Interactive teaching method and device based on artificial intelligence and electronic equipment

Abstract

The invention provides an interactive teaching method and device based on artificial intelligence and electronic equipment, and the method comprises the steps: inputting the data of a strategy game into a pre-training artificial intelligence model corresponding to a current student, outputting a first competitive strategy, generating a second competitive strategy through an input instruction of the current student for the strategy game, and inputting the second competitive strategy into a pre-training artificial intelligence model corresponding to the current student; based on the first competitive strategy and the second competitive strategy, the roles of the two parties are controlled to perform strategy game fight, and a first score of the current student is obtained; inputting data of the strategy game into pre-trained artificial intelligence models corresponding to other students, outputting a third competitive strategy, and then respectively controlling the roles of the two parties to perform strategy game fight based on the first competitive strategy and the third competitive strategy to obtain a second score of the current student; and adjusting a course outline and a learning plan according to the first score, the second score and the data of the strategy game, and guiding the current student to enter course learning so as to finish a teaching task in a targeted manner.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an interactive teaching method, device and electronic equipment based on artificial intelligence. Background Art

[0002] In the past decade, artificial intelligence technology has made remarkable progress. The people, especially young people, have a strong demand for learning artificial intelligence knowledge. Based on the computer properties of artificial intelligence technology itself, the rapid development of artificial intelligence has spawned numerous artificial intelligence education platforms. These platforms usually use artificial intelligence as an additional content of programming teaching, combined with programming courses, to guide students to learn the concept of artificial intelligence through programming. For example, low-code programming platforms such as Scratch enable students to learn artificial intelligence through programming through drag-and-drop programming. However, this education model often limits artificial intelligence education to the stage of programming language teaching, requiring learners to first master programming languages ​​(mainly Python) before they can access artificial intelligence content. This not only has a high threshold, but also makes it difficult to attract young people, especially primary school students, because it is difficult for them to persist in long-term programming learning. In addition, this education model overemphasizes algorithm learning and ignores the importance of data in artificial intelligence, resulting in young people failing to fully understand that artificial intelligence is a data-based science.

[0003] How to combine artificial intelligence with education to enable different courses for students with different levels of mastery of artificial intelligence is a problem that current education platforms need to solve. Summary of the invention

[0004] The present invention provides an interactive teaching method, device and electronic device based on artificial intelligence, which are used to solve the defect that the teaching platform in the prior art cannot provide different courses for learning according to the artificial intelligence mastery levels of different students.

[0005] The present invention provides an interactive teaching method based on artificial intelligence, which is used in a teaching system. The method comprises: By inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, a first competitive strategy is output, a second competitive strategy is generated through the input instructions of the current student for the strategy game, and then based on the first competitive strategy and the second competitive strategy, the characters of both parties are controlled to play the strategy game, and a first score of the current student is obtained; By inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, outputting a third competitive strategy, and then controlling the characters of both parties to play the strategy game based on the first competitive strategy and the third competitive strategy, a second score of the current student is obtained; The course outline and study plan are adjusted according to the first score, the second score and the data of the strategy game, and the current students are guided to start the course study.

[0006] According to an interactive teaching method based on artificial intelligence provided by the present invention, the data of the strategy game includes the number of props and the position of the props; By inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, the first competitive strategy is output, including: According to the number and location of props in the strategy game, multiple competitive strategies for obtaining props are generated; wherein each competitive strategy includes the competition order and path of multiple props; The multiple competitive strategies are input into the artificial intelligence model corresponding to the current student for calculation to obtain the winning probability corresponding to each competitive strategy, and the competitive strategy with the highest winning probability is output as the first competitive strategy.

[0007] An artificial intelligence-based interactive teaching method provided by the present invention generates a second competitive strategy through the input instructions of the current student, including: determining the competition order and path of the props displayed on the screen through the input instructions of the current student; and generating the second competitive strategy according to the competition order and path of the props.

[0008] According to an interactive teaching method based on artificial intelligence provided by the present invention, the data of the strategy game includes the number of props and the position of the props; By inputting the data of the strategy game into the corresponding pre-trained artificial intelligence models of other students, the third competitive strategy is output, including: According to the number and location of props in the strategy game, multiple competitive strategies for obtaining props are generated; wherein each competitive strategy includes the competition order and path of multiple props; The multiple competitive strategies are input into the artificial intelligence models corresponding to other students for calculation, so as to obtain the winning probability corresponding to each competitive strategy, and the competitive strategy with the highest winning probability is output as the third competitive strategy.

[0009] According to an interactive teaching method based on artificial intelligence provided by the present invention, the artificial intelligence model corresponding to the current student is trained in the following manner: Training data screening: Real-time capture and recording of the strategies and results adopted by students after the game, and storing these data in a secure data storage module, and automatically selecting the most recent N wins as the winning strategy data; Training data enhancement: Using data enhancement algorithms, more sample data is automatically generated from winning strategy data, including winning sample data and losing sample data; Feature engineering: Calculate the difference between the coordinate values ​​of game props to generate new data points to expand the feature dimension of the data set; Algorithm selection: According to the conditions of the students, select the artificial intelligence algorithm that matches the students; Parameter adjustment: adjust the algorithm parameters according to the type of selected algorithm; Model building and optimization: Use the screened training data, selected algorithms and parameters to train the artificial intelligence model and save it to the operation and maintenance module.

[0010] According to an interactive teaching method based on artificial intelligence provided by the present invention, based on the first competitive strategy and the second competitive strategy, two characters are respectively controlled to play a strategy game against each other to obtain a first score of the current student, including: Based on the first competition strategy and the second competition strategy, the characters of both parties are controlled to obtain props according to the competition order and path of their respective props; If one of the characters obtains any item first, the item will be removed from the competition order and path of both characters, and their respective competition order and path will be updated; Continue to control the two characters to continue to obtain props according to the updated order and path of the competition for props, until all props are obtained, and count the number of props obtained by each character; Determine the current student's first score based on the difference in the number of props.

