2026 ASEE Annual Conference & Exposition

This paper will be submitted as a Research study.
Artificial Intelligence (AI) has moved from the periphery of engineering practice to its core,
rapidly transforming how industries design, optimize, and deliver technological solutions. This
shift has created an urgent demand for graduates equipped not only with technical proficiency in
AI tools and methods, but also with the ethical reasoning, adaptability, and systems-level
thinking necessary to thrive in AI-augmented workplaces. While policy and industry discourse
now frame AI competence as essential to national competitiveness-underscored by federal
investments through the National Science Foundation (NSF), the Department of Energy, and the
White House Office of Science and Technology Policy-the academic response remains
uneven. Within engineering education, there is wide variation in how faculty conceptualize AI’s
role in the curriculum and, critically, how they perceive their own responsibility in preparing
students for this future. These tensions highlight a growing gap between the accelerating
evolution of AI in industry and the slower, often uncertain pace of its integration into engineering
education. Addressing this gap requires understanding not only institutional readiness but also
faculty perceptions, as faculty serve as the critical link between innovation and student
preparation. Guided by Diffusion of Innovations theory (Rogers, 2003), this study examines how
engineering faculty perceive their role in positioning students for the AI-driven workforce and
how they interpret the promise and risks of AI education. The framework conceptualizes
adoption as a social process influenced by perceived usefulness, compatibility, institutional
culture, and professional identity, factors particularly salient within engineering disciplines
characterized by deeply entrenched pedagogical norms.
To explore these dynamics, the study employed a qualitative research design using semi-
structured interviews with five engineering faculty members across multiple subdisciplines.
Participants reflected on their awareness of AI’s relevance to engineering practice, their
pedagogical experiences, adoptions, and reservations, and their perceptions of institutional
support. Interviews were audio- and video-recorded, transcribed verbatim, and analyzed using
Reflexive Thematic Analysis (Braun & Clarke, 2019). This analytic approach facilitated an
iterative process of meaning-making that foregrounded the researcher’s reflexivity and
acknowledged the co-construction of knowledge between participant and analyst.
Preliminary findings reveal a set of recurring tensions shaping faculty engagement with AI: (1)
enthusiasm for innovation tempered by uncertainty about ethical and curricular boundaries; and
(2) perceptions that responsibility for AI workforce preparation lies ambiguously between
individual initiative and institutional mandate. These tensions underscore the need for faculty
development initiatives that move beyond technical training to address AI pedagogical
readiness, which includes considerations of conceptual understanding, ethical fluency, and
curricular integration strategies. Ultimately, this work contributes to the growing discourse on AI
in engineering education by situating faculty not as passive adopters of innovation but as active
agents shaping the future of AI literacy and workforce preparedness. The findings illuminate
opportunities for universities to invest in sustained professional development that empowers
educators to engage critically and creatively with AI.
The preferred paper presentation format is a Talk.