AI classroom scene construction experience method and system based on meta universe

Abstract

The invention relates to the technical field of artificial intelligence and virtual reality, and provides an AI classroom scene construction experience method and system based on meta universe, and the method comprises the steps: receiving voice teaching content information, and extracting keywords in the voice teaching content information; analyzing the teaching content in the meta universe environment to obtain key information points of the teaching content; identifying teaching key points according to the key information points and the student interaction data, and optimizing teaching contents; calculating the improvement probability of the learning experience of the student according to the teaching key point and the optimized teaching content, and calculating the presentation gain of the teaching content; inputting the teaching content with enhanced experience and the teaching target into the intelligent teaching system, comparing the output of the intelligent teaching system with a preset teaching effect, controlling the behavior and teaching strategy of an AI teacher in the universe, and outputting the teaching content; according to the invention, dynamic adjustment of the teaching environment and personalized display of the teaching content are realized, and the learning experience and the teaching effect of students are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and virtual reality technology, and more specifically, to a method and system for building an AI classroom scene experience based on a metaverse. Background Art

[0002] In the current field of educational technology, virtual teaching environments and teaching methods are gradually becoming an important part of improving the educational experience. However, most existing technologies focus on the digitization and networking of teaching content, and fail to fully utilize the advantages of intelligent analysis and personalized teaching. Although some systems already support the identification of key information points through voice recognition and text data analysis, these systems still have shortcomings in dynamically building a metaverse teaching environment, optimizing teaching content in real time, and adjusting teaching strategies based on student interaction data.

[0003] The inventors have found that there are at least the following problems or defects in the prior art: First, the prior art often lacks the ability to intelligently analyze the depth and breadth of teaching content, resulting in insufficient personalization of the presentation of teaching content, making it difficult to meet the personalized learning needs of different students. Secondly, the optimization of teaching content is usually based on static, preset standards, lacks a dynamic adjustment mechanism, and cannot respond to real-time feedback in the teaching process in a timely manner. In addition, the existing systems also have defects in integrating teaching content with students' interactive data to improve teaching effectiveness, and rarely can they adjust teaching strategies based on students' real-time feedback and learning experience. Finally, most of the prior art has not built an intelligent teaching system that can achieve the combination of teaching content quality and teaching objectives to ensure that the teaching output matches the preset teaching effect, and lacks the ability to monitor and adjust teaching performance in real time. Summary of the invention

[0004] The present invention provides an AI classroom scene construction experience method and system based on the metaverse.

[0005] In a first aspect of the present invention, a method for building an AI classroom scene experience based on a metaverse is provided, comprising:

[0006] Receiving voice teaching content information, extracting keywords from the voice teaching content information, constructing a virtual teaching environment in the metaverse according to the keywords, and calculating the presentation method of the teaching content in the metaverse environment;

[0007] Analyze the teaching content in the metaverse environment to obtain key information points of the teaching content; identify the teaching focus based on the key information points and student interaction data, and optimize the teaching content to form optimized teaching content;

[0008] According to the teaching focus and optimized teaching content, the probability of improvement of students' learning experience is calculated, and the presentation gain of the teaching content is calculated. The presentation gain is integrated with the teaching content and then presented in a scenario to obtain teaching content with enhanced experience.

[0009] The teaching content and teaching objectives of the enhanced experience are input into the intelligent teaching system. The output of the intelligent teaching system is compared with the preset teaching effect, the behavior and teaching strategy of the AI ​​teacher in the metaverse are controlled, and the teaching content is output.

[0010] Furthermore, the step of receiving voice teaching content information and extracting keywords includes:

[0011] Use speech recognition technology to convert received speech information into text data, and apply natural language processing technology to extract key words in the text;

[0012] Taking the extracted key words as the center, the teaching is expanded to related knowledge points according to the preset teaching depth, and the key information points in the expanded knowledge points are used as the first key information and the second key information; the optimal value of the first key information and the second key information is taken as the key information point of the current knowledge point.

[0013] Furthermore, the metaverse teaching environment includes a knowledge depth range, a knowledge breadth range and an interaction frequency range, each range corresponds to a teaching experience threshold, and the teaching experience threshold is dynamically adjusted according to the voice content received by the metaverse teaching environment.

