Repositioning Artificial Intelligence in Architectural Conceptual Design: An Experimental Comparative Model for Data-Driven Spatial Decision-Making
TL;DR
Whether AI-informed conceptual design generates spatial configurations that differ measurably from conventional approaches is examined, which will contribute to the development of data-driven, adaptive, and user-centered architectural methodologies.
Abstract
Integrating artificial intelligence (AI) into the built environment has greatly improved building automation and performance optimization. However, the potential of AI to inform early spatial configuration, user-oriented planning, and its role in shaping architectural decisions at the conceptual design stage have not been well studied. This study aims to compare traditional architectural design processes with AI-informed design approaches at the conceptual stage. An experimental comparative design model is employed in which two parallel design scenarios are developed for the same prototype: a conventional, architect-led concept design and an AI informed concept design. The comparative evaluation framework is structured around five multidimensional criteria: Space Utilization Efficiency, Daylight Performance, Circulation Optimization, User Scenario Compatibility, and Spatial Adaptation Capacity. This study examines whether AI-informed conceptual design generates spatial configurations that differ measurably from conventional approaches. The findings will contribute to the development of data-driven, adaptive, and user-centered architectural methodologies.