The Use of Thinking Visualization Techniques in College Teaching Based on Improved Genetic Algorithms

Area of Science:

• Educational Technology
• Artificial Intelligence
• Cognitive Science

Background:

• Current educational resources lack energy efficiency and proven teaching methods.
• University education requires integrated resources and improved teaching quality.
• Artificial intelligence (AI) and thinking visualization offer potential solutions.

Purpose of the Study:

• To explore the application of AI and thinking visualization in college education.
• To leverage genetic algorithms (GA) and thinking visualization for improved teaching effectiveness.
• To analyze the integration of GA and visualization for enhanced learning outcomes.

Main Methods:

• Utilizing genetic algorithms as AI technical support for teaching and learning.
• Employing thinking visualization technology as a tool for college teaching.
• Conducting research analysis on the combined application of GA and visualization.

Main Results:

• GA and thinking visualization optimize educational service delivery.
• Teacher workload and learning costs are reduced.
• Learning effects, student experience, and efficient thinking are enhanced.

Conclusions:

• AI, specifically GA, and thinking visualization are effective in optimizing college education.
• The integration of these technologies improves teaching quality and student learning outcomes.
• This approach contributes to more efficient and effective educational practices.