[0014] Furthermore, the step of identifying teaching focus based on key information points and student interaction data includes:

[0015] The ratio of the key information point of each knowledge point to the student interaction data is taken as the teaching experience quantity. The corresponding teaching experience threshold is obtained according to the range of the metaverse teaching environment where each knowledge point is located, which is used as the comparison threshold. The knowledge points with teaching experience greater than the comparison threshold are taken as teaching focuses.

[0016] Furthermore, the step of optimizing the teaching content to form optimized teaching content includes:

[0017] According to the ratio of the first key information and the second key information of each knowledge point, the information point change amount of each knowledge point is obtained, and compared with the preset change amount threshold: when the information point change amount is less than the change amount threshold, the key information point of the knowledge point is directly taken as the optimized teaching content of the knowledge point;

[0018] When the change amount of an information point is greater than or equal to the change amount threshold, if the knowledge point is not a teaching focus, the teaching content of the knowledge point remains unchanged. If the knowledge point is a teaching focus, the optimized teaching content of the previous knowledge point is taken as the optimized teaching content of the knowledge point.

[0019] Furthermore, the optimized teaching content is normalized using the ratio of the number of teaching moments in a class to the teaching depth to ensure the balance and consistency of the teaching content.

[0020] Furthermore, according to the teaching focus and the optimized teaching content, the probability of improvement of the student learning experience is calculated by the following formula:

[0021]

[0022] In the formula, pi represents the probability of improvement of students’ learning experience, ω represents the presentation gain of optimized teaching content, and C min represents the minimum presentation gain of teaching content, and θ represents the experience improvement threshold.

[0023] Furthermore, the presentation gain of the teaching content is calculated by the following formula:

[0024] G=G0×(1+α×π)(2)

[0025] In the formula, G represents the presentation gain of teaching content, G0 represents the baseline presentation gain, α represents the experience improvement coefficient, and π is pi.

[0026] Furthermore, the benchmark presentation gain is calculated using the quality of the teaching content and the teaching objectives through the following formula:

[0027]

[0028] In the formula, Q represents the quality of teaching content, and T represents the teaching goal.

[0029] In a second aspect of the present invention, there is provided an AI classroom scene construction experience system based on the metaverse, comprising:

[0030] The teaching content processing module is used to receive voice teaching content information, extract keywords, construct a virtual teaching environment in the metaverse based on the keywords, and calculate the presentation method of the teaching content in the metaverse environment;

[0031] The teaching analysis module is used to analyze the teaching content in the metaverse environment and obtain the key information points of the teaching content; identify the teaching focus based on the key information points and student interaction data, and optimize the teaching content to form optimized teaching content;

[0032] The teaching experience module is used to calculate the probability of improvement of students' learning experience based on the teaching focus and optimized teaching content, and calculate the presentation gain of the teaching content. After integrating the presentation gain with the teaching content, the module presents the content in a scenario-based manner to obtain teaching content with enhanced experience.

[0033] The intelligent teaching system module is used to input the teaching content and teaching objectives of the enhanced experience, compare the output of the intelligent teaching system with the preset teaching effect, control the behavior and teaching strategy of the AI ​​teacher in the metaverse, and output the teaching content.

[0034] In a third aspect of the present invention, an electronic device is provided, comprising: at least one processor, a memory and an input-output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any one of the methods described in the first aspect.

[0035] In a fourth aspect of the present invention, a computer-readable storage medium is provided, which includes instructions, and when the instructions are executed on a computer, the computer executes any one of the methods in the first aspect.

[0036] According to the above-mentioned embodiments of the present invention, at least the following beneficial effects are achieved: the present invention can realize in-depth analysis and optimization of teaching content by combining speech recognition, natural language processing technology and intelligent teaching system. By dynamically adjusting the presentation mode in the metaverse teaching environment, the system can identify the teaching focus according to the students' interactive data and learning experience, and optimize the teaching content accordingly. This optimization can not only improve the quality and relevance of the teaching content, but also enhance the students' learning experience and improve the teaching effect through personalized teaching strategies. In addition, the system can also calculate the benchmark presentation gain according to the quality of the teaching content and the teaching objectives, and further ensure the balance and consistency of the teaching content. In addition, the present invention can also monitor and adjust the teaching strategy in real time to ensure that the teaching output matches the preset teaching effect. Through the intelligent teaching system module, the teacher can control the behavior of the AI ​​teacher in the metaverse according to the comparison result between the system output and the preset effect, and realize the dynamic adjustment of the teaching strategy. This method can not only improve the flexibility and adaptability of teaching, but also provide students with a richer and immersive learning environment by presenting the teaching content with enhanced experience in a scenario-based manner. These improvements help to stimulate students' interest in learning, promote in-depth understanding and mastery of knowledge, and ultimately achieve efficient achievement of teaching goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0038] Figure 1 A flowchart of a method for constructing an AI classroom scene experience based on the Metaverse provided in one embodiment of the present invention;

