Teenager practice AI project cooperative training system and method

Abstract

The invention discloses a teenager practice AI project collaborative training system and method. The teenager practice AI project collaborative training system comprises a user management module, a project management module, a collaborative communication module, a knowledge learning module, a practice operation module, an evaluation feedback module and a data statistical analysis module. The invention provides a complete teenager practice AI project cooperative training platform which covers a plurality of functional modules such as user management, project management, cooperative communication, knowledge learning, practice operation, evaluation feedback and data statistical analysis, and various requirements of teenagers in the AI project practice training process are met. Through the cooperation communication module and the project management module, team cooperation among teenager users is promoted, and team cooperation ability and communication ability of teenagers are cultivated.

Description

Technical Field

[0001] The present invention relates to the technical field of adolescent practical AI projects, and particularly to a collaborative training system and method for adolescent practical AI projects. Background Art

[0002] With the rapid development of artificial intelligence technology, the learning and practical needs of adolescents for AI knowledge are increasing day by day. However, there are many problems in the current AI practical training for adolescents. For example, there is a lack of a systematic collaborative training platform, making it difficult for adolescents to communicate, collaborate, and share resources effectively during the project practice process; the training content is single and cannot meet the needs of adolescents at different learning stages and with different interests; there is a lack of a scientific evaluation mechanism, making it difficult to accurately measure the learning achievements and ability improvement of adolescents in AI project practice. Therefore, there is an urgent need for a collaborative training system and method for adolescent practical AI projects that can solve the above problems. Summary of the Invention

[0003] The purpose of the present invention is to provide a collaborative training system and method for adolescent practical AI projects to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: A collaborative training system for adolescent practical AI projects includes a user management module, a project management module, a collaborative communication module, a knowledge learning module, a practical operation module, an evaluation and feedback module, and a data statistics and analysis module;

[0005] The user management module is used to manage the information of adolescent users and teacher users, including user registration, login, personal information modification, and permission allocation:

[0006] The project management module is responsible for the whole-process management of adolescent practical AI projects;

[0007] The collaborative communication module provides a platform for real-time communication and collaboration for adolescent users and teachers participating in the same project, including text chat, voice call, and file sharing;

[0008] The knowledge learning module integrates rich AI knowledge resources, including course videos, document materials, and case analyses; according to the learning progress and knowledge mastery of adolescent users, personalized learning content is pushed to them using an adaptive learning algorithm;

[0009] The practical operation module is used to provide a collaborative training platform for adolescents to practice AI projects, helping them improve their practical ability and teamwork level in the field of artificial intelligence;

[0010] The said evaluation feedback module is responsible for comprehensively evaluating the performance of teenagers in AI project collaborative training. Through multi-dimensional data collection, including project completion progress, team collaboration effectiveness, and problem-solving ability, it generates a detailed evaluation report;

[0011] The said data statistics and analysis module statistically analyzes the learning behavior data, project practice data, and evaluation data of teenage users, generates various statistical reports and visualization charts, such as learning progress statistics, project completion situation statistics, skill mastery situation analysis, etc., to provide a decision-making basis for teachers and system administrators.

[0012] Preferably, the user information in the said user management module is stored in a database, and the user password is encrypted and stored using the hash algorithm to improve the security of user information; when a user registers, the system verifies the uniqueness of the username through the following formula:

[0013]

[0014] where U represents the set of all registered users, new_name is the newly registered username, and U unique indicates whether the username is unique.

[0015] Preferably, the said project management module supports teachers to create and publish AI project tasks, and teenage users can browse and select interesting projects to join; the information for joining a project includes project name, goal, task description, time requirement, and required skills; a content-based recommendation algorithm is adopted to recommend suitable AI projects for teenage users according to their historical project participation records, skill tags, and interest preferences. The recommendation formula is as follows:

[0016]

[0017] where R ij represents the predicted score of user i for project j, w ik represents the weight of user i for skill k, s kj represents the relevance between project j and skill k, and n is the total number of skills.

[0018] Preferably, the said collaboration and communication module uses the WebSocket protocol to achieve real-time communication, improving the timeliness and stability of information transmission; during the file sharing process, a version control algorithm is adopted to record each modification of the file, facilitating users to trace and restore historical versions.