[0011] According to an interactive teaching method based on artificial intelligence provided by the present invention, based on the first competitive strategy and the third competitive strategy, the characters of both parties are controlled to play a strategic game against each other, and the second score of the current student is obtained, which specifically includes: Based on the first competition strategy and the third competition strategy, the characters of both parties are controlled to obtain props according to the competition order and path of their respective props; If one of the characters obtains any item first, the item will be removed from the competition order and path of both characters, and their respective competition order and path will be updated; Continue to control the two characters to continue to obtain props according to the updated order and path of the competition for props, until all props are obtained, and count the number of props obtained by each character; The second score of the current student is determined by calculating based on the initial score of the current student's character, the initial scores of other students' characters, the number of props obtained by the current student, and the number of props obtained by other students.

[0012] According to an artificial intelligence-based interactive teaching method provided by the present invention, the course outline and learning plan are adjusted according to the first score, the second score and the data of the strategy game, and the current students are guided to enter the course learning, including: If the score obtained by the current student in the first competitive strategy exceeds the first set score threshold, a competitive mode is unlocked in which the first competitive strategy and the second competitive strategy are used to control the two characters to play a strategy game, and basic artificial intelligence popular science teaching content is added to the current student's course outline; In the case where the first score of the current student exceeds the second set score threshold, adding an explanation of the basic principles of artificial intelligence to the course outline of the current student; In the case where the second score of the current student exceeds the third set score threshold, continue to add relevant knowledge of establishing an artificial intelligence model to the course outline of the current student.

[0013] The present invention also provides an interactive teaching device based on artificial intelligence, comprising the following modules: The human-machine battle module is used to input the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, output a first competitive strategy, generate a second competitive strategy through the input instructions of the current student for the strategy game, and then control the characters of both parties to play the strategy game based on the first competitive strategy and the second competitive strategy, and obtain a first score of the current student; The machine battle module is used to input the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, output a third competitive strategy, and then control the characters of both sides to play the strategy game based on the first competitive strategy and the third competitive strategy, so as to obtain the second score of the current student; The course selection module is used to adjust the course outline and study plan according to the first score, the second score and the data of the strategy game, and guide the current students to enter the course study.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an interactive teaching method based on artificial intelligence as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements any of the above-mentioned interactive teaching methods based on artificial intelligence.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned interactive teaching methods based on artificial intelligence.

[0017] The interactive teaching method and device based on artificial intelligence provided by the present invention respectively obtain a first score by competing the second competitive strategy input by the current student with the first competitive strategy output by the pre-trained artificial intelligence model, and obtain a second score by competing the first competitive strategy output by the artificial intelligence model corresponding to the current student with the third competitive strategy output by the artificial intelligence model corresponding to other colleges. The total score of the current student can be determined to characterize the degree of mastery of artificial intelligence by the current student, and the course outline and learning plan are adjusted according to the first score, the second score and the data of the strategy game, and the current student is guided to enter the course learning, thereby realizing the learning of corresponding courses for students with different degrees of mastery of artificial intelligence based on the way of playing games between students and artificial intelligence models, so as to complete the teaching tasks in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a structural schematic diagram of the interactive teaching system provided by the present invention.

[0020] Figure 2 This is one of the flow charts of the interactive teaching method based on artificial intelligence provided by the present invention.

[0021] Figure 3 The present invention provides a schematic diagram of an interface of the strategy game.

[0022] Figure 4 This is the second flow chart of the interactive teaching method based on artificial intelligence provided by the present invention.

[0023] Figure 5 It is a schematic diagram of the battle process of the strategy game provided by the present invention.

[0024] Figure 6 This is the third flow chart of the interactive teaching method based on artificial intelligence provided by the present invention.

[0025] Figure 7 It is a structural schematic diagram of an interactive teaching device based on artificial intelligence provided by the present invention.

[0026] Figure 8 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0028] Combine the following Figure 1-Figure 7 The interactive teaching method and device based on artificial intelligence of the present invention are described. The teaching method of this embodiment is used in an interactive teaching system.

[0029] First, the various functional modules of the interactive teaching system are schematically described. Figure 1 , the interactive teaching system comprises: 1. Front-end interaction module.

[0030] Login module: Students log into the system through the login module.

[0031] Winning game screening module: Students can browse previous game results and decide which game data can be used to train their own artificial intelligence models.

[0032] Algorithm selection module: Students can build their own artificial intelligence models and adjust parameters and algorithms. Students can select the algorithm they want to use from the preset algorithm library and adjust the corresponding parameters of the algorithm.

[0033] Mode selection module: Students select the game mode in this module. It can be divided into three modes according to the design purpose: 1) Beginner Mode: In this mode, students compete against an AI model that uses random strategies to learn basic operations and collect initial battle data.

[0034] 2) Advanced mode: In this mode, the system will call the fully automatic model building function in the AI ​​model building module to use the game data generated by the students in the entry-level mode to train an AI model based on the students' own competitive strategy. The students will play against the game characters driven by the model. After the students win a certain number of games (for example, 10 games), the system will retrain the model using the same method and let the students play against the characters driven by the newly trained AI model in a new game.

[0035] 3) AI Battle Mode: Students will use the models they created in the AI ​​model building module to control their own characters, and through the matching module in the battle module, they will battle against characters controlled by AI models created by other students. After the battle, the scores of the models of both sides will be adjusted through the scoring module, and finally the ranking on the leaderboard will be updated through the ranking module.

[0036] 2. Data storage module: including database and artificial intelligence model operation and maintenance module.

[0037] Database: A database based on Structured Query Language (SQL) is used to store all user login information, game strategies and win / loss information, event information organized by the event organization module, and all generated digital information.

[0038] Artificial intelligence model operation and maintenance module: This module is used to store and manage various artificial intelligence models established by users and systems through the artificial intelligence model establishment module.

[0039] 3. Artificial intelligence model building module: 1) Data screening: After completing the game, the system will capture and record the strategies adopted by the students and their game results in real time. These data will be stored in a secure module designed for data storage to ensure data integrity and accessibility.