[0039] Figure 2 A schematic diagram of the structure of an AI classroom scene construction experience system based on the metaverse provided in one embodiment of the present invention;

[0040] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the technical means, creative features, objectives and effects achieved by the embodiments of the present invention easy to understand, the embodiments of the present invention are further described below in combination with diagrams and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not used to limit the embodiments of the present invention.

[0042] A specific embodiment of the present invention discloses a method for constructing an AI classroom scene experience based on the metaverse, such as Figure 1 shown.

[0043] A method for constructing an AI classroom scene experience based on the metaverse, comprising the following steps:

[0044] Step 101, receiving voice teaching content information, extracting keywords from the voice teaching content information, constructing a virtual teaching environment in the metaverse according to the keywords, and calculating the presentation mode of the teaching content in the metaverse environment;

[0045] Step 102, analyzing the teaching content in the metaverse environment to obtain key information points of the teaching content; identifying the teaching focus according to the key information points and student interaction data, and optimizing the teaching content to form optimized teaching content;

[0046] Step 103, calculating the probability of improvement of the student learning experience according to the teaching focus and the optimized teaching content, and calculating the presentation gain of the teaching content, integrating the presentation gain with the teaching content and performing scene-based display to obtain teaching content with enhanced experience;

[0047] Step 104, input the teaching content and teaching objectives of the enhanced experience into the intelligent teaching system, compare the output of the intelligent teaching system with the preset teaching effect, control the behavior and teaching strategy of the AI ​​teacher in the metaverse, and output the teaching content.

[0048] It should be noted that in the present invention, the metaverse is a virtual digital space in which users can conduct learning activities. During implementation, the voice teaching content information generated by the teacher or AI is first received and converted into text data through voice recognition technology. Natural language processing technology is used to extract key words in the text, and these key words serve as the basis for constructing the metaverse teaching scene. Based on these keywords, the corresponding virtual environment and teaching resources can be automatically generated or selected to form a virtual teaching environment in the metaverse.

[0049] In some embodiments, speech-to-text conversion can utilize automatic speech recognition (ASR) technology to convert captured speech signals into text data, ensuring the accuracy and real-time nature of the conversion. Keyword extraction can utilize natural language processing (NLP) technology, such as a keyword extraction algorithm based on machine learning, to identify and extract keywords most relevant to the teaching content from text data.

[0050] Based on the extracted keywords, a virtual teaching environment in the metaverse can be constructed. 3D modeling and virtual reality (VR) technology can be used to design and generate relevant virtual teaching scenes based on the keywords, such as virtual laboratories, historical scene reproduction, etc.

[0051] Exemplarily, constructing a virtual teaching environment in the metaverse according to the extracted keywords includes:

[0052] First, semantic analysis is performed on the extracted keywords to understand the meaning of each keyword and the correlation between them, which provides a basis for building a teaching environment; the teaching theme and style are determined based on the keywords, and the layout and structure of the virtual teaching environment, including the background, objects and characters of the scene, are designed; virtual objects and characters are created according to the plan using 3D modeling software, ensuring that their visual style is consistent with the teaching theme; interactive elements in the virtual environment, such as clickable objects, triggerable events and explorable areas, are designed to promote student participation. A physics engine is integrated into the virtual environment to ensure that the movement and interaction of objects conform to the physical rules of the real world; audio, video and other teaching media resources are combined with the virtual environment to enhance the expressiveness of the teaching content; scripts are written for virtual characters and events to ensure that the teaching information is delivered accurately; tests are conducted in the virtual environment, feedback is collected, and adjustments are made based on the feedback to improve the teaching quality and user experience of the environment; the constructed virtual teaching environment is integrated into the Metaverse platform and deployed to ensure the stability and accessibility of the environment.

