2026 ASEE Annual Conference & Exposition

Toward Mastery Learning: A Conceptual Framework for AI-Supported Feedback in Undergraduate Engineering

Presented at The Intersection of AI and Methods for Research and Teaching

This theory full paper proposes a technology-assisted model for giving formative feedback to students. Feedback is a vital component of engineering teaching and learning because it helps students achieve the expected learning outcomes and develop the necessary knowledge and skills for their profession. However, there are certain limitations in both the existing literature on feedback models as well as instructors’ practice of giving feedback. For example, feedback typically positions learning as a discrete, short-term performance rather than a dynamic and developmental process. The limitations constrain the ability of feedback to truly support student learning and cultivate self-regulated learning (SRL) skills. Developed from extensive learning theories that have been verified by empirical studies, this paper aims to conceptualize how large language models (LLM) could provide mastery-oriented feedback to undergraduate engineering students to improve the effectiveness of their learning as well as enhance their SRL skills. Our proposed framework embeds structured opportunities for continued practice aligned with the underlying learning objectives, positioning the LLM as a support for sustained learning and mastery rather than short-term performance. Student improvement is facilitated through two complementary mechanisms: targeted feedback that supports sense-making and regulation during the task, and curated opportunities for additional practice that promote transfer and long-term development of competence.

Authors

Dr. Hong H. Tran Virginia Polytechnic Institute and State University [biography]

Hong H. Tran is a Research Scientist in the Department of Engineering Education and an Affiliate Faculty member of the Center for Educational Networks & Impacts at Virginia Tech. She taught high school science and math for more than ten years prior to earning her PhD in science education from the University of Georgia. Her primary research interests include teacher education, self-regulated learning, and science and engineering practices.
Dr. Andrew Katz Virginia Polytechnic Institute and State University [biography]

Andrew Katz is an assistant professor in the Department of Engineering Education at Virginia Tech. He leads the Improving Decisions in Engineering Education Agents and Systems (IDEEAS) Lab.
Dr. Edward J. Berger http://orcid.org/0000-0003-0337-7607 Virginia Polytechnic Institute and State University [biography]

Edward Berger is a Professor of Engineering Education and Mechanical Engineering at Purdue University, joining Purdue in August 2014. He has been teaching mechanics for over 20 years, and has worked extensively on the integration and assessment.
Anyerson Cuervo-Basurto Virginia Tech Department of Engineering Education [biography]

Anyerson Cuervo is a Ph.D. Engineering Education student at Virginia Tech. Anyerson has a background in mechanical engineering and a master’s degree in materials science. He has work experience in engineering roles at Colombian companies and has taught materials science and technical drawing courses at Colombian universities for four years. His current research interests include teacher identity in engineering faculties, cultural and institutional challenges in engineering teaching, and improvement of faculty development programs.

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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