Repositioning Artificial Intelligence in Architectural Conceptual Design: An Experimental Comparative Model for Data-Driven Spatial Decision-Making

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.