2026 ASEE Annual Conference & Exposition

Improving Undergraduate STEM Education (IUSE) By Using Active Learning – IoT Fundamentals to AI Enhanced Edge Devices

Presented at NSF Grantees Poster Session II

The concept and implementation of Internet of Things (IoT) continues to make inroads into applications and products. During the last few years at Texas A&M University-Kingsville and Texas A&M University-Corpus Christi, both Minority Serving Institutions, a team developed exercises and materials related to IoT for use on two platforms: a Raspberry Pi IoT kit as well as a commercial IoT kit built around a BeagleBone Green microcontroller. The initial exercises introduced students to each of the platforms, various sensors, motors, cloud-based platforms such as Thingspeak and Xbee communications. Next student materials introduced using cloud-based AI processing for object detection in images using an application programming interface (API) to implement the cloud-based artificial intelligence (AI) functionality. Finally, the team has built upon the prior IoT and AI related materials to include using a YOLO machine learning algorithm for object detection running on an edge device, in this case a Raspberry Pi 5.

Over the course of the grant, the developed student materials progress from introductory materials and implementations, and continue to more advanced applications and functionality, such as including machine learning and other AI algorithms for processing or object recognition. Through these developed exercises, students will gain experience with IoT that they can then utilize in their senior design projects. As AI and IoT continue to become more ubiquitous in commercial products, it becomes more imperative to introduce students to these concepts before graduation.

Authors

Dr. Lifford McLauchlan Texas A&M University - Kingsville [biography]

Dr. Lifford McLauchlan is an Associate Professor in the Electrical Engineering and Computer Science Department at Texas A&M University - Kingsville, and has also worked for Raytheon, Microvision, AT&T Bell Labs, and as an ONR Distinguished Summer Faculty at SPAWAR San Diego, CA. He has over 85 publications covering areas such as IoT, adaptive and intelligent controls, robotics, an ocean wave energy converter, green technology, education, wireless sensor networks and image processing. He is a co-inventor on 3 US patents related to control systems. Dr. McLauchlan is a senior member of ASEE and is the current Program Chair for the Ocean and Marine Engineering Division (OMED); in prior years, he was an officer for the OMED as well, including 2012-2014 Chair, 2014-2016 Past Chair and 2023-2025 Secretary/Treasurer. He is also a senior member of IEEE, and a member of SPIE, Eta Kappa Nu, ACES and Tau Beta Pi, and has served on the IEEE Corpus Christi Section Board in various capacities such as Chair, Vice Chair, Secretary, Treasurer and Membership Development Officer. Dr. McLauchlan has received the Dean’s Distinguished Service Award twice and the Dean’s Outstanding Teaching Award once for the College of Engineering at Texas A&M University-Kingsville.
Dr. David Hicks [biography]

David Hicks is an Associate Professor in the Electrical Engineering and Computer Science Department at Texas A&M University-Kingsville. Before joining TAMUK he served as Associate Professor and Department Head at Aalborg University in Esbjerg, Denmark. He has also held positions in research labs in the U.S. as well as Europe, and spent time as a research scientist in the software industry.
Dr. Mehrube Mehrubeoglu http://orcid.org/https://0000-0002-5927-8408 Texas A&M University - Corpus Christi [biography]

Dr. Mehrubeoglu received her B.S. degree in Electrical Engineering from The University of Texas at Austin. She earned an M.S. degree in Bioengineering and Ph.D. degree in Electrical Engineering from Texas A&M University. She is a professor of Electrical Engineering at Texas A&M University-Corpus Christi currently serving as the Program Coordinator for BS in Electrical Engineering, and MS in Engineering programs. Her research interests are multidisciplinary in nature and entail applications of imaging modalities (hyperspectral, thermal, color) for image segmentation and object classification using classical image processing and machine learning/deep learning models. She is also interested in engaged student learning using engineering pedagogies, advanced technologies such as IoT and the cloud, with consideration of sustainable design and professional skills.

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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