The Vibe-Check Protocol: Quantifying Cognitive Offloading in AI Programming
It addresses educators and researchers concerned about skill retention in AI-assisted programming, but it is incremental as it builds on existing empirical evidence with a new benchmarking framework.
This paper tackles the problem of evaluating whether 'Vibe Coding'—using AI for software engineering education—improves learning outcomes, proposing the Vibe-Check Protocol with metrics like skill decay and error detection to quantify trade-offs between acceleration and cognitive offloading.
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.