[0053] Furthermore, the teaching content presentation method calculation can design the presentation method of the teaching content in the virtual environment according to the nature and objectives of the teaching content, including:

[0054] Interactive dialogue between virtual characters used to explain concepts or demonstrate procedures.

[0055] Interactive 3D models or diagrams to help students visualize complex theories.

[0056] Gamification elements promote student engagement and learning through game tasks and challenges.

[0057] For example, the specific steps for calculating the presentation of teaching content in the metaverse environment are as follows:

[0058] Analyze the structure and characteristics of the teaching content to determine which parts are suitable for presentation through visual, auditory or tactile means; design different presentation methods based on the results of content analysis, such as 3D models, animations, charts, virtual reality interactions, etc.; design the user interface to ensure that the presentation of teaching content is coordinated with the interactive design of the virtual environment and provide intuitive user navigation; select appropriate technical tools and platforms to realize the presentation of teaching content, such as virtual reality (VR), augmented reality (AR), game engines, etc.; according to the design, produce various parts of the teaching content, including text, images, sounds, animations and interactive elements; program to implement the interactive logic of the teaching content to ensure that the presentation of the content can respond to user input and selection.

[0059] In some embodiments, the constructed virtual teaching environment is deeply analyzed to identify key information points in the teaching content. By analyzing students' interactive data, such as questions, feedback, and participation, the system can identify the teaching focus and optimize the teaching content accordingly to meet the learning needs and preferences of different students.

[0060] Furthermore, the system calculates the probability of improvement in students’ learning experience based on the optimized teaching content and teaching focus, and determines the presentation gain of the teaching content. Through a specific algorithm, these gains are applied to the scenario-based presentation of the teaching content, thereby obtaining teaching content with enhanced experience.

[0061] The system combines these enhanced teaching contents with the preset teaching objectives and inputs them into the intelligent teaching system. The intelligent teaching system adjusts the AI ​​teacher's behavior and teaching strategy in real time according to the preset algorithm to achieve the best teaching effect and outputs adaptive teaching contents.

[0062] Compared with the prior art, the method provided in this embodiment can not only dynamically adjust the teaching content according to the students' interactive data, but also provide a more vivid and personalized learning experience in the metaverse environment. This method makes the teaching content closer to the actual needs of students and improves the interactivity and effectiveness of teaching.

[0063] In some embodiments, the step of receiving voice teaching content information and extracting keywords includes:

[0064] Use speech recognition technology to convert received speech information into text data, and apply natural language processing technology to extract key words in the text;

[0065] Taking the extracted key words as the center, the teaching is expanded to related knowledge points according to the preset teaching depth, and the key information points in the expanded knowledge points are used as the first key information and the second key information; the optimal value of the first key information and the second key information is taken as the key information point of the current knowledge point.

[0066] Receiving voice teaching content information and extracting keywords is the basis of the entire process. First, the system receives the teacher's voice teaching content in real time, which can be done through a high-quality microphone array to ensure the clarity and accuracy of the voice signal. Based on the received voice teaching content, voice recognition technology is used to convert the voice signal into text data.

[0067] Furthermore, natural language processing technology is applied to analyze the converted text data to extract key words in the teaching content. Key words include not only the core concepts of the teaching subject, but also professional terms, important people, places and other elements closely related to the teaching content. The process of extracting keywords can be automated through preset algorithms, such as semantic analysis models based on deep learning.

[0068] Furthermore, with the extracted key words as the center, the relevant knowledge points are expanded according to the preset teaching depth. It is necessary to understand the teaching content and build a knowledge map, identify the knowledge points directly or indirectly related to the keywords, and classify these knowledge points according to the teaching depth. For example, for a scientific concept, the basic definition can be identified as the first key information, and the related experiments or applications can be identified as the second key information. The importance of these key information points is evaluated, and the optimal value is selected as the core content of the current knowledge point.

[0069] In some embodiments, the diversity and complexity of teaching content also need to be considered. For example, for the teaching of a historical event, key information will be extracted from different historical perspectives and influences to ensure the comprehensiveness and depth of the teaching content. In this way, a multi-dimensional information framework can be constructed for each knowledge point, which not only helps students better understand the teaching content, but also provides a basis for subsequent teaching content optimization and personalized teaching.

