Pedagogy and Professional Development

Enhancing Fashion Education Through AI: Evaluating the Impact of Generative AI Critiques on Student Learning and Engagement

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Abstract

This study examines the role of generative artificial intelligence (GAI) in critique-based learning within fashion education, comparing AI-generated feedback with traditional peer critique through the lens of experiential learning theory (ELT). The research was conducted across multiple fashion courses using a three-step critique activity that included peer critique, GAI critique using ChatGPT, and reflective assessment. Quantitative and qualitative data were analyzed across the four stages of ELT. Findings indicate that GAI critiques were perceived as clear, structured, and efficient, particularly supporting reflective observation and abstract conceptualization. However, students viewed peer critiques as more personal and contextually nuanced, especially when evaluating creativity and artistic intent. While GAI was not seen as a replacement for human feedback, students expressed strong intentions to use GAI critiques in future design work. Overall, the study highlights GAI as a valuable technology-enhanced learning tool that complements peer critique in fashion education.

Keywords: generative artificial intelligence, fashion education, peer critique, experiential learning theory, technology-enhanced learning

How to Cite:

Cho, H. & Palmer, J., (2025) “Enhancing Fashion Education Through AI: Evaluating the Impact of Generative AI Critiques on Student Learning and Engagement”, International Textile and Apparel Association Annual Conference Proceedings 82(1). doi: https://doi.org/10.31274/itaa.21946