Learning Dependency Issues and Educational Solutions in Generative AI-Based Python Instruction for University Students

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Key Points

The research aims to examine the impact of generative AI on students' coding capabilities and dependency issues.
Compared assignment performance with generative AI assistance and final exam performance in Python programming students.
Analyzed dependency trends based on task difficulty and strategic behaviors of students using AI-generated code.
Found a significant performance drop of 42.3% between assignment achievement (90%) and actual exam performance (47.7%).
Identified that students engaged in strategic behaviors to disguise AI-generated code as their own.
Highlight AI's contribution to short-term performance while indicating limitations in fostering intrinsic capabilities.

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Analyzes learning dependency in coding education, suggesting a shift in assessment models.

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Hee-Sook Park (2026) studied this question.

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