Abstract
This study investigates how Industrial Design (ID) programs can effectively adopt and integrate Generative Artificial Intelligence (Gen-AI) into curricula, while respecting the well-established ID workflow and preserving the core ID values of originality and creativity. Through multi-perspective qualitative research (including 7 ID faculty members, 6 industry professionals, and over 70 ID students), this study maps current AI tools, roles, and use cases within the ID workflow, identifies key pedagogical challenges, and proposes strategies that bridge foundational design traditions with emerging AI opportunities. Findings introduces a progressive learning trajectory, supported by innovative teaching practices that cultivate an AI-collaborative design mindset. By emphasizing critical thinking, ethical reflection, and responsible use, this study positions AI as a complement to design heritage, enriching both ID curricula development and future career pathways for industrial designers.
Keywords
Industrial Design, Human-AI Collaboration, Pedagogy Design, Curriculum Innovation
DOI
https://doi.org/10.21606/drs.2026.2331
Citation
Tian, Y., and Kim, B. (2026) Reimagining ID education with AI: Perceptions, practices, and pedagogical implications of AI adoption, in Simeone, L., Gray, C. M., Verhoeven, A., de Götzen, A., Bakırlıoğlu, Y., Zohar, H., Stead, M., and Buwert, P. (eds.), DRS2026: Edinburgh, 8–12 June, Edinburgh, United Kingdom. https://doi.org/10.21606/drs.2026.2331
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Included in
Reimagining ID education with AI: Perceptions, practices, and pedagogical implications of AI adoption
This study investigates how Industrial Design (ID) programs can effectively adopt and integrate Generative Artificial Intelligence (Gen-AI) into curricula, while respecting the well-established ID workflow and preserving the core ID values of originality and creativity. Through multi-perspective qualitative research (including 7 ID faculty members, 6 industry professionals, and over 70 ID students), this study maps current AI tools, roles, and use cases within the ID workflow, identifies key pedagogical challenges, and proposes strategies that bridge foundational design traditions with emerging AI opportunities. Findings introduces a progressive learning trajectory, supported by innovative teaching practices that cultivate an AI-collaborative design mindset. By emphasizing critical thinking, ethical reflection, and responsible use, this study positions AI as a complement to design heritage, enriching both ID curricula development and future career pathways for industrial designers.