THE SYNTACTIC FACADE: A THEORETICAL PERSPECTIVE ON COGNITIVE ATROPHY IN AI-AUGMENTED LEARNING

The integration of Large Language Models (LLMs) into the Software Development Life Cycle (SDLC) marks a fundamental transition from human-centric programming to AI- augmented workflows. While this evolution promises substantial productivity gains, it introduces significant pedagogical risks for students and novice developers. This paper investigates the phenomenon of “Cognitive Atrophy,” arguing that an over- reliance on generative AI prioritizes Syntactic Fluency, the ability to produce functional code at the expense of Architectural Reasoning. We define this discrepancy as the “Syntactic Facade,” a condition where developers produce seemingly correct code without possessing the underlying mental models to explain its logic or maintain its structure. This creates a dangerous “Confidence-Competence Gap,” where the speed of delivery masks a lack of foundational understanding. As active learning characterized by struggle, trial, and error is replaced by the passive acceptance of AI suggestions, the long-term development of deep problem-solving skills is threatened. By analyzing these theoretical concepts, this paper highlights the risks of substituting critical thinking with automated tools. We explore how the erosion of mental effort in computer science education may produce a generation of “assemblers” rather than “architects,” ultimately weakening the technical resilience of the future workforce.