[0019] Preferably, in the said knowledge learning module, the adaptive learning algorithm adjusts the difficulty of learning content according to the user's answer correct rate p and answer time t. The formula is as follows:

[0020]

[0021] Among them, D old is the difficulty of the current learning content, D new is the adjusted difficulty of the learning content, α and β are adjustment coefficients, and T is the set answering time threshold.

[0022] Preferably, the practical operation module provides an online programming environment and AI development tools, enabling young users to write code, train models, and conduct tests for AI projects in this module; automatically detect code syntax errors and provide real-time error prompts and solutions; during the model training process, use the stochastic gradient descent algorithm SGD to optimize the model parameters, and the formula is as follows:

[0023]

[0024] Among them, θ t is the model parameter at the t-th iteration, θ t+1 is the updated model parameter, η is the learning rate, is the gradient of the objective function J(θ) at θ t at that point.

[0025] Preferably, in the evaluation and feedback module, teachers can evaluate the performance of young users in project practice. The evaluation indicators include code quality, model performance, teamwork ability, and learning attitude; generate a detailed feedback report based on the evaluation results to provide improvement suggestions and learning directions for young users; use the analytic hierarchy process AHP to determine the weights of each evaluation indicator.

[0026] Preferably, the analytic hierarchy process AHP includes the following steps:

[0027] Step 1: Construct a judgment matrix: According to the relative importance of each indicator, construct a judgment matrix A = (a ij ) n×n , where a ij represents the degree of importance of indicator i relative to indicator j;

[0028] Step 2: Calculate the weight vector: By calculating the maximum eigenvalue λ max and the corresponding eigenvector W of the judgment matrix, obtain the weight vector of each indicator, and the formula is W = (w1, w2,..., w n ) T , where w n is the weight of indicator n;

[0029] Step 3: Consistency test: Calculate the consistency index and find the corresponding average random consistency index RI, and calculate the consistency ratio When CR < 0.1, it is considered that the judgment matrix has satisfactory consistency and the weight vector is valid.

[0030] A training method for a collaborative training system of an AI project for teenagers' practice, comprising the following steps:

[0031] Step 1, user registration and login: Teenager users and teacher users register and log in through the user management module, and the system verifies the accuracy and legality of the user information;

[0032] Step 2, project selection and participation: Teachers create and publish AI project tasks in the project management module, and teenager users select interesting projects to join from the projects recommended by the system according to their own interests and abilities;

[0033] Step 3, collaborative learning and practice: Teenager users participating in the project communicate and collaborate through the collaborative communication module, obtain the required AI knowledge using the knowledge learning module, and write project codes, train models, and conduct tests in the practice operation module;

[0034] Step 4, evaluation and feedback: After the project ends, teachers evaluate the performance of teenager users through the evaluation feedback module, and the system generates a feedback report and pushes it to the users;

[0035] Step 5, data analysis and optimization: The data statistical analysis module conducts statistical analysis on user data, and the system optimizes the project recommendation strategy, learning content push, and evaluation index system according to the analysis results, continuously improving the training effect of the system and the user experience.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] The present invention provides a complete collaborative training platform for an AI project for teenagers' practice, covering multiple functional modules such as user management, project management, collaborative communication, knowledge learning, practice operation, evaluation feedback, and data statistical analysis, meeting various needs of teenagers during the practice training process of AI projects. Through the collaborative communication module and the project management module, the teamwork among teenager users is promoted, and the teamwork ability and communication ability of teenagers are cultivated. The evaluation feedback module and the data statistical analysis module can timely and accurately evaluate the learning achievements and ability improvement of teenager users, providing a decision-making basis for teachers and system administrators, and facilitating the optimization and improvement of the system. Description of the Drawings

[0038] Figure 1 is the system schematic diagram of the present invention;

[0039] Figure 2 is the method flow chart of the present invention. Detailed Embodiments

[0040] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] Please refer to Figure 1-2 , the present invention provides a collaborative training system for adolescent practical AI projects, including a user management module, a project management module, a collaborative communication module, a knowledge learning module, a practical operation module, an evaluation and feedback module, and a data statistics and analysis module;

[0042] The user management module is used to manage the information of adolescent users and teacher users, including user registration, login, personal information modification, and permission allocation:

