content that meets the personalized needs of users. The large-scale pre-trained model uses the input context information to generate high-quality learning materials, such as examples, answers, dialogue examples, etc., thereby achieving a personalized educational experience.
[0016] Furthermore, the personalized learning engine is based on a recommendation system and a cluster analysis algorithm, and by analyzing the user's historical learning data, interests, and learning style, it dynamically adjusts the generated learning content, optimizes the learning experience, and ensures that the content is highly matched with the user's needs.
[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:
[0018] 1. This large-model-based personalized educational robot architecture dynamically generates personalized learning content by introducing a large-scale pre-trained model and combining learners' real-time feedback and historical data. It can conduct a comprehensive analysis based on multiple dimensions such as learners' knowledge level, interests, and learning style, and adjust the learning path in real time, thereby providing a truly personalized learning experience.
[0019] 2. This personalized educational robot architecture based on a large model provides a large-scale dialogue model based on natural language processing, which can interact with learners in multiple rounds. By intelligently analyzing learners' questions and responses, the system can generate more targeted and inspiring responses, helping learners to deeply understand knowledge points and cultivate their critical thinking ability.
[0020] 3. This personalized educational robot architecture based on large models utilizes the powerful generation capabilities of large models to generate high-quality learning materials in real time according to learners' needs, covering a variety of subjects and content forms. This dynamic generation capability ensures the timeliness and adaptability of learning materials, enabling learners to continuously obtain the best learning content in a changing learning environment.
[0021] 4. This large-scale model-based personalized educational robot architecture conducts in-depth analysis of learners' learning behaviors through an embedded real-time learning feedback system. It not only provides right and wrong judgments, but also can identify learners' emotional fluctuations, attention changes and other information. Based on these feedbacks, it dynamically adjusts teaching strategies and gives learners detailed improvement suggestions and incentive mechanisms, thereby significantly improving learning efficiency and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 The overall framework structure schematic diagram of the present invention is shown;
[0023] Figure 2 A schematic diagram of the system workflow of the present invention is shown. DETAILED DESCRIPTION
[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0025] Example:
[0026] like Figure 1-2As shown, the personalized educational robot architecture based on the large model includes a user interface module, a natural language understanding module, a model pre-training module, a personalized learning engine, an interactive dialogue module and an evaluation and feedback module, wherein the user interface module is bidirectionally connected to the natural language understanding module and the evaluation and feedback module, and is connected to the model pre-training module, the model pre-training module is interconnected with the natural language understanding module and the personalized learning engine, the personalized learning engine is connected to the interactive dialogue module, and the interactive dialogue module is connected to the evaluation and feedback module.
[0027] The user interface module is used to obtain the user's learning needs through the interface, obtain the feedback results of the evaluation and feedback module, and make further choices based on the feedback results;
[0028] The natural language understanding module is used to parse the user's natural language input according to the user's learning needs, and understand the user's learning needs and questions based on the natural language input;
[0029] The model pre-training module is used to generate learning content or answer user questions based on the user's learning needs and questions;
[0030] The personalized learning engine is used to optimize the content generated by large-scale pre-trained models to better match the personalized needs of users;
[0031] The interactive dialogue module ensures that learning content is effectively delivered and increases the interactivity of learning through multiple rounds of interaction with users.
[0032] The real-time evaluation and feedback module is used to evaluate the user's learning performance, provide real-time learning feedback, and send the feedback results to the user interface module.
[0033] The model pre-training module uses a large-scale pre-training model, which is trained through massive text data. It has powerful natural language understanding and generation capabilities, and captures the complex structure and semantic relationships in the language through multi-layer neural networks and self-attention mechanisms.
[0034] Large-scale pre-trained models are used to dynamically generate educational content that meets the personalized needs of users. Large-scale pre-trained models use input contextual information to generate high-quality learning materials, such as examples, answers, dialogue examples, etc., thereby achieving a personalized educational experience.
[0035] The learning engine is based on a recommendation system and cluster analysis algorithm. It analyzes users' historical learning data, interests, and learning styles to dynamically adjust the generated learning content, optimize the learning experience, and ensure that the content is highly matched with user needs.