[0040] The system has two methods for filtering strategy data: The system will automatically select the last 10 winning games as strategy data. With the participation of students, the system will guide students to manually screen key strategy data in historical winning games through an intuitive user interface. The system will use a data enhancement algorithm to automatically generate more winning samples and corresponding losing samples from existing winning game data to expand the scale and diversity of the data set. This step is crucial to improving the generalization ability and robustness of the model. The specific implementation steps of the algorithm are as follows: a) The system will import user strategy information, including the number of props, the user's specific strategy for obtaining props, and the results of obtaining props. The system will filter the props that the user successfully obtained and import their coordinate data.

[0041] b) For the imported coordinate data, the system will P x ) and the y coordinate (denoted as P y ) performs the following processing to generate new coordinate data: P x ' and P y ': P x ' = P x+ max(0,min(255, P x +random_sign)

[0042] P y ' = P y + max(0,min(255, P y +random_sign)

[0043] where random_sign is a randomly chosen sign that can be either 1 (indicating an increase of 1) or -1 (indicating a decrease of 1).

[0044] c) The system will use the newly generated coordinate data P x ′ and 𝑃 y ′Replace the original x coordinate (P x value) and y coordinate (P y value) data, and add these data as a new win data set to the previously filtered data set.

[0045] Additionally, the system will perform the following steps to apply the feature engineering algorithm: a) For each user data in the data set, the system will calculate the x-coordinate value of the prop (𝑃 𝑥 ) and the y-coordinate value (𝑃 𝑦 ) is the difference between .

[0046] b) Taking a data set containing 7 sets of competitive props as an example, the system will calculate the x-coordinate value of each prop (from 𝑃 𝑥1 to 𝑃 𝑥7 ) are subtracted pairwise to generate new data points. Specifically, for any two coordinate values ​​𝑃 𝑥𝑖 and 𝑃 𝑥𝑗 (where 𝑖≠𝑗), calculate the difference, the formula is: 𝐷 𝑥𝑖𝑗 =𝑃 𝑥𝑖 −𝑃 𝑥𝑗 .

[0047] c) The difference 𝐷 obtained from these calculations 𝑥𝑖𝑗 It will be added to the original data set as a new data point.

[0048] Through this process, the system is able to expand the feature dimension of the data set and provide additional information for subsequent data analysis and model training.

[0049] 2) Algorithm selection: The system will select and adjust the artificial intelligence algorithm through a series of meticulous steps. The specific implementation sequence is as follows: a) The system will take into account the student's personalized selection. If the student has specified a preferred model, the system will adopt this selection.

[0050] b) The system will evaluate the number of props in the dataset, and if the number exceeds a certain value (for example, 10), the system will select the neural network model because neural networks are suitable for processing datasets with a large number of features.

[0051] c) The system will analyze the proportion of wins in the enhanced dataset. If the win ratio is below a certain threshold, such as 10%, the system will select the XgBoost model, which is suitable for processing unbalanced datasets. If the win ratio is above another threshold, such as 80%, the random forest model will be selected to take advantage of its advantages in processing diverse datasets.

[0052] d) If none of the above conditions are met, the system will select the logistic regression model by default.

[0053] 3) Parameter selection: After selecting the model, the system will perform intelligent parameter adjustment. The system will select specific parameters for adjustment based on the type of algorithm selected by the previous students or the user. The specific contents are as follows: a) Adjust the number of layers, number of neurons, learning rate and regularization parameters of the neural network; b) Set the tree depth, learning rate, number of trees and regularization parameters of XgBoost; c) Optimize the number of trees in the random forest, the minimum number of samples for splitting, and the feature selection method; d) Adjust the regularization strength and number of iterations for logistic regression.

[0054] 4) Model building: The system will automatically use the screened data, selected algorithms and parameters to train the AI ​​model, and save it to the AI ​​model operation and maintenance module. During the model training process, the system implements cross-validation to evaluate the model performance under different parameter settings to ensure that the selected model and parameter configuration can provide high prediction accuracy and generalization capabilities. At the same time, the system will continue to monitor the key performance indicators of the model, such as accuracy, recall rate and F1 score, to ensure the effectiveness of training. Based on the results of cross-validation and performance monitoring data, the system will adjust the parameters of the model, including learning rate, regularization parameter, tree depth, etc., to optimize the training effect.

[0055] After completing these steps, the system will save the final AI model to the operation and maintenance module, providing the most suitable algorithm and fine-tuning for each student and data set to ensure the best training effect.

[0056] 4. Battle module: This module consists of scoring module, matching module and ranking module.

[0057] 1) Scoring module: When a game is over, the latest score of the user character is calculated through a specific calculation method and stored in the database.

[0058] 2) Matching module: When a student enters the AI ​​battle mode, the system will automatically start the matching function. The selection process is as follows: From the leaderboard, the system will select 20 students with similar rankings to the student, including the 10 students ranked before the student and the 10 students ranked after the student, and then randomly select a student from these 20 students, and then the system will call the student's latest trained artificial intelligence model for battle.

[0059] 3) Ranking module: Rank all students or students in a specific activity by calling the data in the database module. The ranking can be viewed on the front-end interactive interface.

[0060] 5. Activity organization module: including information module and management module.

[0061] The information module will display basic information about the event, including but not limited to: event organizer, instructor, event time, event rewards, QR code for participating in the event, etc.

[0062] Management Module: Administrators and event managers can use this module to create events and manage the number and start / end times of events.

[0063] Figure 2 This is one of the flow charts of the interactive teaching method based on artificial intelligence provided by the present invention, which is used in the above-mentioned teaching system, including: Step 201: Input the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, output the first competitive strategy, generate the second competitive strategy through the current student's input instructions for the strategy game, and then control the characters of both sides to play the strategy game based on the first competitive strategy and the second competitive strategy to obtain the first score of the current student.

[0064] In this embodiment, the strategy game refers to a game in which two players formulate a competitive strategy and then control characters to fight based on the formulated competitive strategy. The two players can be students and students, students and machines (i.e., artificial intelligence modules), and machines and machines.

