2026 ASEE Annual Conference & Exposition

This Work in Progress paper presents a pilot study examining how engineering faculty, students, and industry practitioners perceive AI utility and readiness. Engineering programs grapple with these questions and need this integrated understanding. Curricula must keep pace with workforce tools that integrate AI without sacrificing the disciplinary rigor that defines the profession. The workplace is signaling that demonstrated AI proficiency functions as a hiring criterion. Yet insufficient research examines whether faculty, students, and employers agree on what AI readiness means. We address this research gap by building and pilot testing a parallel survey suite, deploying one version each for engineering faculty, undergraduate students, and industry practitioners across engineering disciplines, institutions, and workplace settings.
Our survey suite draws on the Technology Acceptance Model, an AI taxonomy aligned with U.S. federal standards based on capabilities, and the UNESCO AI competency framework for teachers. By integrating these established foundations, we create a diverse framework to measure perceptions of technical skill, applied competence, and ethical judgment. The survey instruments are structured for analysis of variance (ANOVA) to detect significant perceptual gaps across survey stakeholder groups along with other analyses made possible by the survey constructs. Semi-structured interviews with a subset of participants probe the reasons behind any divergence.
Preliminary analysis of our pilot data shows that all three stakeholder groups are close in their rating of perceived value of AI and their intent to use it, but their ratings diverge on proper usage and readiness assessments. Practitioners rate graduate’s AI readiness significantly below students' self-assessments. One qualitative finding from interviews came forward. The perceived "readiness gap" between academic preparation and industry expectation may be less a skills deficit than a "translation gap" rooted in differing terminologies and contextual priorities. Industry participants consistently value deep understanding of engineering fundamentals over proficiency with specific AI software, which they view as transient. We define translational skills herein as the competencies required to bridge theoretical AI knowledge and its practical, responsible application within engineering workflows. These include selecting appropriate tools, verifying outputs against required standards, and communicating AI-assisted decisions to stakeholders.
These preliminary findings suggest that engineering programs should develop shared AI-Engineering Technology lexicons with industry partners and treat ethical reasoning as a core competency that spans disciplines. The consistent priority placed on fundamentals indicates that curricula should teach durable AI principles instead of training students on specific tools. This study's sample is concentrated in the U.S. Midwest, yet it provides a foundation for wider dialogue on aligning engineering education with the AI readiness demands of the engineering profession.