2026 ASEE Annual Conference & Exposition

Using Machine Learning Techniques to Develop Attitudinal Surveys on Generative AI Tool Adoption in the Engineering Classroom

Presented at DSAI-Session 4: Ethics, Policy, and the AI-Integrated Engineering Workforce

As faculty, administrators, and students are bombarded with polarizing opinions regarding the use of Generative AI (GenAI) tools in higher education, it is important to explore the perceptions of these constituencies and to examine their readiness to embrace AI technologies throughout their studies. In preparation for a future study on this topic, the authors conducted an analysis of course-level and department-level policies relating to the use of AI in the classroom using manual coding and a GPT tool. The analysis provided an early thematic map of AI policies or positions, and laid the groundwork for future survey instrument design. Manual analysis of the sampled departmental policies and course syllabi indicated that a large majority of policies allowed some level of AI use. The analysis validated the challenges that students and instructors face with sensemaking in a rapid changing environment related to the use of GenAI in higher education. The long-term result of this effort will be an analysis of the influence of course policies on prevailing attitudes and the adoption of artificial intelligence in academic practices at institutions with notably strong honor codes addressing academic integrity and personal responsibility, based on a future constituent survey informed by the results of this exploration.

Authors

Dr. David M. Feinauer P.E. Virginia Military Institute [biography]

Dr. Feinauer is a Professor of Electrical and Computer Engineering at Virginia Military Institute. His scholarly work spans a number of areas related to engineering education, including the first-year engineering experience, incorporating innovation and entrepreneurship practice in the engineering classroom, and
P-12 engineering outreach. Additionally, he has research experience in the areas of automation and control theory, system identification, machine learning, and energy resilience. He holds a PhD and BS in Electrical Engineering from the University of Kentucky.
Dr. Michael Cross Norwich University [biography]

Michael Cross, Ph.D., is Chair and Associate Professor of Electrical & Computer Engineering at Norwich University. He teaches classes in first-year engineering, circuits, electronics, and energy systems. His research interests are in battery electric vehicles, autonomous vehicles, and grid-connected energy systems. Cross received his B.S. in Physics from the Rochester Institute of Technology and his M.S. and Ph.D. in Materials Science from the University of Vermont, where he investigated semiconductor thin films and nanomaterials for sustainable energy systems. As a development engineer at IBM Microelectronics, he contributed to the development of advanced semiconductor technologies. He is also the Director of the Governor’s Institutes of Vermont Engineering Institute, promoting STEM outreach and workforce development in the state.
Kundan P. Kushwaha
Ali Al Bataineh Norwich University

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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