[0070] Furthermore, these key information points can be combined with student interaction data to identify the key and difficult points in teaching. This can be achieved by analyzing students' interactive behaviors in the metaverse environment, such as asking questions, discussing, and giving feedback. Students' interaction data can be compared with key information points to determine which knowledge points require more teaching resources and attention.

[0071] In some embodiments, the metaverse teaching environment includes a knowledge depth range, a knowledge breadth range, and an interaction frequency range, each range corresponds to a teaching experience threshold, and the teaching experience threshold is dynamically adjusted according to the voice content received by the metaverse teaching environment.

[0072] In some embodiments, the metaverse teaching environment is a multi-dimensional virtual space that can not only simulate real-world educational scenarios, but also dynamically adjust according to the depth and breadth of teaching content and the frequency of student interaction. The design of this environment can provide students with an immersive learning experience while ensuring the quality of teaching content and the efficiency of teaching activities.

[0073] Specifically, the knowledge depth range in the Metaverse teaching environment refers to the depth of concepts, theories, and skills involved in the teaching content. The knowledge breadth range covers the different fields and topics covered by the teaching content. The interaction frequency range refers to the frequency and intensity of interaction between students and teaching content, AI teachers, and other students. These three ranges are set to ensure that the teaching content has sufficient depth and breadth, and can stimulate students' participation and interest.

[0074] Furthermore, the teaching experience threshold is a key indicator for measuring teaching effectiveness, and can be dynamically adjusted based on the voice content received in the teaching environment. For example, if the voice content indicates that the teaching topic is more complex, the system will automatically increase the knowledge depth range and adjust the teaching experience threshold to accommodate deeper discussions and explorations. Similarly, if the student's interaction frequency is low, the system may increase student participation by increasing the interaction session or adjusting the interaction frequency range.

[0075] More specifically, this dynamic adjustment can be achieved by setting a series of algorithms and parameters. For example, the system can preset a series of parameters related to the depth and breadth of knowledge, such as concept difficulty, theoretical complexity, skill mastery, etc. At the same time, the system can also adjust the interaction frequency range based on students' interaction data, such as the number of questions asked, discussion participation, etc. In addition, the teaching experience threshold can be optimized through machine learning algorithms to adapt to the learning habits and preferences of different student groups.

[0076] In addition, alternative solutions can be used to further enhance the teaching experience. For example, the system can use sentiment analysis technology to evaluate students' emotional reactions and adjust teaching strategies. Or, the system can integrate virtual reality technology to provide students with a more realistic and intuitive learning environment. These alternative solutions can bring students a richer and more personalized learning experience.

[0077] In some embodiments, the step of identifying teaching focus based on key information points and student interaction data includes:

[0078] The ratio of the key information point of each knowledge point to the student interaction data is taken as the teaching experience quantity. The corresponding teaching experience threshold is obtained according to the range of the metaverse teaching environment where each knowledge point is located, which is used as the comparison threshold. The knowledge points with teaching experience greater than the comparison threshold are taken as teaching focuses.

[0079] In some embodiments, by analyzing the ratio of key information points of each knowledge point to student interaction data, it is possible to quantify the student's participation and interest level in specific teaching content. This quantitative method provides data support for the optimization of teaching content and ensures that teaching resources can be concentrated on the areas that need the most attention.

[0080] Specifically, the calculation of teaching experience involves collecting and analyzing various interactive data of students in the Metaverse teaching environment, including but not limited to students' questions, answers, discussion participation, and frequency of interaction with virtual teaching elements. These data reflect students' understanding and interest in specific knowledge points and are the basis for evaluating teaching experience. At the same time, it is also necessary to determine the scope of each knowledge point in the Metaverse teaching environment, including specific parameters of knowledge depth, knowledge breadth, and interaction frequency.

[0081] Furthermore, a teaching experience threshold can be set for each knowledge point. This threshold is determined based on teaching objectives, student group characteristics, and historical teaching data. The setting of the teaching experience threshold is dynamic and can be adjusted according to the teaching progress and student feedback. When the teaching experience of a certain knowledge point exceeds this threshold, it will be marked as a teaching focus, indicating that the knowledge point requires more teaching resources and attention to improve students' learning effects.

