Generative AI and the Reordering of Design Creativity in Architectural Education: Evidence from Undergraduate Students

Generative AI has reached the early, ideational phase of architectural design, the stage most tightly bound to conceptual creativity, yet its effect on student creativity remains unevenly investigated. This study examines the transfer of creativity from handmade concept to AI refinement. In a first-year studio, 45 students developed a bus-stop design and then reworked it using AI visualisation tools. Pre-AI and post-AI designs were rated by experts using the same nine-dimensional Consensual Assessment Technique (CAT) rubric; the prompt log was text-mined; and the two streams were linked for each student and analysed. Results suggest a modest rise in total creativity (3.38 to 3.71; Wilcoxon p = 0.022, dz = 0.38), yet concentrated in appropriateness; functional adequacy (+0.80), structural feasibility (+0.78), and environmental response (+0.56) increased significantly, while novelty dimensions remained unaltered. The cohort suggested a reordering: weaker starters gained most and several strong starters declined. Although baseline input creativity dominated the prediction of output quality and prompt behaviour added no significant variance once it was controlled, gains were largest when students let AI transform their original concepts. Results indicate that in the context of this study, generative AI acts as a leveller and redirector of creativity, strengthening the buildability of weaker designs without raising originality, with direct implications for how studios deploy and teach the tool. As results are based on a single first-year cohort, institution, design brief, and an observational within-student design, the findings are limited to exploratory evidence of conditional creativity transfer rather than causal evidence.