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A Study of the Teaching of Python Programming at Secondary Vocational Levels with a View to Generative Artificial Intelligence

Author(s)

Shangyi Cai1, Yanna Feng2,*

Affiliation(s)

1College of Applied Science and Technology,Beijing Union University, Beijing, China 2Beijing Union University, Beijing, China *Corresponding Author

Abstract

Generative artificial intelligence may offer an innovative solution to some of the problems of secondary vocational Python teaching. The present article takes up the students' cognitive standpoint, looks at AI's role in instruction, and formulates a basic design principle—limited, moderate, or full—for the degree of AI involvement. Regarding BOPPPS, three defects in secondary vocational contexts are analyzed: pre-assessment not detecting difficulties, participatory learning without adequate task design, and post-assessment neglecting understanding. It suggests connecting pre-assessment with AI-centered diagnosis of learning, using task-oriented approaches for participatory learning, and adding a "code explanation" item to post-assessment. Then it outlines—on an exploratory basis—an overall scheme for pre-class, in-class, and post-class stages.

Keywords

Generative Artificial Intelligence; Secondary Vocational Training; Python Code; BOPPPS Model; Teaching Experimentation

References

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