[0082] In some embodiments, the step of optimizing the teaching content to form optimized teaching content includes:

[0083] According to the ratio of the first key information and the second key information of each knowledge point, the information point change amount of each knowledge point is obtained, and compared with the preset change amount threshold: when the information point change amount is less than the change amount threshold, the key information point of the knowledge point is directly taken as the optimized teaching content of the knowledge point;

[0084] When the change amount of an information point is greater than or equal to the change amount threshold, if the knowledge point is not a teaching focus, the teaching content of the knowledge point remains unchanged. If the knowledge point is a teaching focus, the optimized teaching content of the previous knowledge point is taken as the optimized teaching content of the knowledge point.

[0085] Specifically, the information point change is calculated based on the ratio of the first key information to the second key information of each knowledge point. This change reflects the change in the depth and breadth of the student's understanding of the knowledge point and is an important indicator for evaluating the student's learning progress. For example, if a student shows a deep understanding of a concept in a discussion or test, the system may believe that the difference between the first key information and the second key information of the concept is small, and thus believe that the student has mastered this knowledge point.

[0086] Furthermore, a change threshold can be preset to evaluate whether the change of information points is significant. This threshold can be set according to the teaching objectives, the students' historical performance and the difficulty of the knowledge points. When the change of information points is less than this threshold, it is considered that the students have a deep enough understanding of the current knowledge points, so the key information points of the knowledge points can be directly used as the optimized teaching content.

[0087] More specifically, the following steps can be taken:

[0088] 1. Setting the change threshold: Dynamically adjust the change threshold based on the syllabus and students’ learning history to ensure that it reflects students’ learning progress.

[0089] 2. Dynamic adjustment of teaching content: When the change in information points is less than the change threshold, the most relevant keywords or concepts are automatically selected as the core of the teaching content, and the parts that students have fully understood are eliminated.

[0090] 3. Special attention to teaching key points: For knowledge points identified as teaching key points, even if the change in information points is less than the change threshold, you can choose not to directly adopt the key information points, but to further explore them in depth or provide more practice opportunities to enhance students' understanding.

[0091] 4. Personalized recommendations for teaching content: Customized teaching content recommendations can be provided to each student based on their individual learning path and style, including additional learning resources, different ways of explanation, or related extended reading.

[0092] 5. Normalization of teaching content: In order to ensure the balance and consistency of teaching content, the ratio of the number of teaching moments in a class to the teaching depth can be used for normalization to ensure that all students can obtain the necessary teaching support within a limited time.

[0093] In some embodiments, the probability of improving the student learning experience is calculated according to the teaching focus and the optimized teaching content by the following formula:

[0094]

[0095] In the formula, pi represents the probability of improvement of students’ learning experience, ω represents the presentation gain of optimized teaching content, and C min represents the minimum presentation gain of teaching content, and θ represents the experience improvement threshold.

[0096] Specifically, the probability of improvement in student learning experience is a dimensionless proportional value that reflects the contribution of teaching content presentation to the improvement of student experience. The presentation gain of optimized teaching content is usually expressed as a score or percentage, reflecting the attractiveness or effectiveness of the teaching content after optimization. The minimum presentation gain of teaching content is a threshold used to ensure that the teaching content at least meets certain quality standards. The experience improvement threshold is a value set based on teaching objectives and student feedback, which is used to measure the minimum level of improvement that teaching content needs to achieve.

[0097] The value of ω can be determined by analyzing the attractiveness of the teaching content, student interaction data, and teaching effectiveness. min The values ​​of and θ can be set based on historical data, teaching standards, and student feedback. Substitute the above values ​​into the formula to calculate the value of pi. If the value of pi is high, it means that the optimization of teaching content has a significant positive impact on students' learning experience.

[0098] The presentation gain of the teaching content is calculated by the following formula:

[0099] G=G0×(1+α×π)(2)

[0100] In the formula, G represents the presentation gain of teaching content, G0 represents the baseline presentation gain, α represents the experience improvement coefficient, and π is pi.

[0101] Specifically, the parameters in the formula are as follows:

[0102] The presentation gain of teaching content refers to the enhanced effect of teaching content on learning experience after optimization.