[0043] The project management module is responsible for the full-process management of adolescent practical AI projects;

[0044] The collaborative communication module provides a platform for real-time communication and collaboration for adolescent users and teachers participating in the same project, including text chat, voice call, and file sharing;

[0045] The knowledge learning module integrates rich AI knowledge resources, including course videos, document materials, and case analyses; according to the learning progress and knowledge mastery of adolescent users, personalized learning content is pushed to them using an adaptive learning algorithm;

[0046] The practical operation module is used to provide a collaborative training platform for adolescents for practical AI projects, helping them improve their practical abilities and team collaboration levels in the field of artificial intelligence;

[0047] The evaluation and feedback module is responsible for comprehensively evaluating the performance of adolescents in the collaborative training of AI projects. Through multi-dimensional data collection, including project completion progress, team collaboration effectiveness, and problem-solving abilities, a detailed evaluation report is generated;

[0048] The data statistics and analysis module statistically analyzes the learning behavior data, project practice data, and evaluation data of adolescent users, generates various statistical reports and visual charts, such as learning progress statistics, project completion situation statistics, and skill mastery analysis, etc., providing a decision-making basis for teachers and system administrators.

[0049] The user information in the user management module is stored in the database, and the user password is encrypted and stored using the hash algorithm to improve the security of user information; when a user registers, the system verifies the uniqueness of the username through the following formula:

[0050]

[0051] Among them, U represents the set of all registered users, new_name is the newly registered user name, and U unique indicates whether the user name is unique.

[0052] The project management module supports teachers to create and publish AI project tasks, and adolescent users can browse and select interesting projects to join; the information for joining a project includes project name, objectives, task description, time requirements, and required skills; a content-based recommendation algorithm is adopted to recommend suitable AI projects for adolescent users according to their historical project participation records, skill tags, and interest preferences. The recommendation formula is as follows:

[0053]

[0054] Among them, R ij represents the predicted score of user i for project j, and w ik represents the weight of user i for skill k, and s k represents the relevance between project j and skill k, and n is the total number of skills.

[0055] The collaboration and communication module uses the WebSocket protocol to achieve real-time communication, improving the timeliness and stability of information transmission; during the file sharing process, a version control algorithm is adopted to record each modification of the file, facilitating users to trace and restore historical versions.

[0056] In the knowledge learning module, the adaptive learning algorithm adjusts the difficulty of the learning content according to the user's answer correct rate p and answer time t. The formula is as follows:

[0057]

[0058] Among them, D old is the current difficulty of the learning content, D new is the adjusted difficulty of the learning content, α and β are adjustment coefficients, and T is the set answer time threshold.

[0059] The practical operation module provides an online programming environment and AI development tools. Adolescent users can write code, train models, and test AI projects in this module; it automatically detects code syntax errors and provides real-time error prompts and solutions; during the model training process, the stochastic gradient descent algorithm SGD is used to optimize the model parameters. The formula is as follows:

[0060]

[0061] Among them, θ t is the model parameter at the t-th iteration, θ t+1 is the updated model parameter, and η is the learning rate. is the gradient of the objective function J(θ) at θ t here.

[0062] In the evaluation feedback module, the teacher can evaluate the performance of adolescent users in project practice. The evaluation indicators include code quality, model performance, teamwork ability, and learning attitude. A detailed feedback report is generated based on the evaluation results to provide improvement suggestions and learning directions for adolescent users. The analytic hierarchy process (AHP) is used to determine the weights of each evaluation indicator.

[0063] The analytic hierarchy process (AHP) includes the following steps:

[0064] Step 1: Construct a judgment matrix: According to the relative importance of each indicator, construct a judgment matrix A = (a ij ) n×n , where a ij represents the degree of importance of indicator i relative to indicator j;

[0065] Step 2: Calculate the weight vector: By calculating the maximum eigenvalue λ max and the corresponding eigenvector W of the judgment matrix, obtain the weight vector of each indicator. The formula is W = (w1, w2,..., w n ) T , where w n is the weight of indicator n;

[0066] Step 3: Consistency test: Calculate the consistency index and find the corresponding average random consistency index RI. Calculate the consistency ratio When CR < 0.1, it is considered that the judgment matrix has satisfactory consistency and the weight vector is valid.

