AI (artificial intelligence) support interactive education system based on digital situation simulation

Abstract

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.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and specifically refers to an AI-based interactive education system for daycare based on digital scenario simulation. Background Art

[0002] With the rapid development of information technology, digital education has become an important trend in the field of education. Most existing digital education systems adopt fixed teaching content and forms, lacking high interaction with learners and personalized customization. In addition, although there are already some systems using AI technology for intelligent teaching, they still have deficiencies in the richness of scenario simulation, dynamic adjustment ability, and the depth of data processing and analysis.

[0003] For example, the patent document with the patent number CN201810123456.X describes an AI-based online education system. Although this system can provide personalized learning suggestions according to the learning history and ability level of learners, in terms of scenario simulation, it is limited to simple graphic or video displays and lacks immersive and dynamic adjustment capabilities.

[0004] Another example is that the utility model patent with the patent number CN201920234567.X proposes an intelligent interactive teaching device. This device can achieve interaction with learners through voice recognition and interaction technology, but it is still single in scenario construction and the formulation of personalized learning paths and cannot be intelligently adjusted according to the real-time feedback and learning progress of learners.

[0005] Therefore, the digital education systems in the prior art still have deficiencies in aspects such as the richness of scenario simulation, dynamic adjustment ability, personalized teaching, and data processing and analysis, and cannot meet the needs of learners for a highly realistic, interactive, and personalized learning experience. Summary of the Invention

[0006] The present invention aims to solve the deficiencies existing in the prior digital education systems in aspects such as the richness of scenario simulation, dynamic adjustment ability, personalized teaching, and data processing and analysis. Through the organic combination of a digital scenario simulation module, an AI interactive teaching module, and a data management and analysis module, the present invention provides a daycare interactive education system that can highly simulate real scenarios, dynamically adjust teaching content according to learners' feedback, provide personalized learning paths and instant feedback, and comprehensively collect and analyze learners' data.

[0007] Specifically, the technical solution provided by the present invention is: an AI nursery interactive education system based on digital scenario simulation, including a digital scenario simulation module, an AI interactive teaching module, and a data management and analysis module based on the system architecture. The digital scenario simulation module is a comprehensive module integrating scenario construction and dynamic adjustment functions. Using graphics rendering and artificial intelligence technologies, it presents educational content to learners in the form of highly realistic and interactive digital scenarios, with high scalability and flexibility. It can be intelligently adjusted according to the real-time feedback and learning progress of learners to provide personalized learning experiences. In terms of architecture, the digital scenario simulation module includes a scenario construction module and a scenario adjustment module. The scenario construction module is used to construct digital educational scenarios according to educational goals and content, capable of simulating real or fictional environments to provide immersive experiences for learners. The scenario adjustment module is used to dynamically adjust the difficulty and complexity of the scenario according to the learning progress and feedback of learners to ensure the pertinence and effectiveness of education.

[0008] The AI interactive teaching module is a comprehensive module integrating intelligent guidance and interactive feedback functions. Using natural language processing and machine learning technologies in artificial intelligence, it provides personalized learning paths, resource recommendations, and instant learning feedback for learners. This module has data processing and analysis capabilities and can be intelligently adapted according to the learning styles and interests of learners to achieve efficient and personalized teaching. In terms of architecture, the AI interactive teaching module includes an intelligent guidance module and an interactive feedback module. The intelligent guidance module uses artificial intelligence technologies to provide personalized learning paths and guidance according to the learning behaviors and performances of learners, and provides corresponding teaching resources and activities by identifying the learning needs of learners. The interactive feedback module is used to monitor the learning status of learners in real time, provide instant feedback, analyze the learning achievements of learners, point out existing problems, and provide improvement suggestions.

