This study focuses on the application effect of the artificial intelligence-enabled blended teaching mode in Python courses and verifies its effectiveness through teaching practice. To enhance the reliability of the experiment, an experimental group and a control group were designed for comparison. The experimental group adopted a blended mode of "AI technology + traditional teaching", while the control group employed traditional teaching methods. The teaching experiment lasted 16 weeks. The research adopted a pre-test-post-test design, comprehensively assessing the impact of AI tools on students' learning interest, Python programming ability, and collaborative learning effectiveness through multi-dimensional data, including analysis of the quality of Python code written by students and surveys on students' engagement in learning Python language. The results indicated that the experimental group, adopting the "AI technology + traditional teaching" approach, significantly outperformed the control group in debugging efficiency, code complexity, and project completion (all t-values > 6.45, p-values < 0.01). The average score of the experimental group in the post-test increased by 13.3 points (p < 0.01), and their scores in learning interest and teaching mode satisfaction were higher than those of the control group (p < 0.01). Through analysis, it was found that the real-time feedback and personalized learning path design of AI tools effectively shortened the "error-correction" cycle time, reducing programming error rates by 42% and increasing students' participation in collaborative learning by 129%. The study demonstrated that the AI-enabled blended teaching mode can significantly enhance the teaching effectiveness of Python and greatly assist in improving students' programming abilities.

Wenbin Shao, Weiwei Shao. Research on the Innovation of Teaching Mode for Python Programming Course Driven by Artificial Intelligence. International Journal of New Developments in Education (2025), Vol. 7, Issue 11: 7-13. https://doi.org/10.25236/IJNDE.2025.071102.

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