Abstract
The integration of Artificial Intelligence (AI) into industrial design education offers transformative potential, particularly within the interdisciplinary context of STEM (Science, Technology, Engineering, and Mathematics) programs. This paper explores how AI-assisted, hypothesis-driven approaches can address cross-disciplinary challenges and enhance the iterative design process in STEM-related design projects. Using a controlled comparative study, we investigate the efficiency and interdisciplinary knowledge integration enabled by AI tools such as Large Language Models (LLMs). Our findings reveal that AI not only accelerates workflows by automating routine tasks but also fosters deeper exploration of technical and contextual knowledge. Participants leveraging AI demonstrated greater productivity, producing higher-quality design justifications and more iterations compared to control groups using traditional methods. AI’s ability to bridge knowledge gaps across disciplines underscores its role in rational decision-making, feasibility analysis, and interdisciplinary synthesis. However, the study also highlights the importance of AI literacy, emphasizing the need for critical validation and thoughtful interaction with AI outputs. By proposing an agile, AI-enabled hypothesis-driven framework, this research provides actionable insights into how AI can be integrated into STEM design education to empower students to move beyond conventional constraints. This approach promotes a ‘fail fast’ mindset, encouraging rapid hypothesis testing and iterative refinement while maintaining a balance between human creativity and AI efficiency. The findings contribute to the ongoing discourse on the pedagogical implications of AI in design education, offering strategies to harness its potential responsibly and effectively.
DOI
https://doi.org/10.21606/drslxd.2025.016
Citation
Sun, Y.(2025) Move Slow or Fail Fast? Enhancing STEM Design Education with AI-Assisted Iterative Workflows, in Clemente, V., Gomes, G., Reis, M., Félix, S., Ala, S., Jones, D. (eds.), Learn X Design 2025, 22-24 September 2025, Aveiro, Portugal. https://doi.org/10.21606/drslxd.2025.016
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Full Paper
Move Slow or Fail Fast? Enhancing STEM Design Education with AI-Assisted Iterative Workflows
The integration of Artificial Intelligence (AI) into industrial design education offers transformative potential, particularly within the interdisciplinary context of STEM (Science, Technology, Engineering, and Mathematics) programs. This paper explores how AI-assisted, hypothesis-driven approaches can address cross-disciplinary challenges and enhance the iterative design process in STEM-related design projects. Using a controlled comparative study, we investigate the efficiency and interdisciplinary knowledge integration enabled by AI tools such as Large Language Models (LLMs). Our findings reveal that AI not only accelerates workflows by automating routine tasks but also fosters deeper exploration of technical and contextual knowledge. Participants leveraging AI demonstrated greater productivity, producing higher-quality design justifications and more iterations compared to control groups using traditional methods. AI’s ability to bridge knowledge gaps across disciplines underscores its role in rational decision-making, feasibility analysis, and interdisciplinary synthesis. However, the study also highlights the importance of AI literacy, emphasizing the need for critical validation and thoughtful interaction with AI outputs. By proposing an agile, AI-enabled hypothesis-driven framework, this research provides actionable insights into how AI can be integrated into STEM design education to empower students to move beyond conventional constraints. This approach promotes a ‘fail fast’ mindset, encouraging rapid hypothesis testing and iterative refinement while maintaining a balance between human creativity and AI efficiency. The findings contribute to the ongoing discourse on the pedagogical implications of AI in design education, offering strategies to harness its potential responsibly and effectively.