[0065] See also Figure 3 , Figure 3 Shown is a schematic diagram of an interface of the strategy game described in this embodiment.

[0066] The operation interface provides students with an interactive page for operation. Figure 3As can be seen in the figure, the page is mainly composed of a chessboard, character 1 (controlled by the student or the AI ​​model generated by the student) appears at one end of the square game interface (such as the upper left corner or the lower left corner), while character 2 (AI model generated automatically or by other students) appears at the other end of the square game interface on the diagonal. Students choose strategies by finger touch (mobile platform) or mouse selection (such as Mac, Linux and PC platforms) to obtain more props.

[0067] Among them, the artificial intelligence model corresponding to the current students is trained in the following ways: The system will follow the following steps to ensure the establishment and optimization of the AI ​​model: Training data screening: The system will capture and record the strategies and results adopted by students after the game in real time, and store these data in a secure data storage module. The system will automatically select the most recent N wins as the winning strategy data, or manually screen key strategy data through the user interface with the participation of students. N can be 10, 15, 20, etc.

[0068] Data enhancement: The system will use a data enhancement algorithm to automatically generate more sample data from the winning game data, including winning sample data and losing sample data, to expand the size and diversity of the data set. The specific steps include: importing user strategy information, screening the successfully acquired prop coordinate data, and processing it to generate new coordinate data, which will be part of the winning game data set.

[0069] Feature Engineering: The system will calculate the difference between the props’ coordinate values ​​to generate new data points, thereby expanding the feature dimension of the dataset. For example, for a dataset of 7 sets of competitive props, the system will calculate the difference between the x-coordinate values ​​of every two props and add these differences as new data points to the dataset.

[0070] Algorithm selection: The system will select an AI algorithm that matches the student based on the student's personalized selection, number of props, win ratio, and other conditions. If the student has a preference, the system will give priority to it; if the number of props exceeds 10, the neural network model will be selected; if the win ratio is less than 10%, the XgBoost model will be selected, and if it is higher than 80%, the random forest model will be selected; in other cases, the logistic regression model will be selected by default.

[0071] Parameter adjustment: The system will adjust the algorithm parameters according to the type of algorithm selected, including the number of layers, number of neurons, learning rate and regularization parameters of neural networks; tree depth, learning rate, number of trees and regularization parameters of XgBoost; number of trees, minimum number of split samples and feature selection method of random forest; regularization strength and number of iterations of logistic regression.

[0072] Model building and optimization: The system will automatically use the selected data, selected algorithms and parameters to train the AI ​​model and save it to the operation and maintenance module. During the training process, the system implements cross-validation to evaluate the model performance under different parameter settings, continuously monitors performance indicators such as accuracy, recall rate and F1 score, and intelligently adjusts model parameters to optimize the training effect. Finally, the system will save the optimized AI model to the operation and maintenance module to ensure that the most appropriate algorithm and fine-tuning are provided for each student and data set to achieve the best training effect.

[0073] Through the above steps: First, after students play the game using the first or second competitive strategy, or a competitive strategy selected based on personal preference, the results will be automatically recorded by the system and stored in a dedicated data storage module. Then, under the guidance of the system, students can manually filter out the winning strategy data from the historical data, which will be used as the basis for training data.

[0074] The system will automatically use the win data to generate additional win data and its corresponding loss data through the data enhancement algorithm. At the same time, the system will use the feature engineering algorithm to expand the features in the data set to enhance the richness and predictive power of the data set. These carefully processed data will be integrated to form a comprehensive training data set.

[0075] Based on this training data set, the system will use the preset algorithm framework, combined with the intrinsic characteristics of the data set - such as the number of win data, the diversity of props and the personalized choices of the students, to recommend or select the most suitable artificial intelligence algorithm for users. These algorithms include but are not limited to logistic regression, XGBoost, random forest and neural network, etc., aiming to assist users in efficiently training excellent artificial intelligence models.

[0076] Step 201 is a human-machine battle mode, in which the student fights against the artificial intelligence model trained by himself, and then the first score is calculated. After the student wins a certain number of games (for example, 10 games), the system will retrain the model using the same method and let the student fight against the character driven by the newly trained artificial intelligence model in the new game.

[0077] Step 202: Input the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, output a third competitive strategy, and then control the characters of both sides to play the strategy game based on the first competitive strategy and the third competitive strategy to obtain the second score of the current student.

[0078] Step 202 is a machine-to-machine battle method, in which students replace each other according to the pre-trained artificial intelligence model to control the characters of both sides to play a strategy game battle to obtain the second score of the student.

[0079] From the rankings, the system will select 20 users with similar rankings to the user, including the 10 users ranked before the user and the 10 users ranked after the user. Then, a user will be randomly selected from these 20 users, and the system will call up the user's latest trained AI model for the battle.

[0080] Step 203: adjust the course outline and study plan according to the first score, the second score and the data of the strategy game, and guide the current student to enter the course study.

[0081] Here is how the method works: 1) The system first prepares a blank course outline for each student. When the score obtained by the student in the first competitive strategy exceeds the first set score threshold (for example, 10 points), the competitive mode based on the first competitive strategy and the second competitive strategy is unlocked to control the two characters to play a strategic game battle, and basic artificial intelligence popular science teaching content is added to the current student's course outline. These contents mainly involve the application of artificial intelligence and programming in daily life and future development trends. Once unlocked, the scores that the student continues to obtain in the first competitive strategy will no longer increase the teaching content.

[0082] 2) When the current student's first score exceeds the second set score threshold (for example, 100 points), the system will automatically add an explanation of the basic principles of artificial intelligence to the student's course outline.

[0083] 3) When the current student's second score exceeds the third set score threshold (e.g., 200 points), the system will continue to add course content to the student's syllabus, including Python programming basics and relevant knowledge on artificial intelligence model building.

[0084] 4) In addition, if the current student's second score exceeds the third set score threshold (e.g. 200 points), the system will automatically detect the student's performance in the game. After meeting certain conditions, the system will continue to add more teaching content to the student's course outline, including: a) If the student's win rate exceeds 80%, or if they use more than three AI models to obtain the second game score, the system will add more in-depth AI and Python programming knowledge.