[0036] Implementation 1: Take a high school student who is preparing for an oral English test as an example, with "environmental protection" and "technological development" as the learning topics:
[0037] 1. User input requirements: Open the personalized English learning assistant and input the learning requirements through the user interface module: "Improve oral expression ability on topics such as environmental protection and scientific and technological development";
[0038] 2. Natural language understanding: The natural language understanding module parses the user's input, understands his learning needs, and identifies the key topics as "environmental protection" and "scientific and technological development";
[0039] 3. Content generation and optimization: The large-scale pre-trained model generates relevant English practice materials and dialogue examples on the topics of "environmental protection" and "scientific and technological development" according to the user's learning needs. The personalized learning engine adjusts the generated content according to the user's learning history to make it moderately difficult and include relevant issues where the user was weak in the past;
[0040] 4. Interactive dialogue: The user conducts a simulated dialogue with the interactive dialogue module. The interactive dialogue module plays the role of an examiner and conducts multiple rounds of dialogues with the user on "environmental protection" and "technological development". It corrects the user's grammatical errors in real time and provides better expressions.
[0041] 5. Real-time evaluation and feedback: After each round of conversation, the evaluation and feedback module analyzes the user's performance and generates a learning report, which points out the user's progress and areas that still need improvement, and recommends further practice materials for the user;
[0042] 6. Feedback display: The user views the feedback report on the user interface module and continues learning based on the suggestions.
[0043] Implementation effect:
[0044] After several weeks of study, with the help of the system, the user significantly improved his oral expression ability on the topics of "environmental protection" and "scientific and technological development". In the mock exam, the user's expression was more fluent and his logic was clearer, and he finally achieved excellent results in the actual English oral exam.
[0045] Implementation 2: Take junior high school students who have learning needs for geometry problems in junior high school data as an example:
[0046] Implementation process
[0047] 1. User input requirements: Users input learning requirements through the user interface module: "understand the basic concepts of geometry and improve problem-solving skills";
[0048] 2. Natural language understanding: The natural language understanding module parses the user's input and identifies the key learning needs as "geometric basic concepts" and "problem-solving ability";
[0049] 3. Content generation and optimization: A large-scale pre-trained model generates learning materials on basic geometric concepts, including the definition, properties, and common theorems of geometric figures. The personalized learning engine adjusts the difficulty of the content based on the user's learning history and generates targeted exercises.
[0050] 4. Interactive dialogue: Users interact with the system through the interactive dialogue module. The interactive dialogue module guides users to understand geometric concepts in the form of questions and answers, and provides guidance on problem-solving ideas in real time. It also helps users understand problems intuitively by dynamically drawing geometric figures;
[0051] 5. Real-time evaluation and feedback: After each practice, the evaluation and feedback module analyzes the user's problem-solving process, points out her mistakes, and provides detailed problem-solving steps, and adjusts the subsequent learning content according to the user's performance.
[0052] 6. Feedback display: Users view the feedback report and relearn the difficult content based on the suggestions displayed in the user interface module.
[0053] Implementation effect:
[0054] Users gradually mastered the basic concepts of geometry and were able to solve geometry problems proficiently. Their geometry scores improved significantly in the final exams, from hovering just above the passing line to being among the top.
[0055] Implementation 3: Based on the needs of vocational skills training and education, take a skilled worker who needs to learn welding technology as an example:
[0056] Implementation process
[0057] 1. User input requirements: Users input learning requirements through the user interface module: "Master the key points of new welding technology";
[0058] 2. Natural language understanding: The natural language understanding module parses the user's input and understands his needs as "the key points of operation of the new welding technology";
[0059] 3. Content generation and optimization: A large-scale pre-trained model generates learning materials about new welding technologies, including operation steps, precautions, and common problems. The personalized learning engine adjusts the depth and focus of the content based on the user's work background and operation experience;
[0060] 4. Interactive dialogue: Users can simulate the operation process through the interactive dialogue module, which guides users to complete each operation step through videos and diagrams, and corrects errors in operation in real time;
[0061] 5. Real-time evaluation and feedback: The evaluation and feedback module monitors the user's operation performance in real time and generates an operation evaluation report, suggesting that the user practice repeatedly on certain key steps;
[0062] 6. Feedback display: Users view feedback reports and perform intensive training based on the suggestions displayed in the user interface module, which is also applicable to vocational skill training education.
