Montreal, Quebec, Canada
June 22, 2025
June 22, 2025
August 15, 2025
ME Division Technical Session 2 - Harnessing AI and Machine Learning to Transform ME Education
Mechanical Engineering Division (MECH)
Diversity
21
10.18260/1-2--56818
https://peer.asee.org/56818
12
Assistant Professor in Department of Mechanical Engineering Technology, Farmingdale State College, Farmingdale, NY 11735
Dr. Yue (Jeff) Hung holds degrees in engineering and technology disciplines (Ph.D. in Materials Science and Engineering, M.S in Mechanical Engineering, and B.S in Manufacturing Engineering Technology). He has over 20 years’ experience in Computer-Aided
Dr. Gonca Altuger-Genc is an Associate Professor at State University of New York - Farmingdale State College in the Mechanical Engineering Technology Department.
Sen Zhang has been teaching Computer Science at SUNY Oneonta since 2004. The recent courses he has been teaching include Python, Artificial Intelligence, Intro to Machine Learning as special topics, Intro to Robotics, Internet Programming, Linux, and Software Design and Development (which typically contains a sizable team term-project as capstone experience). He publishes on data mining algorithms, conducts educational research and values project-based learning.
Akin Tatoglu is an Assistant Professor of Mechanical Engineering at University of Hartford, CT. He received his Ph.D. degree in mechanical engineering from Stevens Institute of Technology, NJ, in 2015. His research focuses on robotics, collaborative navig
I am an Assistant Professor at SUNY Farmingdale State College. My teaching and research interests include robotics and virtual reality in engineering education. I have a Ph.D. and a bachelor's degree in Mechanical Engineering, and my master's degree is in Electrical Engineering. I have over seven years of industrial experience as an electrical and mechanical engineer. I also have extensive teaching and research experience with respect to various interdisciplinary topics involving Mechanical Engineering, Electrical Engineering, and Computer Science.
This study explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) education into the Senior Projects course for Mechanical Engineering Technology (MET) students, addressing the growing demand for AI/ML skills in engineering fields. In the absence of a dedicated AI/ML course within the current MET curriculum, the initiative bridges this gap through a dual approach: weekly lectures tailored to MET students, focusing on accessible tools and practical applications, and senior projects specifically designed to apply AI/ML concepts to solve engineering problems. A comprehensive assessment plan, incorporating pre- and post-course identical quizzes, topics-specific quizzes, self-evaluations and reflections, demonstrated significant learning gains. The successful completion of these AI-focused senior projects highlights the effectiveness of this approach in equipping students with essential AI/ML skills. This innovative strategy not only addresses the curriculum gap but also offers a scalable model for integrating emerging technologies into undergraduate engineering education.
Li, W., & Hung, Y., & Altuger-Genc, G., & Zhang, S., & Tatoglu, A., & Zhang, Z. (2025, June), Integrating AI/ML Learning in Senior Projects for Mechanical Engineering Technology Students Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--56818
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