[0067] A training method for an adolescent practice AI project collaboration training system includes the following steps:

[0068] Step 1: User registration and login: Adolescent users and teacher users register and log in through the user management module, and the system verifies the accuracy and legality of the user information;

[0069] Step 2: Project selection and participation: The teacher creates and publishes AI project tasks in the project management module, and adolescent users select interesting projects to join from the projects recommended by the system according to their own interests and abilities;

[0070] Step 3: Collaborative learning and practice: Adolescent users participating in the project communicate and collaborate through the collaborative communication module, obtain the required AI knowledge using the knowledge learning module, and perform project code writing, model training, and testing in the practice operation module;

[0071] Step 4: Evaluation and Feedback: After the project ends, the teacher evaluates the performance of adolescent users through the evaluation and feedback module, and the system generates a feedback report and pushes it to the users;

[0072] Step 5: Data Analysis and Optimization: The data statistical analysis module conducts statistical analysis on user data, and the system optimizes the project recommendation strategy, learning content push, and evaluation index system according to the analysis results, continuously improving the training effect of the system and the user experience.

[0073] Example:

[0074] In practical applications, adolescent user A registers and logs in to the system through the user management module. After the system verifies their information, corresponding permissions are granted. The teacher publishes an AI project based on image recognition in the project management module. The project management module recommends this project to user A using a content-based recommendation algorithm based on user A's historical project participation records and skill tags. After user A selects to join the project, they communicate with other project members through the collaboration and communication module to discuss the project plan and division of labor.

[0075] In the knowledge learning module for user A, the system uses an adaptive learning algorithm to push course videos and document materials related to image recognition based on their previous learning situation. User A writes image recognition code using an online programming environment in the practical operation module and trains the model using the stochastic gradient descent algorithm. During the training process, the practical operation module automatically detects code syntax errors and provides error prompts.

[0076] After the project is completed, the teacher uses the analytic hierarchy process to determine the weights of each evaluation index through the evaluation and feedback module, evaluates user A's code quality, model performance, teamwork ability, etc. in the project, and the system generates a feedback report and pushes it to user A. At the same time, the data statistical analysis module conducts statistical analysis on the data of all adolescent users participating in this project, generates a statistical report on the project completion situation and an analysis chart of skill mastery, providing a basis for subsequent project optimization and system improvement.

[0077] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A collaborative training system for adolescent practical AI projects, characterized in that: It includes a user management module, a project management module, a collaboration and communication module, a knowledge learning module, a practical operation module, an evaluation and feedback module, and a data statistics and analysis module; The user management module is used to manage the information of adolescent users and teacher users, including user registration, login, personal information modification, and permission allocation; The project management module is responsible for the full-process management of adolescent practice AI projects; The collaboration and communication module provides a platform for real-time communication and collaboration for adolescent users and teachers participating in the same project, including text chat, voice call, and file sharing; The knowledge learning module integrates rich AI knowledge resources, including course videos, document materials, and case analyses; according to the learning progress and knowledge mastery of adolescent users, it uses an adaptive learning algorithm to push personalized learning content for them; The practical operation module is used to provide a collaborative training platform for adolescents to practice AI projects, helping them improve their practical abilities and teamwork levels in the field of artificial intelligence; The evaluation and feedback module is responsible for comprehensively evaluating the performance of adolescents in the collaborative training of AI projects. Through multi-dimensional data collection, including project completion progress, teamwork effectiveness, and problem-solving abilities, it generates a detailed evaluation report; The data statistics and analysis module statistically analyzes the learning behavior data, project practice data, and evaluation data of adolescent users, generates various statistical reports and visualization charts, and provides a decision-making basis for teachers and system administrators.

2. The collaborative training system for adolescent practice AI projects according to claim 1, characterized in that: In the user management module, user information is stored in a database, and the user password is encrypted and stored using a hash algorithm to improve user information security; when a user registers, the system verifies the uniqueness of the username through the following formula: Among them, U represents the set of all registered users, new_name is the newly registered user name, and U unique indicates whether the user name is unique.

