2026 ASEE Annual Conference & Exposition

This complete paper discusses how Generative AI (Gen AI) is poised as a disruptive tool for industry and a challenge for education. Academic institutions are issuing policies concerning student use of GenAI, yet student perspectives on use, value, and risk remain unprobed. If GenAI is to effectively join the ranks of routine engineering tools like Finite Element Analysis, Computer Aided Design, and Computational Fluid Dynamics, its adoption will require a more active pedagogical role than mere permission or prohibition through policy.

Using Rogers’ diffusion of innovations, we track first-year engineering students as they move along the five perceived attributes that shape adoption: relative advantage, compatibility, complexity, trialability, and observability. Early results from a survey administered in the third week of a Introduction to engineering course in a large, southeastern R1 university show that students claim relative advantage (speed, convenience) and basic operational know-how with respect to GenAI tools, but report ambiguous compatibility with academic ethical norms and uneven institutional support that makes competent use feel uncertain. In most prior courses, sanctioned opportunities to try GenAI and to see its use in practice have likely been scarce, leaving gaps in trialability and observability and, in turn, dampening diffusion.

We examine how these perceptions evolve across a first-year general engineering course in which GenAI use is structured, scaffolded, and required. By situating classroom activities as institutional supports that explicitly increase trialability and observability, we explore how students’ reported understanding develops with respect competence and ethical practice. The contribution of this work is to provide a baseline of student views at the point of where institutional policy is under development, and to provide evidence on how targeted scaffolding and organizational support shift the perceived attributes about GenAI’s diffusion in early engineering education.