Design and Optimization of Aesthetic Education Teaching Information Platform Based on Big Data Analysis

Area of Science:

• Educational Technology
• Artificial Intelligence Education

Background:

• Schools face challenges in aesthetic education, including inadequate campus environments and a lack of aesthetic integration across disciplines.
• Current educational approaches may not fully leverage technology for specialized courses like artificial intelligence.

Purpose of the Study:

• To develop an AI-driven teaching platform to address deficiencies in school aesthetic education.
• To enhance the teaching quality and student learning efficiency in artificial intelligence (AI) core courses.

Main Methods:

• Utilized a flipped classroom model within a network learning space.
• Developed the platform using PHP, HTML+CSS+JS, and other web technologies.
• Implemented a genetic algorithm for multi-combination optimization to find optimal solutions.

Main Results:

• Successfully constructed an AI core course website serving as a dedicated teaching platform.
• The platform integrates AI task-driven teaching characteristics with the flipped classroom model.
• The genetic algorithm approach optimized teaching strategies and resource allocation.

Conclusions:

• The developed AI teaching platform effectively improves the quality of AI education.
• Student learning efficiency in AI courses is significantly enhanced through this innovative approach.
• The integration of AI, flipped classrooms, and genetic algorithms offers a promising model for future educational technologies.