TRACE-SC: A Protocol-Based Framework for Mapping Generative AI in Space-Constituting Architectural Design Decisions
TL;DR
GenAI proves more than a visual production tool, but its engagement is limited, episodic, and designer-steered rather than a routine design partnership—partial support for the proposition.
Abstract
Architectural space is produced through decisions about form, material, construction, structure, program, and environment, and design education centers on it. In design studios, generative AI (GenAI) most often enters through visual representation, raising the risk we term the visualization trap: images can appear resolved before their tectonic implications are worked out. This exploratory study examines GenAI participation through protocol analysis of the documented process traces of 18 students in a single, AI-aware bioclimatic design studio, yielding 1107 protocols. The TRACE-SC methodology maps space-constituting components, GenAI use types, design phases, and cognitive breaking points; reliability was examined through a blind expert coding audit and cross-LLM comparison. GenAI appeared in 44.2% of protocols (489/1107), where visual generation and information gathering accounted for 81.2% (397/489). Suggestions were transformed before use in 68.1% (333/489) and adopted verbatim in 0.4% (2/489); interaction was designer-initiated. Cognitive breaking points appeared in 2.2% of protocols (24/1107), with indirect evidence in 70.8% (17/24). GenAI proves more than a visual production tool, but its engagement is limited, episodic, and designer-steered rather than a routine design partnership—partial support for the proposition. The transferable contribution is the TRACE-SC framework and codebook; its patterns describe this studio and invite comparison elsewhere.