3. The collaborative training system for adolescent practical AI projects according to claim 1, wherein: The project management module supports teachers to create and publish AI project tasks, and adolescent users can browse and select interesting projects to join; the information for joining a project includes project name, goal, task description, time requirement, and required skills; using a content-based recommendation algorithm, according to the historical project participation records, skill tags, and interest preferences of adolescent users, it recommends suitable AI projects for them, and the recommendation formula is as follows: Among them, R ij represents the predicted score of user i for project j, w ik represents the weight of user i for skill k, s kj represents the relevance between project j and skill k, and n is the total number of skills.

4. The collaborative training system for adolescent practice AI projects according to claim 1, characterized in that: The collaboration and communication module uses the WebSocket protocol to achieve real-time communication, improving the timeliness and stability of information transmission; during the file sharing process, a version control algorithm is used to record each modification of the file, facilitating users to trace and restore historical versions.

5. The collaborative training system for adolescent practical AI projects according to claim 1, characterized in that: In the knowledge learning module, the adaptive learning algorithm adjusts the difficulty of learning content according to the user's answer correct rate p and answer time t, and the formula is as follows: Among them, D old is the difficulty of the current learning content, D new is the adjusted difficulty of the learning content, α and β are adjustment coefficients, and T is the set answering time threshold.

6. The collaborative training system for adolescent practice AI projects according to claim 1, characterized in that: The practical operation module provides an online programming environment and AI development tools. Adolescent users can write code, train models, and test AI projects in this module; it automatically detects code syntax errors and provides real-time error prompts and solutions; during the model training process, the stochastic gradient descent algorithm SGD is used to optimize model parameters, and the formula is as follows: where, θ t is the model parameter at the t-th iteration, θ t+1 is the updated model parameter, η is the learning rate, is the gradient of the objective function J(θ) at θ t at that point.

7. An AI project collaborative training system for teenagers' practice according to claim 1, characterized in that: In the said evaluation and feedback module, teachers can evaluate the performance of adolescent users in project practice. The evaluation indicators include code quality, model performance, teamwork ability, and learning attitude. A detailed feedback report is generated based on the evaluation results to provide improvement suggestions and learning directions for adolescent users. The Analytic Hierarchy Process (AHP) is used to determine the weights of each evaluation indicator.

8. An AI project collaborative training system for teenagers' practice according to claim 1, characterized in that: The said Analytic Hierarchy Process (AHP) includes the following steps: Step 1. Construct a judgment matrix: According to the relative importance of each index, construct a judgment matrix \(A=(a_{ij})\), where \(a_{ij}\) represents the degree of importance of index \(i\) relative to index \(j\). ij ) n×n , where \(a_{ij}\) ij represents the degree of importance of index \(i\) relative to index \(j\). Step 2. Calculate the weight vector: By calculating the maximum eigenvalue λ of the judgment matrix max and the corresponding eigenvector W, the weight vector of each index is obtained. The formula is W = (w1, w2,..., w n ), T , where w n is the weight of index n; Step 3. Consistency check: Calculate the consistency index and find the corresponding average random consistency index RI, and calculate the consistency ratio When CR < 0.1, it is considered that the judgment matrix has satisfactory consistency and the weight vector is effective.

9. The training method of a collaborative training system for adolescent practice AI projects according to any one of claims 1-8, characterized in that: It includes the following steps: Step 1: User registration and login: Adolescent users and teacher users register and log in through the user management module, and the system verifies the accuracy and legality of user information. Step 2: Project selection and participation: Teachers create and publish AI project tasks in the project management module. Adolescent users select projects they are interested in from the projects recommended by the system to join according to their own interests and abilities. Step 3: Collaborative learning and practice: Adolescent users participating in the project communicate and collaborate through the collaborative communication module, obtain the required AI knowledge using the knowledge learning module, and write project code, train models, and conduct tests in the practice operation module. Step 4: Evaluation and feedback: After the project ends, teachers evaluate the performance of adolescent users through the evaluation and feedback module, and the system generates a feedback report and pushes it to the users. Step 5: Data analysis and optimization: The data statistics and analysis module conducts statistical analysis on user data. The system optimizes the project recommendation strategy, learning content push, and evaluation index system according to the analysis results to continuously improve the training effect of the system and the user experience.

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