The Impact of Large Language Models on Functional Diagram Generation: An Extended Cognition-Based Investigation of First-Year Architecture Students

Abstract

This study investigates the impact of Large Language Models (LLMs) on the functional diagram generation process of novice architecture students within the framework of extended cognition theory. Through a quasi-experimental design involving 41 first-year students, the research compares topological networks generated through traditional sketching with those produced through GenAI interaction for a weekend house project. Quantitative analyses of spatial nodes and edges reveal a significant cognitive expansion: LLM assistance increased the number of spaces by 11.4% and connections by 17.9%. However, the findings also indicate a qualitative shift toward instrumental dominance. Students made statistically insignificant curatorial reductions to the AI-generated complex networks, acting more as passive editors than active co-creators. Furthermore, extended dialogue loops resulted in additive topological expansion rather than hierarchical refinement. The study concludes that while AI offers substantial capacity enhancement, architectural pedagogy must cultivate spatial critical thinking to filter the hyper-productive nature of AI.