Constructing a Case Teaching Model for Packaging Design Courses in the Era of Generative AI

Abstract

Generative artificial intelligence (GAI) is transforming design education by reshaping how design knowledge is generated, explored, and applied. While previous studies have primarily examined AI-assisted creativity and curriculum innovation, comparatively limited attention has been devoted to reconstructing case teaching for AI-supported learning. Addressing this gap, this study proposes the Generation–Analysis–Iteration–Reflection (GAIR) pedagogical model for packaging design education. Drawing upon literature on generative AI, case-based learning, and design pedagogy, the study first analyses the challenges that AI presents for traditional case teaching before developing a process-oriented pedagogical framework centred on human–AI collaboration. The GAIR model reconceptualises design cases as evolving pedagogical resources that accompany the entire learning process rather than static instructional examples. Through the four interconnected stages of Generation, Analysis, Iteration, and Reflection, students progressively develop creative exploration, design reasoning, design optimisation, and design judgement. The study contributes to the literature by extending case-based learning to AI-assisted design education and provides a practical framework for curriculum design, teacher facilitation, and formative assessment in packaging design courses. The proposed model also offers a theoretical foundation for future empirical research on AI-assisted case teaching in studio-based design education.