2026 ASEE Annual Conference & Exposition

Teaching with (and About) AI: A Convergence–Divergence Framework for Engineering Education

Presented at CIT Technical Session 12: Foundations and Emerging Topics.

The use of generative artificial intelligence (AI) has outpaced pedagogical guidance in engineering education. This paper reports on the work of a Faculty Learning Community (FLC) at Worcester Polytechnic Institute (WPI), composed of faculty from systems engineering, data science, business, economics, political science, geography and global studies, who are experimenting with teaching AI through a humanistic lens. We propose a convergence–divergence framework for integrating AI into STEM education. The convergence (core) defines common outcomes across disciplines: developing AI literacy (effective prompting, critique, and awareness of limits), raising ethical and risk awareness, and situating projects within governance and standards frameworks (e.g., NIST AI Risk Management). The divergence (petals) reflects discipline-specific applications and challenges.

Vignettes illustrate this framework in practice: systems engineering capstone teams are required to embed AI in design revealed unexamined ethical risks in concept-of-operations work; business/data science students used AI to support blockchain-based data tokenization while checking compliance against ISO/NIST/IEEE standards; social science students explored the societal benefits and risks of AI through in-class writing and design-for-change exercises; economics students tested AI on graph interpretation and simulated AI’s impact on labor markets; and global project-based learning leveraged an AI-driven avatar chatbot to surface multi-stakeholder perspectives.

Our contribution is threefold: (1) a portable framework for designing AI-infused project-based courses; (2) pedagogical strategies and rubrics linking classroom activities to governance/standards; and (3) lessons learned from multidisciplinary teaching contexts navigating uneven institutional AI policy. We conclude with implications for Interactive Qualifying Project and Major Qualifying Project (MQP/IQP) advising, industry readiness, and fostering ethically resilient design practices.

Authors

Dr. Crystal H. Brown http://orcid.org/0000-0003-2300-6774 Worcester Polytechnic Institute [biography]

Dr. Crystal H. Brown is an Assistant Professor of Political Science at Worcester Polytechnic Institute (WPI), where her work focuses on inclusivity, governance, public policy, and research methods. She is a computational social scientist whose interdisciplinary approach bridges political science, data analytics, and policy-relevant research. Dr. Brown is committed to advancing inclusive practices in both her scholarship and teaching. Her work emphasizes preparing students to engage with complex social and policy challenges.
Dr. Shamsnaz Virani Bhada http://orcid.org/0000-0001-9869-2137 Worcester Polytechnic Institute [biography]

Shamsnaz Virani Bhada (Senior Member, IEEE) received the Ph.D. degree from the Industrial and Systems Engineering Department at The University of Alabama, Huntsville, in 2008. She is currently an Assistant Professor in Systems Engineering with the Electrical and Computer Engineering Department in Worcester Polytechnic Institute (WPI). Her research interests include applying model-based systems engineering to safety analysis and policy modeling and digitization
Stephen McCauley Worcester Polytechnic Institute
Patricia C Agupusi Worcester Polytechnic Institute
Oleg V. Pavlov Worcester Polytechnic Institute
Daniel Narh Treku Worcester Polytechnic Institute
Raha Moraffah Worcester Polytechnic Institute

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 July 31, 2026

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