Guidance on Generative AI in instruction in the College of Engineering
Alan Zehnder, Alexandra Werth, Chris Schaffer, David Goldberg, Trystan Goetze and Kathryn Dimiduk
Spring 2026, Last update 1/7/2026
Introduction
Generative artificial intelligence (GenAI) is poised to change how engineers and other professionals learn, work and approach problem solving. It is advancing so rapidly that many of us are grappling with its implications for education and our own teaching, research and day-to-day life. While GenAI promises to accelerate learning by scaling personal support, by enabling timely feedback, enabling novel pedagogical approaches and generating practice questions and quizzes[1], “There are legitimate worries that AI systems may provide a crutch for students, stifling the development of foundational skills needed to support higher-order thinking.”[2]
GenAI is here and students are using it with or without our guidance or permission. The VPAI’s spring 2025 survey shows that about 90% of our students are using GenAI. In recent panel discussions, students have expressed the sentiment that “everyone is using GenAI”, with or without faculty guidance. We, as faculty, have a responsibility to help our students learn effective and ethical use of GenAI and there are now some great examples of creative use of GenAI to promote thinking and learning.
Generative AI encompasses a wide range of tools and technologies. What they have in common is that GenAI tools can produce material—text, audio, images, 3D models, computer code, and so on—that would ordinarily require sustained human work to create, often merely from a text prompt entered by the user. See https://teaching.cornell.edu/generative-artificial-intelligence#GenerativeAI for Cornell’s definition of GenAI.
Acknowledging that there is much we don’t know, this document is intended to provide base level guidance on principles, policy, suggestions and resources regarding the integration of GenAI in instruction in the College of Engineering.
Policy
Principles
Suggestions
Resources
[1] Yan, L., Greiff, S., Teuber, Z. et al. Promises and challenges of generative artificial intelligence for human learning. Nat Hum Behav 8, 1839–1850 (2024). https://doi.org/10.1038/s41562-024-02004-5
[2] https://www.anthropic.com/news/anthropic-education-report-how-university-students-use-claude, accessed 8/6/2025.