Co-Creating Fast Fashion with AIGC: Toward New Efficiencies and Creative Practices

The rapid development of AI-generated content (AIGC) is profoundly impacting fast fashion design processes. While existing research has largely emphasized AIGC’s role in enhancing efficiency and expanding the design space, there remains a lack of empirical analysis grounded in real-world contexts regarding how it influences creative decision-making, iterative mechanisms, and the role of designers in actual design processes. Drawing on a human–AI collaboration perspective, this study systematically examines the impact of AIGC on fast fashion design efficiency, iterative structures, and role reconfiguration through a comparative experiment, a questionnaire survey (N = 157), and semi-structured interviews (N = 10). The study found that AIGC reduces the marginal cost of the generation and revision of design alternatives, alters the exploration and convergence mechanisms in fast fashion front-end design, and shifts the design process from a linear progression to a structured model of “parallel generation of multiple alternatives–rapid comparison–iterative convergence.” However, it also increases the pressure to filter options and gives rise to potential risks such as stylistic convergence and algorithmic dependence. The results indicate that AIGC has not replaced designers but has instead driven a shift in their roles from direct producers of visual proposals to constraint setters, outcome evaluators, and style integrators. This study provides an empirically grounded account of how AIGC is embedded in professional fast fashion design workflows, offering empirical insights into human–AI collaboration, creative decision-making, and design process governance within high-speed design systems.