[0009] The data management and analysis module is a comprehensive module integrating data collection, storage, processing, and analysis functions. Using big data technologies and machine learning algorithms, it comprehensively collects and deeply analyzes the data generated by learners during the learning process, and provides valuable information and references for educators. This module has high-efficient data processing capabilities and can perform customized data analysis and report generation according to the needs of educators. In terms of architecture, the data management and analysis module includes a data collection module and a data analysis module. The data collection module is used to collect the learning data of learners, including learning duration, learning progress, and learning achievement data, ensuring the accuracy and integrity of the data and providing a basis for subsequent analysis. The data analysis module deeply analyzes the collected learning data, mines the learning rules and trends of learners, and generates detailed learning reports to provide valuable reference information for educators and learners.

[0010] Furthermore, the functions of the digital scenario simulation module include: (1) Comprehensive scenario construction: By integrating various scenario elements (such as characters, environments, events, etc.), a rich and diverse digital education scenario is constructed. The scenario elements include characters, environments, and events. The scenarios include simulating real-world scenes and fictional, educationally meaningful scenarios; (2) Intelligent scenario adjustment: According to the learner's learning progress, interests, and feedback, the difficulty, complexity, and element combination of the scenario are dynamically adjusted to ensure the adaptability and challenge of the learning content.

[0011] Furthermore, the functions of the AI interactive teaching module include: (1) Personalized learning path: By analyzing the learner's learning history, interests, and ability levels, a personalized learning plan and resource recommendations are developed for the learner; (2) Instant interactive feedback: The learner's learning status and learning outcomes are monitored in real time, and instant feedback and suggestions are provided to the learner in the form of text, pictures, and videos.

[0012] Furthermore, the functions of the data management and analysis module include: (1) Comprehensive data collection: Collect various data of learners during the learning process, including learning duration, learning progress, and learning outcome data; (2) In-depth data analysis: Use machine learning algorithms to deeply analyze the collected data to discover the learning patterns and trends of learners; (3) Customized report generation: Generate detailed learning reports and charts according to the needs of educators to provide valuable reference information for educators.

[0013] Furthermore, the data interaction process between the digital scenario simulation module and the AI interactive teaching module includes: (1) Data request: The digital scenario simulation module requests relevant learner data from the AI interactive teaching module according to the learner's current learning status provided by the AI interactive teaching module, including learning progress and mastered knowledge points; (2) Scenario adjustment: After receiving the learner data returned by the AI interactive teaching module, the digital scenario simulation module dynamically adjusts the difficulty, content, and structure of the scenario simulation according to these data to ensure that the simulated scenario matches the actual learning needs of the learner; (3) Data feedback: Send the adjusted scenario simulation data back to the AI interactive teaching module for use in the teaching process by the AI interactive teaching module.

[0014] Furthermore, the data interaction process between the digital scenario simulation module and the data management and analysis module includes: (1) Data reporting: During the scenario simulation process, the digital scenario simulation module reports the learner's behavior data to the data management and analysis module in real time or periodically. The behavior data includes participation, completion rate, and error rate; (2) Data request: The digital scenario simulation module can request the learner's historical learning data and learning trend prediction analysis results from the data management and analysis module as needed for further optimizing the design of the scenario simulation.

[0015] The advantages of the present invention compared with the prior art are as follows: (1) Highly realistic and interactive digital scenarios: Through graphics rendering and artificial intelligence technologies, the present invention can construct a rich variety of digital educational scenarios, providing learners with immersive experiences; (2) Intelligent adjustment and content personalization: According to the real-time feedback and learning progress of learners, the present invention can dynamically adjust the difficulty, content, and structure of scenario simulations to ensure that the simulated scenarios match the actual learning needs of learners. At the same time, the AI interactive teaching module can be intelligently adapted according to the learning styles and interests of learners, providing personalized learning paths and resource recommendations; (3) Comprehensive data collection and analysis: The data management and analysis module can comprehensively collect various data of learners during the learning process and use machine learning algorithms for in-depth analysis to explore the learning rules and trends of learners. This provides valuable reference information for educators and helps optimize teaching content and methods. Brief Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the architecture of the present invention. Detailed Description of the Preferred Embodiments

[0017] The present invention will be further described in detail below with reference to the accompanying drawings.