[0085] b) If the student participates in the game using all preset AI models and obtains a second game score, the system will unlock all course content.

[0086] c) When students reach a very high score (e.g. 2000 points), the course content on building artificial intelligence products and Python algorithms from scratch will be unlocked for them.

[0087] At the same time, the system allows students to select and enter courses at any time. The system will generate a new syllabus based on the automatically generated syllabus and the teaching content that students have completed, merging the course content with high similarity.

[0088] The system will automatically match educational resources (such as videos, PPTs, and text materials) in the resource library according to the content of the syllabus and attach a link to the syllabus. The specific matching method is as follows: a) Use the pre-trained BERT model to vectorize the content of the items in the syllabus and generate a digital matrix M A .

[0089] b) Similarly, vectorize the introduction content of all courses in the resource library to generate a digital matrix series M Bi , where i is the course sequence number.

[0090] c) Calculate the matrix M A and M B The cosine similarity between the vectors in is calculated, and the course corresponding to the maximum value is selected as the content that best matches the corresponding course outline.

[0091] d) Convert items in the syllabus into hyperlinks directly to the course content.

[0092] In addition, study dates are automatically attached to the course outline, based on the expected completion date.

[0093] The adjusted syllabus will help students start learning AI-related knowledge immediately, improve learning efficiency, ensure the personalization and pertinence of teaching content, and meet the learning needs and progress of different students.

[0094] The interactive teaching method based on artificial intelligence provided by the present invention obtains a first score by competing against the second competitive strategy input by the current student with the first competitive strategy output by the pre-trained artificial intelligence model, and obtains a second score by competing against the first competitive strategy output by the artificial intelligence model corresponding to the current student with the third competitive strategy output by the artificial intelligence model corresponding to other colleges. The total score of the current student can be determined to represent the degree of mastery of the current student for artificial intelligence, and the course outline and learning plan are adjusted according to the first score, the second score and the data of the strategy game, and the current student is guided to enter the course learning, so as to complete the teaching task in a targeted manner.

[0095] The invention mainly improves and optimizes the concepts and architecture of traditional AI-based games, specifically: 1) We established and designed a code-free AI model building system to help students use their own data to build their own AI models and manage these AI models.

[0096] 2) Under this code-free system, the present invention example creates a platform that allows students to compete with an artificial intelligence model that continuously learns from the data it generates.

[0097] 3) This example provides a platform for students to build their own AI models and automatically compete and score with other students’ AI models.

[0098] Furthermore, the data of the strategy game includes the number of props and the position of props; in step 201, the data of the strategy game is input into the pre-trained artificial intelligence model corresponding to the current student to output the first competitive strategy, including: generating multiple competitive strategies for obtaining props according to the number of props and the position of props in the strategy game; wherein each competitive strategy includes the competition order and path of multiple props; the multiple competitive strategies are input into the artificial intelligence model corresponding to the current student for calculation to obtain the winning probability corresponding to each competitive strategy, and the competitive strategy with the highest winning probability is output as the first competitive strategy.

[0099] Furthermore, in step 201, a second competitive strategy is generated through the input instructions of the current student, including: determining the competition order and path of the props displayed on the screen through the input instructions of the current student; and generating the second competitive strategy according to the competition order and path of the props.

[0100] Furthermore, step 202 specifically includes: generating multiple competitive strategies for obtaining props based on the number and position of props in the strategy game; wherein each competitive strategy includes a competition order and path for multiple props; inputting the multiple competitive strategies into the artificial intelligence models corresponding to other students for calculation, obtaining the winning probability corresponding to each competitive strategy, and outputting the competitive strategy with the highest winning probability as the third competitive strategy.

[0101] Among them, in this embodiment, the system will call the model pre-saved by the student in the artificial intelligence model storage module, and use the competitive strategy output by it as the third competitive strategy.

[0102] Further, see Figure 4 In step 201, based on the first competitive strategy and the second competitive strategy, the characters of both parties are controlled to play a strategic game battle, and the first score of the current student is obtained, including the following steps 401-404: 401. Based on the first competition strategy and the second competition strategy, the characters of both parties are controlled to obtain props according to their respective competition order and path for props.

[0103] 42. If one of the characters obtains any item first, the item will be removed from the competition order and path of both characters, and their respective competition order and path will be updated.

[0104] 403. Continue to control the characters of both parties to continue acquiring props according to the competition order and path of the updated props, until all props are acquired, and count the number of props acquired by the characters of both parties.

[0105] 404. Determine the first score of the current student based on the difference in the number of props.

[0106] Specifically, the calculation of the first score refers to the following formula: S r = N a -N b

[0107] Where: S r is the user's score change, N a Get the number of props for the user role, N b The number of items obtained by the opponent character.

[0108] For example: the current student obtains 5 props, the opponent obtains 2 props, the user score is +3; the current student obtains 4 props, the opponent obtains 5 props, the user score is -1.

[0109] See also Figure 5 , Figure 5 The figure shows the battle process of a strategy game.

[0110] Interface generation: Character 1 is controlled by the student and appears at one end of the square game interface (for example, the upper left corner or the lower left corner), while Character 2 is controlled by the AI ​​model generated by the student and appears at the other end of the square game interface on the diagonal. An odd number of competitive props (for example, 7) appear on the interface, such as Figure 5 As shown in a.

[0111] Before starting a game, a fairness check is performed: the system will try different initial food distributions until a distribution scheme is found so that the sum of the coordinate values ​​(x and y values) of the initial props on the board and the theoretical average prop coordinate value (calculated as (board side length - 1) × number of props) are within an acceptable tolerance. Once such a distribution is found, the function returns this distribution scheme and ends. This tolerance calculation example: Lower limit: (board side length - 1) × number of props × 75%; Upper limit: (board side length - 1) × number of props × 125%; User strategy selection: Users use the mouse or the touch screen of their mobile device to select the competitive props they want to obtain in order. The order of acquisition and the preset route will be displayed on the screen, such as Figure 5 As shown in b.

