The Impact of Generative AI on Architectural Conceptual Design: Performance, Creative Self-Efficacy and Cognitive Load

TL;DR

This study investigates how generative AI affects architectural design performance, self-efficacy, and cognitive load, revealing benefits for novices but potential declines in confidence among users, with interaction strategies influencing outcomes.

Contribution

It provides empirical evidence on the differential impact of GenAI in architectural design, highlighting the importance of user expertise and prompting strategies.

Findings

GenAI improves design performance for novice users.

Self-efficacy declines with GenAI use.

Iterative prompts reduce cognitive load.

Abstract

Our study examines how generative AI (GenAI) influences performance, creative self-efficacy, and cognitive load in architectural conceptual design tasks. Thirty-six student participants from Architectural Engineering and other disciplines completed a two-phase architectural design task, first independently and then with external tools (GenAI-assisted condition and control condition using an online repository of existing architectural projects). Design outcomes were evaluated by expert raters, while self-efficacy and cognitive load were self-reported after each phase. Difference-in-differences analyses revealed no overall performance advantage of GenAI across participants; however, subgroup analyses showed that GenAI significantly improved design performance for novice designers. In contrast, general creative self-efficacy declined for students using GenAI. Cognitive load did not differ significantly between conditions, though prompt usage patterns showed that iterative idea generation and visual feedback prompts were linked to greater reductions in cognitive load. These findings suggest that GenAI effectiveness depends on users' prior expertise and interaction strategies through prompting.