Author(s)

Renjie Lv ¹, Jing Zhang ¹

Affiliation(s)

¹ School of Information and Intelligence Engineering, University of Sanya, Sanya, China

Corresponding Author

Renjie Lv

ABSTRACT

The widespread application of large AI models has brought dual impacts on Python programming education: while enhancing learning convenience, it has also intensified students' cognitive dependence and weakened their practical programming abilities. This study constructs and implements a "micro-project-based practice teaching model driven by large AI models," decomposing course content into logically progressive micro-projects and integrating human–AI collaboration mechanisms with process-oriented evaluation. A total of 52 students participated in a one-semester teaching experiment. The results show that the average score increased from 68.5 to 79.2, the proportion of high-achieving students rose from 12% to 31%, classroom interaction rate improved from 41.5% to 78.6%, and the proportion of AI-generated code directly used decreased from 72.4% to 38.7%. Furthermore, this study establishes a competency development model and a human–AI collaboration efficiency model, with a model fitting coefficient of R2=0.87 and a 54.7% improvement in learning efficiency. The findings provide both theoretical support and practical guidance for programming education reform in the AI era, and offer a replicable framework for integrating AI technologies into foundational programming curricula.

KEYWORDS

Large AI Models; Python Teaching; Micro-Project-Based Learning; Competency Development Model; Human–AI Collaboration

CITE THIS PAPER

Renjie Lv, Jing Zhang. Research on Micro-Project-Based Python Programming Practice Teaching Reform Driven by Large AI Models. Automation and Machine Learning (2026). Vol. 7, No. 1, 143-149. DOI: http://dx.doi.org/10.23977/autml.2026.070118.

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