An AI-Driven Framework for Teaching Industrial Design Students Using a Generate-and-Evaluate Approach Within the Double Diamond Model



Author Information

Hung-Hsiang Wang, National Taipei University of Technology, Taiwan
Shin-Bei Yu, National Taipei University of Technology, Taiwan

Abstract

As AI technologies increasingly influence the design industry, there is a growing need to equip design students with skills in generative AI (GenAI) and machine learning (ML) to stay competitive. This paper presents an AI-driven framework for teaching industrial design students using a generate-and-evaluate approach within the double diamond model. This framework integrates GenAI and ML to enhance creativity, analytical skills, and brand-focused thinking. Students first use Stable Diffusion, a GenAI model, to explore diverse car design concepts inspired by Porsche’s aesthetics, engaging in a divergent phase that expands creative possibilities. To manage the large volume of designs, students then apply classification models with Weka, an ML tools, in the convergent phase to filter and select options aligned with Porsche’s brand identity. This evaluation step teaches students to critically assess the relevance and consistency of AI-generated results, reinforcing the double diamond model’s balance between exploration and refinement. The framework offers key insights into design education. First, it bridges AI—including generative and ML classification—with traditional design processes, making AI more accessible to students. Second, the structured generate-and-evaluate approach aligns with students' natural design inclinations, fostering higher engagement and acceptance of AI as a valuable tool. Finally, the framework provides a scalable model for broader design education, adaptable across various design disciplines, supporting a technology-rich curriculum. By blending innovative technology with established design methods, this study highlights an effective approach to preparing students for industry demands through experiential learning with AI tools.


Paper Information

Conference: ACEID2025
Stream: Interdisciplinary

This paper is part of the ACEID2025 Conference Proceedings (View)
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To cite this article:
Wang H., & Yu S. (2025) An AI-Driven Framework for Teaching Industrial Design Students Using a Generate-and-Evaluate Approach Within the Double Diamond Model ISSN: 2189-101X – The Asian Conference on Education & International Development 2025 Official Conference Proceedings (pp. 355-363) https://doi.org/10.22492/issn.2189-101X.2025.29
To link to this article: https://doi.org/10.22492/issn.2189-101X.2025.29


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