Quantification of Competencies-based Curricula for Artificial Intelligence
- Conference
- Location
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Baltimore , Maryland
- Publication Date
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June 25, 2023
- Start Date
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June 25, 2023
- End Date
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June 28, 2023
- Conference Session
- Tagged Division
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Electrical and Computer Engineering Division (ECE)
- Tagged Topic
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Diversity
- Page Count
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13
- DOI
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10.18260/1-2--44004
- Permanent URL
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https://peer.asee.org/44004
- Download Count
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193
Paper Authors
Yufang Jin
The University of Texas at San Antonio
orcid.org/0000-0002-7421-527X
Dr. Yu-Fang Jin got her Ph.D. from the University of Central Florida in 2004. After her graduation, she joined the University of Texas at San Antonio (UTSA). Currently, she is a Professor at the Department of Electrical and Computer Engineering at UTSA. Her research interest focus on applications of artificial intelligence, interpretation of deep learning models, and engineering education.
Robert Applonie
The University of Texas at San Antonio
orcid.org/0000-0002-4020-4839
Robert Applonie received his M.S. in Electrical Engineering from The University of Texas at San Antonio in 2018 and serves as an adjunct lecturer in the Department of Electrical and Computer Engineering at UTSA and full-time as Director of Product Development at TRC Consultants, where he leads a team of developers in producing petroleum engineering software. His teaching and research interests include engineering programming, control systems, and robotics.
Paul E. Morton The University of Texas at San Antonio
Dr. Paul Morton, MD, Ph.D. (Electrical Engineering), Col USAF Retired: Paul Morton is the unusual cross between an experienced physician and PhD engineer. He received his BS EE from Purdue in 1970, and then served in Viet Nam as an Airborne Ranger Infantry Officer, flying UH-1H Helicopters. After Viet Nam he earned a MS EE in 74, MD and PhD EE both in 81, from the University of Missouri and completed residency in OB/GYN at Washington University in St Louis in 85. He joined the Air Force and practiced at George AFB in California for 2 years and then went to the Armstrong Aeromedical Research Lab at Wright-Patterson AFB for 9 years. While there he did research in the Human Engineering Division, deployed around the world with the Test Wing, served on the National Aerospace Plane Program team, served as Chief Scientist of the Lab and military commander, and worked in the OB/GYN department of the Medical Center training residents in urodynamics and gynecologic surgery and teaching medical students. He then went to San Antonio and was chair of the OB/GYN Department for Brook Army Medical Center / Wilford Hall for 8 years and then retired and did solo private practice for 9 years. During this time he also taught Logic Design in the evenings as an adjunct professor at the University of Texas at San Antonio. Closing his practice in 2013, Dr Morton has been full time Professor of Practice in the Electrical and Computer Engineering Department at UTSA where he teaches Logic Design, Microprocessors and Digital Systems Design. He is active in his local church in marriage ministry and is the Medical Co-Chair of the Christian Medical and Dental Association, San Antonio advisory council as well as a Group Leader in Bible Study Fellowship. He is also an amateur violist playing in his church orchestra.
Mason Cole Conkel Electrical and Computer Engineering, Klesse College of Engineering and Integrated Design, University of Texas at San Antonio
Mason Conkel is currently a graduate student at the University of Texas at San Antonio. He is pursuing a Ph.D. in Electrical Engineering. His research interests focus on artificial intelligence theory, software, hardware, and education.
Thuy Khanh Nguyen University of Texas at San Antonio
Mrs. Khanh Nguyen has been with UTSA's Electrical and Computer Engineering department as the Program Coordinator since 2018. She works directly with the graduate population and faculty in the department with regards to academic and student matters.
Chunjiang Qian
The University of Texas at San Antonio
orcid.org/0000-0002-3897-8149
Dr. Chunjiang Qian received his Ph.D. degree in Electrical Engineering from Case Western Reserve University, 2001. Since August 2001, he has been with the Department of Electrical and Computer Engineering, University of Texas at San Antonio, where he is currently the department chair and Mary Lou Clarke Endowed Professor. His current research interests include robust and adaptive control, nonlinear system theory, optimal control, network control, and mathematical foundation of deep learning. He has also applied research to UAV systems, power generation systems, electric vehicles, and marine vehicles.
Dr. Qian is a recipient of 2003 U.S. National Science Foundation (NSF) CAREER Award and one of the inaugural recipients of the University of Texas System Regents’ Outstanding Teaching Award in 2009. He received the 3rd Best Paper Award in the ISA (International Society of Automation) Power Industry Division Symposium (2011) and the Best Poster Paper Award in the 3rd IFAC International Conference on Intelligent Control and Automation Science (2013). He currently serves as an Associate Editor for Automatica and International Journal of Robust and Nonlinear Control. Dr. Qian is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).
Abstract
Objective and Motivation: Artificial intelligence (AI) has been identified as a national priority for future technologies in the United States. AI, as a backbone for big data analysis, has demonstrated its potential as a lifestyle-changing technology in different areas such as speech/image recognition, bioinformatics, drug design, and autonomous vehicles. A significant amount of effort has been dedicated to promoting student training on AI in undergraduate electrical and computer engineering (ECE) or computer science (CS) programs in the past 5 years. However, current efforts do not match the posted job requirements, leading to a shortage of a well-trained workforce on AI to meet the unprecedentedly increasing demands in the job market. Specifically, quantitative evaluation of students' training outcomes is lacking. Therefore, the goal of this study is to examine the AI-related curriculum in a department of ECE, evaluate the competencies of ECE graduates, and bridge the gap between desired educational outcomes and job requirements identified in the global market.
Methods: An AI certificate program was launched in 2020 in the department of ECE hosted in a Hispanic-serving institute with 45% of first-generation college students. The AI certificate program requires 1 required course and 4 elective courses from 8 undergraduate-level courses and 2 graduate-level courses from ECE and CS. All course topics were compared with recent industrial-specific skills reported to evaluate students’ technical skills gained from their AI-certificate courses. The competencies of a student will be defined based on how much they meet the job requirements, their post-graduation placement, and their job-hunting period. A total of 20 students who gained AI certificates from 2020 to 2022 were included in the study. Their competencies were compared with a reported job-hunting period in 2022. Data collected will be analyzed using descriptive and inferential statistics and presented in graph or tabular forms.
Statement of Results: This study evaluated students' competencies in AI based on their performances in AI-certificated courses, their post-graduate placement, and their job-hunting period. The comparison between course topics and industrial-specific skills leads to a list of recommendations to improve the AI-related curriculum design and bridge the gap between training for the AI workforce and increasing AI job demands in the market.
Jin, Y., & Applonie, R., & Morton, P. E., & Conkel, M. C., & Nguyen, T. K., & Qian, C. (2023, June), Quantification of Competencies-based Curricula for Artificial Intelligence Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44004
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