[0018] Embodiment 1

[0019] As Figure 1 shown, this embodiment proposes an AI daycare interactive education system based on digital scenario simulation, including:

[0020] (1) Digital scenario simulation module: This module integrates scenario construction and dynamic adjustment functions, and uses graphics rendering and artificial intelligence technologies to present educational content to learners in the form of highly realistic and interactive digital scenarios. This module has high scalability and flexibility, and can be intelligently adjusted according to the real-time feedback and learning progress of learners to provide personalized learning experiences.

[0021] (2) AI interactive teaching module: This module integrates intelligent guidance and interactive feedback functions, and uses natural language processing and machine learning technologies to provide learners with personalized learning paths, resource recommendations, and instant learning feedback. This module can be intelligently adapted according to the learning styles and interests of learners to achieve efficient and personalized teaching.

[0022] (3) Data management and analysis module: This module integrates data collection, storage, processing, and analysis functions, and uses big data technologies and machine learning algorithms to comprehensively collect, deeply analyze the data generated by learners during the learning process, and provide valuable information and references for educators.

[0023] The working process of this system mainly includes the following steps:

[0024] (1) System initialization and configuration: After the system starts, initialization operations are first performed to load necessary configuration information and resources. According to educational goals and content, relevant parameters of the digital scenario simulation module are configured, such as scenario elements, difficulty levels, etc. The learning paths, resource recommendation algorithms, etc. of the AI interactive teaching module are configured. Data collection and analysis rules of the data management and analysis module are set.

[0025] (2) Learner login and authentication: Learners log in through the system interface and enter personal information for authentication. The system assigns personalized learning resources and paths to learners based on their identity information and historical learning records.

[0026] (3) Digital scenario simulation and interactive teaching: The digital scenario simulation module constructs and presents digital educational scenarios according to configuration parameters and learners' real-time feedback. Learners conduct interactive learning in the scenarios, and the AI interactive teaching module monitors learners' learning status and learning outcomes in real time. According to learners' learning progress and feedback, the AI interactive teaching module dynamically adjusts learning paths and resource recommendations, and at the same time, the digital scenario simulation module also adjusts the difficulty and complexity of the scenarios accordingly.

[0027] (4) Data collection and analysis: The data management and analysis module collects learners' learning data in real time or regularly, including learning duration, learning progress, learning outcomes, etc. The collected data is deeply analyzed to mine learners' learning rules and trends. According to the analysis results, detailed learning reports and charts are generated to provide valuable reference information for educators.

[0028] (5) Learning feedback and report generation: The AI interactive teaching module provides instant learning feedback and suggestions to learners in the forms of text, pictures, videos, etc. The system generates personalized learning reports based on learners' learning data and analysis results for learners and educators to view.

[0029] Example 2

[0030] Based on Example 1, as Figure 1 shown, the working principle of this system is mainly based on the following key technologies:

[0031] (1) Digital scenario simulation technology: Using graphics rendering and artificial intelligence technologies, educational content is presented to learners in the form of highly realistic and interactive digital scenarios. By integrating various scenario elements (such as characters, environments, events, etc.), rich and diverse digital educational scenarios are constructed. According to learners' real-time feedback and learning progress, the difficulty, complexity, and element combinations of the scenarios are dynamically adjusted to ensure the adaptability and challenge of the learning content.

[0032] (2) Artificial intelligence technology: Utilize natural language processing and machine learning technologies to achieve intelligent guidance and interactive feedback functions. By analyzing the learning history, interests, and ability levels of learners, develop personalized learning plans and resource recommendations for learners. Monitor the learning status and learning outcomes of learners in real time, and provide immediate feedback and suggestions.

[0033] (3) Data management technology: Utilize big data technology and machine learning algorithms to comprehensively collect and deeply analyze the data generated by learners during the learning process. Mine the learning rules and trends of learners to provide valuable reference information for educators. Generate detailed learning reports and charts according to the needs of educators.

[0034] Embodiment Three

[0035] Based on Embodiment One, as Figure 1 shown, the algorithm steps of the scenario construction module:

[0036] (1) Educational goal analysis: Input educational goals (such as "understanding the concept of fractions" in mathematics), content outline; Analyze the educational goals to determine key concepts, learning steps, and required scenario elements;

[0037] (2) Scenario element selection: Input key concepts, learning steps; According to the content outline and key concepts, select appropriate elements (such as characters, scenes, tasks, etc.) from the preset scenario element library.

