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.

翻译：大型语言模型（LLM）与软件工程教育的融合催生了“氛围编程”范式的兴起，该范式允许开发者通过自然语言表达高层意图，并将实现任务委托给AI智能体。支持者认为，这种方法通过强调概念设计而非语法记忆，实现了教学法的现代化；然而，不断积累的实证证据引发了关于技能保持与深层概念理解的担忧。本文提出一个理论框架以研究以下核心问题：\textit{氛围编程是否是一种更优的软件工程学习方式？} 我们假设，利用AI进行加速的学生与将其用于认知卸载的学生在学习成果上存在分化。为评估这些教育权衡，我们提出\textbf{Vibe-Check协议（VCP）}——一个系统化的基准测试框架，包含三项量化指标：用于建模技能衰退的\textit{冷启动重构指标}（$M_{CSR}$）；基于信号检测理论以评估错误识别能力的\textit{幻觉陷阱检测指标}（$M_{HT}$）；以及用于量化代码复杂度与概念理解间差异的\textit{可解释性差距指标}（$E_{gap}$）。通过受控对比实验，VCP旨在为教育者确定最佳教学边界提供量化依据：识别氛围编程能真正促进技能掌握的场景，以及可能引入隐性技术债务与表面能力的场景。