[0103] The baseline presentation gain is a measure of the basic quality or effectiveness of the teaching content when it is not optimized. The experience improvement coefficient is an adjustment factor used to adjust the presentation gain based on the probability of improvement in the student's learning experience.

[0104] The value of G0 can be determined by evaluating the initial quality, depth and breadth of the teaching content. The value of α is calculated based on the optimization effect of the teaching content and the probability of improvement of the students' learning experience.

[0105] Substitute the values ​​of G0, α and π into the formula to calculate the value of G. The value of G reflects the degree to which the learning experience is enhanced after the teaching content is optimized.

[0106] The baseline presentation gain is calculated using the quality of the teaching content and the teaching objectives using the following formula:

[0107]

[0108] In the formula, Q represents the quality of teaching content, and T represents the teaching goal.

[0109] Specifically, the parameters in the formula are as follows:

[0110] The benchmark presents a gain, which indicates the quality and effectiveness of the teaching content before any optimization. The quality of the teaching content can be evaluated by the accuracy, completeness and logic of the teaching content. The teaching objectives reflect the educational goals or standards that the teaching content needs to achieve.

[0111] The Q value of teaching content can be evaluated through a detailed review of teaching materials and an analysis of the consistency between teaching content and the syllabus. The teaching goal T can be set based on educational standards and student learning needs. Substitute the values ​​of Q and T into the formula to calculate the value of G0. The G0 value is the baseline presentation gain of the teaching content before any optimization, which provides a starting point for subsequent optimization of teaching content.

[0112] The above-mentioned embodiments of the present invention have the following beneficial effects: the present invention utilizes speech recognition and natural language processing technology to efficiently extract keywords from speech teaching content, which can provide a basis for building a virtual teaching environment. By intelligently analyzing the teaching content and identifying key information points, the system can more accurately grasp the core content of teaching, and dynamically adjust the teaching focus according to the students' interactive data. Such interactive analysis enhances the pertinence and real-time nature of the teaching content. In addition, the system can further improve the quality of the teaching experience and the students' participation by calculating the presentation gain and the optimized teaching content, and achieve the optimal match between the teaching content and the students' acceptance. Secondly, the present invention can achieve quantitative management of the presentation gain of the teaching content through a series of calculation formulas, and ensure the coordinated optimization of the teaching content quality and the teaching objectives. By normalizing the teaching content, the balance and consistency of teaching can be improved in terms of knowledge depth and the allocation of teaching time. This refined management can not only improve the presentation effect of the teaching content, but also provide teachers with powerful tools to enable them to flexibly adjust the teaching strategy according to the students' feedback and learning progress. By comparing the intelligent teaching system with the preset effect, the AI ​​teacher can achieve adaptive adjustment of the teaching behavior, and further improve the personalization and effectiveness of teaching.

[0113] It is understandable that the modules and references recorded in the AI ​​classroom scene construction experience system 200 based on the metaverse Figure 1Therefore, the operations, features and beneficial effects described above for the AI ​​classroom scene construction experience method based on the Metaverse are also applicable to the AI ​​classroom scene construction experience system 200 based on the Metaverse and the modules contained therein, and will not be repeated here.

[0114] Reference below Figure 3 , which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0115] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0116] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0117] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0118] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A method for building AI classroom scenes based on the Metaverse. It is characterized in that The following steps are involved: Receiving voice teaching content information, extracting keywords from the voice teaching content information, constructing a virtual teaching environment in the metaverse according to the keywords, and calculating the presentation method of the teaching content in the metaverse environment; Analyze the teaching content in the metaverse environment to obtain key information points of the teaching content; identify the teaching focus based on the key information points and student interaction data, and optimize the teaching content to form optimized teaching content; According to the teaching focus and optimized teaching content, the probability of improvement of students' learning experience is calculated, and the presentation gain of the teaching content is calculated. The presentation gain is integrated with the teaching content and then presented in a scenario to obtain teaching content with enhanced experience. The teaching content and teaching objectives of the enhanced experience are input into the intelligent teaching system. The output of the intelligent teaching system is compared with the preset teaching effect, the behavior and teaching strategy of the AI ​​teacher in the metaverse are controlled, and the teaching content is output.

