Generative AI as a Catalyst for Deeper Design Skills in Undergraduate Engineering
TL;DR
Current literature on AI integration into design courses is reviewed, capturing best practices and proposals for AI scaffolding, and pedagogical framing, specific activities, student guidelines, and instructor observations of engagement patterns across year levels are described.
Abstract
Generative AI is transforming how engineers conceptualize and evaluate design ideas, presenting both opportunities and risks for engineering education. This practice-based paper reviews current literature on AI integration into design courses, capturing best practices and proposals for AI scaffolding. It then describes practical experiences integrating conversational AI into undergraduate engineering design courses at the University of Ottawa, from first-year introductory design through fourth-year capstone projects. AI tools were introduced incrementally, supporting ideation, scenario exploration, critique of alternatives, and structured feedback, alongside instruction in prompt design, critical evaluation, and ethical considerations. The paper describes pedagogical framing, specific activities, student guidelines, and instructor observations of engagement patterns across year levels. Faculty observed that early reluctance and superficial use gave way to broader design exploration and more precise design rationale with sustained, guided practice. Challenges include managing expectations, variation in prompting skill, and maintaining student agency.