[0112] Opponent strategy selection: The system loads the corresponding model from the AI ​​model operation and maintenance module (AI model built by the user's own data). The system generates all strategies for obtaining props based on the number of props (N) and the position (a total of N! groups of strategies). Then, the winning probability corresponding to all N! groups of strategies is calculated through the loaded model, and the group with the highest winning rate is selected as the output strategy.

[0113] Method of synchronizing and updating information: After the student confirms the strategy and selects "Start Game", the characters of both parties will automatically act immediately and move towards the next item that can be obtained according to the established strategy. The characters of both parties compete for the acquisition of items according to the preset strategy. All information will be collected and calculated in the cloud and displayed on the user end.

[0114] Information update: If any character obtains a new item, the item will be removed from the route of both characters, and the routes of both characters will be updated in the cloud computing center. The party that first touches the competing item is considered to have obtained it successfully.

[0115] When both parties reach the item at the same time: If both parties come into contact with the competing item at the same time, each party has a 50% probability of being judged as successfully obtaining the item.

[0116] Example: See Figure 5 c and Figure 5 d. For example, if the strategy set by the student is 1-2-3-4-5, and the strategy of the opponent character is generated as 2-3-4-5-1, and the student character first obtains item 1. Then the routes of both parties will be updated at the same time, the student character's route will be corrected to 2-3-4-5, and the opponent character's route will be corrected to 2-3-4-5. After that, the student character will turn to move towards item 2, while the opponent character's route will not change. At this time, the opponent character will give priority to obtaining item 2. At this time, the strategies of the student and opponent characters will be corrected to 3-4-5 at the same time, and their routes will be adjusted, moving towards item 3 at the same time. Both parties will continue this process until all the props are obtained.

[0117] After all props are obtained, the game will settle the number of props obtained by both parties and determine the winner. For example, if the user character obtains 3 props and the opponent character obtains 4 props, the user character is judged to have lost. The game results and all data are recorded in the data storage unit.

[0118] Further, see Figure 6 In step 202, based on the first competitive strategy and the third competitive strategy, the characters of both parties are controlled to play a strategic game to obtain the second score of the current student, which specifically includes: 601. Based on the first competition strategy and the third competition strategy, the characters of both parties are controlled to obtain props according to their respective competition order and path for props.

[0119] 602. If one of the characters obtains any item first, the item will be removed from the competition order and path of both characters, and their respective competition order and path will be updated.

[0120] 603. Continue to control the characters of both parties to continue acquiring props according to the updated order and path of competition for props until all props are acquired, and count the number of props acquired by the characters of both parties.

[0121] 604. Determine the second score of the current student by performing calculations based on the initial score of the character of the current student, the initial scores of the characters of other students, the number of props obtained by the current student, and the number of props obtained by other students.

[0122] The calculation process of the second score is as follows:

[0123] : Take 1 when your team wins, and take 0 when you lose

[0124] Where: S r is the settlement score after the user plays the game, S a is the initial score of the user role, S b is the initial score of the opponent character, N a Get the number of props for the user role, Q a and Q b is an intermediate parameter in the calculation process; Ea is the score ratio of the current user (user A) and the other user (user B) calculated using the amplified scores of both parties; K and c are both preset parameters, set to 16 and 200 respectively.

[0125] The final output S r Will round up if S r If the calculated value is negative, 0 is returned.

[0126] For example: If your initial score is 456 and your opponent's score is 567, and you obtain 4 items and win, your final score after the game is 472. If your initial score is 300 and your opponent's score is 300, and you obtain 3 items and lose, your final score after the game is 295.

[0127] For the situation where the characters of both parties are controlled based on the first competitive strategy and the third competitive strategy to play a machine-machine strategy game, the above Figure 5 The process of the human-machine battle shown is similar and will not be repeated here.

[0128] In the entire teaching process, students first fight against computer characters with the second competitive strategy and the chance random strategy, and then unlock and experience the advanced mode after accumulating certain experience and achieving certain victories. In the advanced mode, students will fight against characters driven by the AI ​​model generated by the first competitive strategy and based on their own data. The students' winning rate will drop sharply, and students will be inspired to think about new ways to defeat this AI model-based character. Regardless of success or failure, interested students will be guided to use their own data to build their own AI model, and fight against characters created by other users and participate in competitions.

[0129] The examples of the present invention can serve as experience courses and leading courses for artificial intelligence teaching. In the process of preparing lessons, teachers can arrange for students to experience the examples of the present invention and feel the changes brought about by artificial intelligence through practical operations. In this process, students can especially experience the relationship between artificial intelligence and data by quickly building artificial intelligence models, and quickly establish an interest in artificial intelligence. Because it is in the form of a strategy game and can be experienced quickly, relatively young students (7 to 15 years old) can also understand the corresponding content. This makes the examples of the present invention significantly different from previous products as a teaching tool. The examples of the present invention create a brand new AI experience environment that teenagers can understand, without requiring teenagers to understand topics such as housing prices, image recognition or text processing that are difficult for teenagers to understand.

[0130] At the same time, the invention example provides a teaching topic for subsequent artificial intelligence teaching. Teachers can design short and effective courses based on this invention example. Through learning the course, students will improve their understanding of artificial intelligence, and then return to the invention example to quickly establish a new artificial intelligence model. This model will quickly provide feedback to students through the competition, thereby helping them learn.

[0131] At the same time, a major challenge in learning artificial intelligence is to process a large amount of data. However, it is very difficult to obtain a large amount of data. The example of the present invention will generate a large amount of highly regular data through games. These regular data can greatly speed up the process of zero-based students getting in touch with artificial intelligence from scratch and lay the foundation for future learning.

[0132] The interactive teaching device based on artificial intelligence provided by the present invention is described below. The interactive teaching device based on artificial intelligence described below and the interactive teaching method based on artificial intelligence described above can be referred to each other.