[0038] (3) Scenario construction: Input the selected scenario elements; Use a graphics rendering engine (such as Unity, Unreal Engine) and a scene editor to combine the scenario elements into a complete digital educational scenario; Output the constructed digital scenario, including all necessary visual, auditory, and interactive elements.

[0039] The following gives some execution codes:

[0040]

[0041] The algorithm steps of the scenario adjustment module:

[0042] (1) Learning progress assessment: Input the interaction data of learners in the scenario (such as the time to complete tasks, error rate, etc.); Process and calculate learning progress indicators, such as "mastery level", "proficiency level", etc.;

[0043] (2) Difficulty and complexity adjustment: Input learning progress indicators, preset difficulty adjustment strategies; According to the learning progress indicators, use a difficulty adjustment algorithm (such as linear adjustment, exponential adjustment, etc.) to calculate new difficulty and complexity values; Output the adjusted scenario difficulty and complexity parameters.

[0044] Difficulty adjustment formula: new_difficulty = base_difficulty * (1 + adjustment_factor * learning_progress_deviation)

[0045] Among them, base_difficulty is the initial difficulty, adjustment_factor is the adjustment coefficient (set according to the strategy), and learning_progress_deviation is the deviation between the learning progress and the expected progress (a positive number indicates ahead, and a negative number indicates behind).

[0046] (3) Situation update: Input the adjusted difficulty and complexity parameters; according to the new parameters, update the elements, task difficulty, interaction logic, etc. in the situation.

[0047] The following gives some execution codes:

[0048]

[0049]

[0050] Example 4

[0051] Based on Example 1, as Figure 1 shown, the algorithm steps of the intelligent guidance module are as follows:

[0052] (1) Learning behavior data collection: Input all the behavior data of the learner on the platform, such as clicks, browsing, answering questions, and stay time; use a log collection system or database to store this data to ensure the integrity and accuracy of the data.

[0053] (2) Learning behavior analysis: Input the collected learning behavior data; apply the clustering analysis machine learning algorithm to analyze the learner's behavior patterns and identify their interests, strengths, and weaknesses.

[0054] The clustering analysis machine learning algorithm uses the K-means algorithm, and its objective function is to minimize the sum of the squares of the distances from each point to its cluster center:

[0055]

[0056] Among them, C i is the i-th cluster, μ i is the center of the i-th cluster, and x is the point in the cluster.

[0057] (3) Personalized learning path generation: Input the learning behavior analysis results and the education content outline; Based on the analysis results and in combination with the education content outline, generate a personalized learning path that meets the characteristics and needs of the learner; Output the personalized learning path, including recommended learning resources, order, time, etc.

[0058] The following gives some implementation codes:

[0059]

[0060] The algorithm steps of the interactive feedback module:

[0061] (1) Learning status monitoring: Input data of the learner during real-time learning, such as answering question correct rate, reaction time, emotion indicators (such as facial expression analysis), etc.; Use real-time data processing technology of the stream processing framework to monitor the learner's learning status and identify whether they encounter difficulties, whether they are fatigued, etc. In the analysis of emotion indicators, simple threshold judgment can be used to identify the learner's emotion state, such as:

[0062]

[0063] Among them, smile_intensity is the smile intensity obtained through facial expression analysis, and threshold_happy and threshold_sad are preset thresholds.

[0064] (2) Immediate feedback generation: Input the learning status monitoring results and the preset feedback strategy. According to the monitoring results and the feedback strategy, generate immediate feedback, including encouragement, tips, explanations, etc.

[0065] (3) Output the immediate feedback content, which can be presented to the learner in forms such as text, voice, and image.