2. According to the AI ​​classroom scene construction experience method based on the metaverse according to claim 1, It is characterized in that The step of receiving voice teaching content information and extracting keywords comprises: Use speech recognition technology to convert received speech information into text data, and apply natural language processing technology to extract key words in the text; Taking the extracted key words as the center, the teaching is expanded to related knowledge points according to the preset teaching depth, and the key information points in the expanded knowledge points are used as the first key information and the second key information; the optimal value of the first key information and the second key information is taken as the key information point of the current knowledge point.

3. According to the AI ​​classroom scene construction experience method based on the metaverse according to claim 1, It is characterized in that The teaching content in the metaverse environment includes a knowledge depth range, a knowledge breadth range and an interaction frequency range. Each range corresponds to a teaching experience threshold, and the teaching experience threshold is dynamically adjusted according to the voice content received by the metaverse teaching environment.

4. According to the AI ​​classroom scene construction experience method based on the metaverse according to claim 3, It is characterized in that The step of identifying teaching focus based on key information points and student interaction data includes: The ratio of the key information point of each knowledge point to the student interaction data is taken as the teaching experience quantity. The corresponding teaching experience threshold is obtained according to the range of the metaverse teaching environment where each knowledge point is located, which is used as the comparison threshold. The knowledge points with teaching experience greater than the comparison threshold are taken as teaching focuses.

5. According to the AI ​​classroom scene construction experience method based on the metaverse according to claim 2, It is characterized in that The step of optimizing the teaching content to form optimized teaching content includes: According to the ratio of the first key information and the second key information of each knowledge point, the information point change amount of each knowledge point is obtained, and compared with the preset change amount threshold: when the information point change amount is less than the change amount threshold, the key information point of the knowledge point is directly taken as the optimized teaching content of the knowledge point; When the change amount of an information point is greater than or equal to the change amount threshold, if the knowledge point is not a teaching focus, the teaching content of the knowledge point remains unchanged. If the knowledge point is a teaching focus, the optimized teaching content of the previous knowledge point is taken as the optimized teaching content of the knowledge point.

6. According to the AI ​​classroom scene construction experience method based on the metaverse according to claim 5, It is characterized in that The optimized teaching content is normalized using the ratio of the number of teaching moments in a class to the teaching depth to ensure the balance and consistency of the teaching content.

7. According to the method for building an AI classroom scene experience based on the metaverse according to claim 1, It is characterized in that According to the teaching focus and optimized teaching content, the probability of improving the student learning experience is calculated by the following formula: In the formula, pi represents the probability of improvement of students’ learning experience, ω represents the presentation gain of optimized teaching content, and C min represents the minimum presentation gain of teaching content, and θ represents the experience improvement threshold.

8. According to the method for building an AI classroom scene experience based on the metaverse according to claim 7, It is characterized in that The presentation gain of the teaching content is calculated by the following formula: G=G 0 ×(1+α×π) (2) In the formula, G represents the presentation gain of teaching content, G 0 represents the benchmark presentation gain, α represents the experience improvement coefficient, and π represents the circumference of a circle.

9. According to the method for building an AI classroom scene experience based on the metaverse according to claim 8, It is characterized in that The baseline presentation gain is calculated using the quality of the teaching content and the teaching objectives using the following formula: In the formula, Q represents the quality of teaching content, and T represents the teaching goal.

10. An AI classroom scene construction experience system based on Metaverse, the system implements the method as claimed in claim 1, It is characterized in that include: The teaching content processing module is used to receive voice teaching content information, extract keywords, construct a virtual teaching environment in the metaverse based on the keywords, and calculate the presentation method of the teaching content in the metaverse environment; The teaching analysis module is used to analyze the teaching content in the metaverse environment and obtain the key information points of the teaching content; identify the teaching focus based on the key information points and student interaction data, and optimize the teaching content to form optimized teaching content; The teaching experience module is used to calculate the probability of improvement of students' learning experience based on the teaching focus and optimized teaching content, and calculate the presentation gain of the teaching content. After integrating the presentation gain with the teaching content, the module presents the content in a scenario-based manner to obtain teaching content with enhanced experience. The intelligent teaching system module is used to input the teaching content and teaching objectives of the enhanced experience, compare the output of the intelligent teaching system with the preset teaching effect, control the behavior and teaching strategy of the AI ​​teacher in the metaverse, and output the teaching content.

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