Penn State aerospace engineering faculty Mark Maughmer (left) and James Coder (right), who earned two national ASEE awards for research on ChatGPT’s role in engineering education, pose for headshots. Credit: Penn State College of Engineering directory.
July 27, 2026
UNIVERSITY PARK, Pa. — Associate Professor James Coder and Professor Mark Maughmer, both faculty members in the Department of Aerospace Engineering, and doctoral candidate in the College of Education Julie Coder were co-authors of a paper that was recognized at the 2026 American Society for Engineering Education (ASEE) Annual Conference with both the Best Paper Award in the Aerospace Division and the Best Professional Interest Council (PIC) V Paper Award.
The paper, “Evaluating Generative AI-Assisted Studying in an Undergraduate Aeronautics Course,” was honored for its contributions to engineering education and was selected from papers presented at the annual conference, which brings together engineering educators and researchers from around the world to share innovations in teaching, learning and curriculum development.
The Best Paper Award recognizes outstanding scholarly contributions within ASEE’s Aerospace Division, while the Best PIC V Paper Award honors exceptional papers presented within Professional Interest Council V, which focuses on advancing engineering education through innovative instructional approaches and educational research.
The research explored how generative AI tools like ChatGPT can support students studying challenging aerospace engineering concepts. The researchers analyzed more than 175 interactions between ChatGPT and a simulated undergraduate learner to assess both the technical accuracy of the responses and whether the AI encouraged meaningful learning.
While ChatGPT often provided clear, accessible explanations, the study found that it sometimes favored quick answers over deeper conceptual understanding. The AI frequently overlooked important visual and mathematical elements, offered overly reassuring feedback that could give students false confidence and generated practice materials that lacked the precision needed for complex engineering coursework.
“The findings highlight the need for a more thoughtful approach to integrating generative AI in engineering education,” Coder said. “Rather than replacing teaching assistants, tutoring or other learning supports, AI tools should be used with guidance that helps students critically evaluate their outputs.”