2026 ASEE Annual Conference & Exposition

WIP: Evaluating a Course-Specific AI Assistant to Support Learning in an Electromagnetics Course

Presented at Electrical and Computer Engineering Division (ECE) Technical Session 2

This work-in-progress (WIP) paper examines how different Artificial intelligence (AI) tool instruction strategies influence undergraduate electrical and computer engineering (ECE) students’ learning experience. The study was conducted across two undergraduate ECE courses at a large R1 university: a junior-level electromagnetics course and a sophomore-level signal processing course. In the junior-level course, a course-specific AI assistant was embedded into the course Learning Management System (LMS). It was trained exclusively using course materials, including the syllabus, lecture slides, homework sets, and lecture recordings, etc. The tool was intentionally designed to support conceptual understanding and course concept navigation while limiting step-by-step solution generation. In contrast, the sophomore-level course framed AI as a general-purpose problem-solving tool, emphasizing output verification and reflective evaluation rather than course-specific guidance.
At the end of the Spring 2025 semester, students in both courses completed an extra credit assignment and a survey to reflect on their experience with AI tools. The survey measured students’ opinions on the AI tool usability, helpfulness, accuracy, etc. Based on the survey result, more frequent AI use was moderately associated with more positive learning-related perceptions across both courses. Different AI integration strategies didn’t affect students’ learning experience with statistical significance. Also, results indicated that instructional emphasis on critical evaluation may shape how students perceive responsible AI use.

Authors

Prof. Yang Victoria Shao University of Illinois at Urbana - Champaign [biography]

Yang V. Shao is a Teaching Associate Professor in electrical and computer engineering department at University of Illinois Urbana-Champaign (UIUC). She earned her Ph.D. degrees in electrical engineering from Chinese Academy of Sciences, China. She has worked with University of New Mexico before joining UIUC where she developed some graduate courses on Electromagnetics. Dr. Shao has research interests in curriculum development, assessment, student retention and student success in engineering, developing innovative ways of merging engineering fundamentals and research applications.
Juan Alvarez University of Illinois at Urbana - Champaign [biography]

Juan Alvarez joined the Department of Electrical and Computer Engineering at University of Illinois faculty in Spring 2011 and is currently a Teaching Assistant Professor. Prior to that, he was a Postdoctoral Fellow in the Department of Mathematics and Statistics at York University, Canada, a Postdoctoral Fellow in the Chemical Physics Theory Group at the University of Toronto, Canada, and a Postdoctoral Fellow in the Department of Mathematics and Statistics at the University of Saskatchewan. He obtained his Ph.D. and M.S. from the Department of Electrical and Computer Engineering at the University of Illinois in 2004 and 2002, respectively. He teaches courses in communications, signal processing and probability.
Prof. Olga Mironenko http://orcid.org/0000-0002-1239-185X University of Illinois at Urbana - Champaign [biography]

Dr. Olga Mironenko is a Teaching Assistant Professor with the Department of Electrical and Computer Engineering at University of Illinois Urbana-Champaign. She received a specialist degree in Physics from Omsk F.M. Dostoevsky State University, Russia in 2009, and she received a Ph.D. degree in Electrical and Computer Engineering from University of Delaware in 2020. Her current interests include improvement of introductory analog signal processing and power systems courses, training for graduate teaching assistants, and mentoring of under-represented students in ECE.
Prof. Xu Chen Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign [biography]

Xu Chen is a Teaching Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. He received his Ph.D. in Electrical and Computer Engineering from Illinois in 2018. Before returning to academia, he spent eight years in industry, working with IBM Systems Group and Apple Inc., where he gained extensive experience in electronic systems design and validation. His research and teaching bridge academia and industry, focusing on uncertainty quantification and scientific machine learning for electronic design automation, with particular emphasis on modeling, optimization, and heterogeneous integration in microelectronic packaging and circuits.

Prof. Chen serves as an Associate Editor of the IEEE Transactions on Components, Packaging, and Manufacturing Technology and as the General Chair of the 2026 IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS). He is a faculty researcher with the Center for Advanced Electronics through Machine Learning (CAEML), where his work advances the integration of machine learning into electronic design automation and system modeling. Prof. Chen and his students have been recognized with the IEEE EDAPS Best Symposium Paper Award and the IEEE MTT-S NEMO Best Student Paper Award. He has authored or co-authored more than 30 peer-reviewed journal and conference papers, advised three M.S. students - now active in the hardware design industry - and currently mentors two Ph.D. students. In addition, he has served on multiple Ph.D. dissertation and qualifying examination committees. He is a Senior Member of IEEE and a Member of SIAM.

Prof. Chen teaches a wide range of undergraduate and graduate courses across the electrical engineering curriculum, including engineering electromagnetics, probability, electronic circuits, microwave engineering, and analog signal processing. He is a dedicated and effective educator who has been recognized multiple times on the University of Illinois's List of Teachers Ranked as Excellent by Their Students. Prof. Chen has mentored more than ten undergraduate researchers, many of whom have gone on to pursue graduate studies at top-tier institutions. Through his teaching and mentorship, he has fostered a strong culture of intellectual curiosity and practical problem-solving in both classroom and research settings.

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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