[0133] The present invention provides an interactive teaching device based on artificial intelligence, see Figure 7 ,include: The human-machine battle module 701 is used to input the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, output a first competitive strategy, generate a second competitive strategy through the input instructions of the current student for the strategy game, and then control the characters of both parties to play the strategy game based on the first competitive strategy and the second competitive strategy, and obtain a first score of the current student; The machine battle module 702 is used to input the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, output a third competitive strategy, and then control the characters of both sides to play the strategy game based on the first competitive strategy and the third competitive strategy, so as to obtain a second score of the current student; The course selection module 703 is used to adjust the course outline and study plan according to the first score, the second score and the data of the strategy game, and guide the current student to enter the course study.

[0134] Optionally, the data of the strategy game includes the number of props and the position of props; The human-machine battle module 701 is specifically used for: According to the number and location of props in the strategy game, multiple competitive strategies for obtaining props are generated; wherein each competitive strategy includes the competition order and path of multiple props; The multiple competitive strategies are input into the artificial intelligence model corresponding to the current student for calculation to obtain the winning probability corresponding to each competitive strategy, and the competitive strategy with the highest winning probability is output as the first competitive strategy.

[0135] Optionally, the human-computer battle module 701 is specifically used to: determine the competition order and path of the props displayed on the screen through the current student's input instructions for the props; and generate a second competition strategy according to the competition order and path of the props.

[0136] Optionally, the data of the strategy game includes the number of props and the position of props; The machine battle module 702 is specifically used for: According to the number and location of props in the strategy game, multiple competitive strategies for obtaining props are generated; wherein each competitive strategy includes the competition order and path of multiple props; The multiple competitive strategies are input into the artificial intelligence models corresponding to other students for calculation, so as to obtain the winning probability corresponding to each competitive strategy, and the competitive strategy with the highest winning probability is output as the third competitive strategy.

[0137] Optionally, it also includes: a model training module, used to: Training data screening: Real-time capture and recording of the strategies and results adopted by students after the game, and storing these data in a secure data storage module, and automatically selecting the most recent N wins as the winning strategy data; Training data enhancement: Using data enhancement algorithms, more sample data is automatically generated from winning strategy data, including winning sample data and losing sample data; Feature engineering: Calculate the difference between the coordinate values ​​of game props to generate new data points to expand the feature dimension of the data set; Algorithm selection: According to the conditions of the students, select the artificial intelligence algorithm that matches the students; Parameter adjustment: adjust the algorithm parameters according to the type of selected algorithm; Model building and optimization: Use the screened training data, selected algorithms and parameters to train the artificial intelligence model and save it to the operation and maintenance module.

[0138] Optionally, the human-machine battle module 701 is specifically used to: control the characters of both parties to obtain props according to the competition order and path of their respective props based on the first competition strategy and the second competition strategy; If one of the characters obtains any item first, the item will be removed from the competition order and path of both characters, and their respective competition order and path will be updated; Continue to control the two characters to continue to obtain props according to the updated order and path of the competition for props, until all props are obtained, and count the number of props obtained by each character; Determine the current student's first score based on the difference in the number of props.

[0139] Optionally, the machine battle module 702 is specifically configured to: Based on the first competition strategy and the third competition strategy, the characters of both parties are controlled to obtain props according to the competition order and path of their respective props; If one of the characters obtains any item first, the item will be removed from the competition order and path of both characters, and their respective competition order and path will be updated; Continue to control the two characters to continue to obtain props according to the updated order and path of the competition for props, until all props are obtained, and count the number of props obtained by each character; The second score of the current student is determined by calculating based on the initial score of the current student's character, the initial scores of other students' characters, the number of props obtained by the current student, and the number of props obtained by other students.

[0140] Optionally, the course selection module 703 is specifically used to: If the score obtained by the current student in the first competitive strategy exceeds the first set score threshold, a competitive mode is unlocked in which the first competitive strategy and the second competitive strategy are used to control the two characters to play a strategy game, and basic artificial intelligence popular science teaching content is added to the current student's course outline; In the case where the first score of the current student exceeds the second set score threshold, adding an explanation of the basic principles of artificial intelligence to the course outline of the current student; In the case where the second score of the current student exceeds the third set score threshold, continue to add relevant knowledge of establishing an artificial intelligence model to the course outline of the current student.

[0141] Figure 8 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute an interactive teaching method based on artificial intelligence, the method comprising: by inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, outputting a first competitive strategy, generating a second competitive strategy through the input instructions of the current student to the strategy game, and then controlling the two characters to play the strategy game based on the first competitive strategy and the second competitive strategy, and obtaining the first score of the current student; by inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, outputting a third competitive strategy, and then controlling the two characters to play the strategy game based on the first competitive strategy and the third competitive strategy, and obtaining the second score of the current student; adjusting the course outline and learning plan according to the first score, the second score and the data of the strategy game, and guiding the current student to enter the course learning.

[0142] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0143] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the interactive teaching method based on artificial intelligence provided by the above-mentioned methods, which includes: by inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, outputting a first competitive strategy, generating a second competitive strategy through the input instructions of the current student to the strategy game, and then controlling the characters of both sides to play a strategy game based on the first competitive strategy and the second competitive strategy, so as to obtain the first score of the current student; by inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, outputting a third competitive strategy, and then controlling the characters of both sides to play a strategy game based on the first competitive strategy and the third competitive strategy, so as to obtain the second score of the current student; adjusting the course outline and learning plan according to the first score, the second score and the data of the strategy game, and guiding the current student to enter the course learning.

[0144] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the interactive teaching method based on artificial intelligence provided by the above-mentioned methods, the method comprising: inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, outputting a first competitive strategy, generating a second competitive strategy through the input instructions of the current student to the strategy game, and then controlling the characters of both sides to play a strategy game based on the first competitive strategy and the second competitive strategy, to obtain the first score of the current student; inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, outputting a third competitive strategy, and then controlling the characters of both sides to play a strategy game based on the first competitive strategy and the third competitive strategy, to obtain the second score of the current student; adjusting the course outline and learning plan according to the first score, the second score and the data of the strategy game, and guiding the current student to enter the course learning.