[0066] The following gives some implementation codes:

[0067]

[0068] Example Five

[0069] Based on Example One, as Figure 1 shown, the algorithm steps of the data collection module:

[0070] (1) Determine the data collection points: Input the function modules and learning processes of the education platform; According to the function modules and learning processes of the education platform, determine the learning data points to be collected, such as learning duration, answering question records, course completion rate, learning resource clicks, etc.;

[0071] (2) Configure data collection tools: Input the determined data collection points; configure corresponding data collection tools, such as log collection systems, databases, API interfaces, etc., to ensure that the required learning data can be collected in real time or regularly.

[0072] (3) Data preprocessing: Input the collected raw data; perform preprocessing operations on the raw data, such as cleaning, formatting, deduplication, etc., to ensure the accuracy and consistency of the data.

[0073] (4) Data storage: Input the preprocessed data. Store the data in a suitable database or data warehouse for subsequent data analysis and use.

[0074] The following gives some execution codes:

[0075]

[0076] Algorithm steps of the data analysis module:

[0077] (1) Data loading: Input the learning data stored in the database or data warehouse. Load the required learning data from the database or data warehouse and perform necessary format conversion and preprocessing.

[0078] (2) Descriptive analysis: Input the loaded learning data. Use statistical methods (such as mean, median, mode, standard deviation, etc.) to perform descriptive analysis on the learning data to understand the overall characteristics and distribution of the data.

[0079] (3) Association analysis: Input the results of descriptive analysis. Use the Apriori association rule mining algorithm to discover the association relationships between learning data, such as the association between learning resources and the association between learning behaviors and grades. In association analysis, the Apriori algorithm uses support and confidence to measure the strength of association rules. Support represents the frequency of the simultaneous occurrence of the antecedent and consequent in the rule, and confidence represents the probability of the consequent occurring under the condition that the antecedent occurs. The formulas are as follows:

[0080]

[0081] where D is the dataset, and A and B are item sets in the dataset.

[0082] (4) Trend analysis: Input the loaded learning data (including time series information). Use time series analysis techniques (such as ARIMA model, exponential smoothing method, etc.) to perform trend analysis on the learning data to predict the future learning behaviors and grades of learners.

[0083] (5) Learning pattern mining: Input the results of relevance analysis and trend analysis. Combining educational theories and practical teaching experience, deeply mine the learning data to discover learners' learning patterns and trends, such as preferences for learning paths and changes in learning speeds.

[0084] The following gives some execution code:

[0085] def analyze_learner_data(data):

[0086] # Data loading

[0087] loaded_data = load_data(data)

[0088] # Descriptive analysis

[0089] descriptive_stats = describe_data(loaded_data)

[0090] # Relevance analysis

[0091] associations = find_associations(loaded_data)

[0092] # Trend analysis

[0093] trends = analyze_trends(loaded_data)

[0094] # Learning pattern mining

[0095] learning_patterns = mine_learning_patterns(associations, trends, descriptive_stats)

