AI as Pedagogy in Design Education: Can GenAI-Integrated Teaching Achieve Course Learning Goals in the Short Term?

Author Information

Jinmei Huang, Guangzhou College of Commerce, China
Henry Chi Fai Ma, The Hong Kong Polytechnic University, China

Abstract

Generative Artificial Intelligence (GenAI) is increasingly embedded in design courses, not only as a production aid but also as a pedagogical approach that can reshape how students work through creative tasks. However, it remains unclear whether GenAI-integrated teaching can help students meet core learning requirements typically associated with studio-based design education within a compressed instructional period, and which difficulties may persist. This study investigates a four-week, 32-contact-hour undergraduate design course that integrated GenAI into creative assignments, using pre- and post-course surveys supplemented by written student reflections. The surveys captured student-reported indicators of perceived efficiency and task progression, perceived improvement in creative output quality, self-efficacy and controllability when using GenAI, as well as process indicators such as iteration behaviors and actions taken after initial AI outputs; they also documented commonly encountered difficulties and coping strategies. Descriptive and comparative analyses were used to summarize changes from pre to post, and reflections were thematically reviewed to contextualize and explain observed patterns. The findings suggest partial alignment with course learning requirements over the short period: students commonly reported faster progress and more deliberate prompting and iteration practices, while persistent challenges remained in controllability, output consistency, and integrating AI results into coherent design solutions. These patterns indicate that GenAI may reduce difficulty in generating options but foreground new demands in steering, refining, and synthesizing outputs into design decisions. The study discusses implications for GenAI-integrated pedagogy, including explicitly teaching control and integration strategies and evaluating learning processes alongside final artifacts.

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