[0063] Implementation effect:
[0064] The user mastered the new welding technology in a short period of time and performed well in actual operation, becoming a promoter of the new technology in the workshop.
[0065] The present invention utilizes a large-scale pre-training model and combines it with a personalized learning engine to generate tailored learning content based on each learner's unique needs and learning style, ensuring that learners learn at the optimal difficulty and content, greatly improving the learning effect. At the same time, by generating and adjusting learning content in real time, learners can obtain timely help and guidance during the learning process, avoiding the problem that fixed content cannot adapt to changing needs, thereby maintaining continuous learning motivation and progress.
[0066] Through the interactive dialogue module, multiple rounds of interaction with learners are achieved, making the learning process more vivid and interesting. Learners can ask questions at any time and get instant responses, which greatly enhances the sense of participation and interactivity in learning. At the same time, the learner's performance can be analyzed in real time, and the learning strategy and content can be adjusted immediately. This instant feedback mechanism not only helps learners correct mistakes, but also stimulates their interest in learning and maintains their enthusiasm for learning;
[0067] The personalized learning engine provided by the present invention can automatically optimize the learning path according to the learner's historical learning data and current performance, so that the learner can use the learning time more efficiently and quickly master the key knowledge, thereby greatly improving the learning efficiency. It can dynamically generate and recommend the most suitable content for the current learning stage according to the learner's actual needs, avoiding the learner from wasting time on redundant or inappropriate materials;
[0068] The present invention is not only applicable to K-12 basic education, but can also be extended to various educational scenarios such as higher education and vocational training. Whether it is language learning, mathematics, physics and chemistry courses, or vocational skills training, the system can provide personalized learning content and paths. At the same time, by continuously updating and training large models, the system can continuously improve content generation and optimization capabilities to adapt to educational needs in different fields and levels. This scalability makes the present invention have broad application prospects in the field of education;
[0069] The system architecture provided by the present invention can be deployed on a large scale, reducing the dependence on human teachers, significantly reducing the cost of education while maintaining high-quality education, and enabling more learners to afford personalized high-quality education. Personalized education is achieved through the present invention, and the introduction of large-scale pre-training models enables personalized learning content to be automatically generated and popularized to more learners, breaking the problem of uneven distribution of traditional educational resources.
[0070] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.
Claims
1. A personalized educational robot architecture based on a large model, including a user interface module, a natural language understanding module, a model pre-training module, a personalized learning engine, an interactive dialogue module, and an evaluation and feedback module, characterized in that: The user interface module is bidirectionally connected to the natural language understanding module and the evaluation and feedback module, which is connected to the model pre-training module. The model pre-training module is interconnected with the natural language understanding module and the personalized learning engine. The personalized learning engine is connected to the interactive dialogue module, and the interactive dialogue module is connected to the evaluation and feedback module. The user interface module is used to obtain the user's learning needs through the interface, obtain the feedback results of the evaluation and feedback module, and make further choices based on the feedback results; The natural language understanding module is used to parse the user's natural language input according to the user's learning needs, and understand the user's learning needs and questions based on the natural language input; The model pre-training module is used to generate learning content or answer user questions based on the user's learning needs and questions; The personalized learning engine is used to optimize the content generated by the large-scale pre-training model to better match the personalized needs of users; The interactive dialogue module conducts multiple rounds of interactions with the user. The real-time evaluation and feedback module is used to evaluate the user's learning performance, provide real-time learning feedback, and send the feedback results to the user interface module.
2. The large model-based personalized educational robot architecture according to claim 1, characterized in that: The model pre-training module adopts a large-scale pre-training model, which is specifically trained through massive text data, and captures the complex structure and semantic relationship in the language through multi-layer neural network and self-attention mechanism.
3. The large model-based personalized educational robot architecture according to claim 2, characterized in that: The large-scale pre-training model is used to dynamically generate educational content that meets the personalized needs of users. The large-scale pre-training model uses input context information to generate high-quality learning materials, such as examples, answers, dialogue examples, etc.
4. The large model-based personalized educational robot architecture according to claim 1, characterized in that: The personalized learning engine is based on a recommendation system and a cluster analysis algorithm. It analyzes the user's historical learning data, interests, and learning style, dynamically adjusts the generated learning content, and optimizes the learning experience.