[0145] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0146] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An interactive teaching method based on artificial intelligence, characterized in that: For use in a teaching system, the method comprises: By inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, a first competitive strategy is output, a second competitive strategy is generated through the input instructions of the current student for the strategy game, and then based on the first competitive strategy and the second competitive strategy, the characters of both parties are controlled to play the strategy game, and a first score of the current student is obtained; By inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, outputting a third competitive strategy, and then controlling the characters of both parties to play the strategy game based on the first competitive strategy and the third competitive strategy, a second score of the current student is obtained; The course outline and study plan are adjusted according to the first score, the second score and the data of the strategy game, and the current students are guided to start the course study.

2. The interactive teaching method based on artificial intelligence according to claim 1, characterized in that: The data of the strategy game includes the number of props and the position of props; By inputting the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, the first competitive strategy is output, including: According to the number and location of props in the strategy game, multiple competitive strategies for obtaining props are generated; wherein each competitive strategy includes the competition order and path of multiple props; The multiple competitive strategies are input into the artificial intelligence model corresponding to the current student for calculation to obtain the winning probability corresponding to each competitive strategy, and the competitive strategy with the highest winning probability is output as the first competitive strategy.

3. The interactive teaching method based on artificial intelligence according to claim 1, characterized in that: Generate the second competitive strategy through the current student's input instructions, including: Determine the competition order and path of the props displayed on the screen through the current student's input instructions; A second competition strategy is generated according to the competition order and path of the props.

4. The interactive teaching method based on artificial intelligence according to claim 1, characterized in that: The data of the strategy game includes the number of props and the position of props; By inputting the data of the strategy game into the corresponding pre-trained artificial intelligence models of other students, the third competitive strategy is output, including: According to the number and location of props in the strategy game, multiple competitive strategies for obtaining props are generated; wherein each competitive strategy includes the competition order and path of multiple props; The multiple competitive strategies are input into the artificial intelligence models corresponding to other students for calculation, so as to obtain the winning probability corresponding to each competitive strategy, and the competitive strategy with the highest winning probability is output as the third competitive strategy.

5. The interactive teaching method based on artificial intelligence according to claim 1, characterized in that: The AI ​​model corresponding to the current students is trained in the following ways: Training data screening: Real-time capture and recording of the strategies and results adopted by students after the game, and storing these data in a secure data storage module, and automatically selecting the most recent N wins as the winning strategy data; Training data enhancement: Using data enhancement algorithms, more sample data is automatically generated from winning strategy data, including winning sample data and losing sample data; Feature engineering: Calculate the difference between the coordinate values ​​of game props to generate new data points to expand the feature dimension of the data set; Algorithm selection: According to the conditions of the students, select the artificial intelligence algorithm that matches the students; Parameter adjustment: adjust the algorithm parameters according to the type of selected algorithm; Model building and optimization: Use the screened training data, selected algorithms and parameters to train the artificial intelligence model and save it to the operation and maintenance module.

6. The interactive teaching method based on artificial intelligence according to claim 1, characterized in that: Based on the first competitive strategy and the second competitive strategy, the characters of both parties are controlled to play a strategic game against each other, and a first score of the current student is obtained, including: Based on the first competition strategy and the second competition strategy, the characters of both parties are controlled to obtain props according to the competition order and path of their respective props; If one of the characters obtains any item first, the item will be removed from the competition order and path of both characters, and their respective competition order and path will be updated; Continue to control the two characters to continue to obtain props according to the updated order and path of the competition for props, until all props are obtained, and count the number of props obtained by each character; Determine the current student's first score based on the difference in the number of props.

7. The interactive teaching method based on artificial intelligence according to claim 1, characterized in that: Based on the first competitive strategy and the third competitive strategy, the characters of both parties are controlled to play a strategic game against each other, and a second score of the current student is obtained, specifically including: Based on the first competition strategy and the third competition strategy, the characters of both parties are controlled to obtain props according to the competition order and path of their respective props; If one of the characters obtains any item first, the item will be removed from the competition order and path of both characters, and their respective competition order and path will be updated; Continue to control the two characters to continue to obtain props according to the updated order and path of the competition for props, until all props are obtained, and count the number of props obtained by each character; The second score of the current student is determined by calculating based on the initial score of the current student's character, the initial scores of other students' characters, the number of props obtained by the current student, and the number of props obtained by other students.

8. The interactive teaching method based on artificial intelligence according to claim 1, characterized in that: Adjusting the course outline and study plan according to the first score, the second score, and the data of the strategy game, and guiding the current students to enter the course study, including: If the score obtained by the current student in the first competitive strategy exceeds the first set score threshold, a competitive mode is unlocked in which the first competitive strategy and the second competitive strategy are used to control the two characters to play a strategy game, and basic artificial intelligence popular science teaching content is added to the current student's course outline; In the case where the first score of the current student exceeds the second set score threshold, adding an explanation of the basic principles of artificial intelligence to the course outline of the current student; In the case where the second score of the current student exceeds the third set score threshold, continue to add relevant knowledge of establishing an artificial intelligence model to the course outline of the current student.

9. An interactive teaching device based on artificial intelligence, characterized in that: For use in a teaching system, the device comprises: The human-machine battle module is used to input the data of the strategy game into the pre-trained artificial intelligence model corresponding to the current student, output a first competitive strategy, generate a second competitive strategy through the input instructions of the current student for the strategy game, and then control the characters of both parties to play the strategy game based on the first competitive strategy and the second competitive strategy, and obtain a first score of the current student; The machine battle module is used to input the data of the strategy game into the pre-trained artificial intelligence model corresponding to other students, output a third competitive strategy, and then control the characters of both sides to play the strategy game based on the first competitive strategy and the third competitive strategy, so as to obtain the second score of the current student; The course selection module is used to adjust the course outline and study plan according to the first score, the second score and the data of the strategy game, and guide the current students to enter the course study.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the interactive teaching method based on artificial intelligence as described in any one of claims 1 to 8 is implemented.

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