2026 ASEE Annual Conference & Exposition

The rapid emergence of generative artificial intelligence (AI) in higher education has reshaped how engineering faculty approach design-focused courses, where iteration, creativity, and ethical reasoning are central. Although institutional and national discussions about AI policy are expanding, scholars have not thoroughly explored how engineering design instructors, specifically, are enacting classroom-level policies.
This paper addresses that gap by presenting findings from 17 semi-structured interviews with engineering faculty from varied US institutional contexts. The study focuses on how instructors interpret and implement AI-related policies, and the implications of these choices for student learning, in addition to academic integrity and professional preparation. We address the following research questions: “What guidelines and policies do design instructors think should (and should not) be included in design courses or projects?” and “How can these guidelines and policies be organized into guiding paradigms?”
Using thematic analysis, we identified several recurring patterns which we then grouped into four “paradigms,” or shared frameworks of assumptions, values, and approaches to AI integration in design teaching and learning. The four paradigms include Restrictive Use (limiting AI usage to human production alone), Ethical Use (prompting ethical and responsible AI usage), Guided Use (scaffolding AI usage), and Pedagogical Alignment (ensuring AI use aligns with sought objectives). We present paradigms discretely with exemplary quotes, but we recognize that instructors in our study enacted distinct paradigms based on varying instructional goals and contexts. In our discussion, we consider the interrelation between paradigms and offer an organizing model regarding how paradigms inform each other.
The paradigms and policies we identify in this study provide a conceptual framework to guide instructional approaches to AI use in engineering and engineering design curricula. This includes evidence-based insights for faculty, administrators, and policymakers seeking to develop AI policies aligned with instructional goals and towards the cultivation of critical and ethical capacities that engineers need to responsibly engage with AI in professional practice.