[0096] return learning_patterns, descriptive_stats, associations, trends

[0097] The above has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An AI nursery interactive education system based on digital scenario simulation, including a digital scenario simulation module, an AI interactive teaching module, and a data management and analysis module based on the system architecture, characterized in that: The digital scenario simulation module is a comprehensive module integrating scenario construction and dynamic adjustment functions. Using graphics rendering and artificial intelligence technologies, it presents educational content to learners in a highly realistic and interactive digital scenario form. It has high scalability and flexibility, and can be intelligently adjusted according to the real-time feedback and learning progress of learners to provide personalized learning experiences. In terms of architecture, the digital scenario simulation module includes a scenario construction module and a scenario adjustment module. The scenario construction module is used to construct digital educational scenarios according to educational goals and content, and can simulate real or fictional environments to provide immersive experiences for learners. The scenario adjustment module is used to dynamically adjust the difficulty and complexity of the scenario according to the learning progress and feedback of learners to ensure the pertinence and effectiveness of education. The AI interactive teaching module is a comprehensive module integrating intelligent guidance and interactive feedback functions. Using natural language processing and machine learning technologies in artificial intelligence, it provides personalized learning paths, resource recommendations, and instant learning feedback for learners. This module has data processing and analysis capabilities, and can be intelligently adapted according to the learning styles and interests of learners to achieve efficient and personalized teaching. In terms of architecture, the AI interactive teaching module includes an intelligent guidance module and an interactive feedback module. The intelligent guidance module uses artificial intelligence technologies to provide personalized learning paths and guidance according to the learning behaviors and performances of learners, and provides corresponding teaching resources and activities by identifying the learning needs of learners. The interactive feedback module is used to monitor the learning status of learners in real time, provide instant feedback, analyze the learning achievements of learners, point out existing problems, and provide improvement suggestions. The data management and analysis module is a comprehensive module integrating data collection, storage, processing, and analysis functions. Using big data technologies and machine learning algorithms, it comprehensively collects and deeply analyzes the data generated by learners during the learning process, and provides valuable information and references for educators. This module has efficient data processing capabilities and can perform customized data analysis and report generation according to the needs of educators. In terms of architecture, the data management and analysis module includes a data collection module and a data analysis module. The data collection module is used to collect the learning data of learners, including learning duration, learning progress, and learning achievement data, to ensure the accuracy and integrity of the data and provide a basis for subsequent analysis. The data analysis module deeply analyzes the collected learning data, explores the learning rules and trends of learners, and generates detailed learning reports to provide valuable reference information for educators and learners.

2. The AI daycare interactive education system based on digital scenario simulation according to claim 1, wherein The functions of the digital scenario simulation module include: (1) Comprehensive scenario construction: By integrating various scenario elements (such as characters, environment, events, etc.), a rich and diverse digital education scenario is constructed. The scenario elements include characters, environment, and events. The scenarios include those that simulate real-world scenes and fictional, educationally meaningful scenarios; (2) Intelligent scenario adjustment: According to the learning progress, interests, and feedback of learners, the difficulty, complexity, and element combination of the scenario are dynamically adjusted to ensure the adaptability and challenge of the learning content.

3. The AI nursery interactive education system based on digital scenario simulation according to claim 1, characterized in that The functions of the AI interactive teaching module include: (1) Personalized learning path: By analyzing the learning history, interests, and ability levels of learners, a personalized learning plan and resource recommendations are developed for learners; (2) Instant interactive feedback: The learning status and learning outcomes of learners are monitored in real time, and instant feedback and suggestions are provided to learners in the form of text, pictures, and videos.

4. The AI daycare interactive education system based on digital scenario simulation according to claim 1, characterized in that The functions of the data management and analysis module include: (1) Comprehensive data collection: Collect various data of learners during the learning process, including learning duration, learning progress, and learning outcome data; (2) In-depth data analysis: Use machine learning algorithms to deeply analyze the collected data to discover the learning rules and trends of learners; (3) Customized report generation: Generate detailed learning reports and charts according to the needs of educators to provide valuable reference information for educators.

5. The AI daycare interactive education system based on digital scenario simulation according to claim 1, characterized in that The data interaction process between the digital scenario simulation module and the AI interactive teaching module includes: (1) Data request: The digital scenario simulation module requests relevant learner data, including learning progress and mastered knowledge points, from the AI interactive teaching module according to the current learning status of learners provided by the AI interactive teaching module; (2) Scenario adjustment: After receiving the learner data returned by the AI interactive teaching module, the digital scenario simulation module dynamically adjusts the difficulty, content, and structure of the scenario simulation according to this data to ensure that the simulated scenario matches the actual learning needs of learners; (3) Data feedback: Send the adjusted scenario simulation data back to the AI interactive teaching module for use in the teaching process by the AI interactive teaching module.

6. The AI-based interactive education system for daycare based on digital scenario simulation according to claim 1, characterized in that The data interaction process between the digital scenario simulation module and the data management and analysis module includes: (1) Data reporting: During the scenario simulation process, the digital scenario simulation module reports the behavior data of learners to the data management and analysis module in real time or periodically. The behavior data includes participation, completion rate, and error rate; (2) Data request: The digital scenario simulation module can request the historical learning data of learners and the results of learning trend prediction analysis from the data management and analysis module as needed for further optimizing the design of the scenario simulation.

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