2026 ASEE Annual Conference & Exposition

Teaching Human-Machine Teaming using Cyber-Physical Systems

Presented at CIT Technical Session 9: AI and Machine Learning Applications.

In the era of generative artificial intelligence (GenAI), students must develop foundational human–machine teaming (HMT) skills early in their academic careers in order to apply them effectively across subsequent coursework and professional contexts. This paper presents an instructional approach for developing such skills across all academic majors through project-based learning centered on microcontrollers and cyber-physical systems (CPSs). Prior to the advent of GenAI, the steep technical learning curve associated with microcontroller-based projects often deterred non-engineering students; however, GenAI has lowered this learning barrier, creating new opportunities for hands-on instruction in HMT principles. CPS-based projects proved effective for teaching HMT because they require deliberate problem solving and hardware–software integration, which in turn necessitate substantive collaboration with GenAI and reinforce key HMT tenets. Assessment results from the third iteration of a pilot course indicate increased confidence in experimenting with technology and practicing HMT skills; additionally, the results reveal comparable technical proficiency between STEM and non-STEM majors. Together, these results suggest that CPS-centered projects provide an effective and scalable pathway for introducing HMT concepts early in the curriculum.

Authors

Col. Christopher Lowrance http://orcid.org/0000-0002-0506-2820 United States Military Academy [biography]

Colonel Christopher J. Lowrance is an Associate Professor and the Deputy Head of the Electrical Engineering and Computer Science Department at the United States Military Academy in West Point, NY.
Prof. Jean R.S. Blair http://orcid.org/0000-0001-7176-6730 United States Military Academy
Devon Callahan United States Military Academy [biography]

LTC Devon Callahan is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the United States Military Academy, West Point. He teaches across the computer and cyber science curriculum, including Cyber Foundations (CY305), Programming Fundamentals (CY300), Network Engineering and Management (CY350), Introduction to Computing and Information Technology (IT105), Database Systems (CS393), and Advanced Networks (CS484), with a focus on software-defined networks.

He holds a Ph.D. in Computer Science from the University of Connecticut, a Master of Engineering in Computer Science from the University of South Carolina, and a Bachelor of Science in Computer Science from Methodist University. His research interests include artificial intelligence and machine learning, cybersecurity, network optimization, systems design, and integration and testing. His work emphasizes the application of network science to both computer and social networks to enhance system performance and resilience.

LTC Callahan began his military career as a Signal Soldier, enlisting with the 10th Mountain Division at Fort Drum, New York, and rising to the rank of Staff Sergeant. Commissioned through Officer Candidate School, he has served in multiple leadership positions across the Signal Corps. His previous assignments include Platoon Leader and Executive Officer in the 327th Signal Battalion at Fort Bragg, North Carolina; Division G6 staff officer and Company Commander, Bravo Company, 10th Sustainment Brigade, both with the 10th Mountain Division; and Signal Advisor to the Royal Saudi Air Defense Force under the U.S. Military Training Mission Task Force. He also commanded the Satellite Communications Station at Camp Roberts, California.
Lt. Col. Christa M Chewar United States Military Academy
Michael Chiu United States Military Academy
Brian Scott Petty United States Military Academy
Edward Sobiesk United States Military Academy

Download paper (2.63 MB)

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.

» Download paper

« View session