用量化方法评估AI编程中思维外包对学习效果的影响

The integration of Large Language Models (LLMs) into software engineering education has driven the emergence of ``Vibe Coding,'' a paradigm where developers articulate high-level intent through natural language and delegate implementation to AI agents. While proponents argue this approach modernizes pedagogy by emphasizing conceptual design over syntactic memorization, accumulating empirical evidence raises concerns regarding skill retention and deep conceptual understanding. This paper proposes a theoretical framework to investigate the research question: \textit{Is Vibe Coding a better way to learn software engineering?} We posit a divergence in student outcomes between those leveraging AI for acceleration versus those using it for cognitive offloading. To evaluate these educational trade-offs, we propose the \textbf{Vibe-Check Protocol (VCP)}, a systematic benchmarking framework incorporating three quantitative metrics: the \textit{Cold Start Refactor} ($M_{CSR}$) for modeling skill decay; \textit{Hallucination Trap Detection} ($M_{HT}$) based on signal detection theory to evaluate error identification; and the \textit{Explainability Gap} ($E_{gap}$) for quantifying the divergence between code complexity and conceptual comprehension. Through controlled comparisons, VCP aims to provide a quantitative basis for educators to determine the optimal pedagogical boundary: identifying contexts where Vibe Coding fosters genuine mastery and contexts where it introduces hidden technical debt and superficial competence.