Artificial intelligence can be applied in many different industrial engineering applications to promote statistical control and improve predictive capabilities. However, the literature is sparse in how to educate industrial engineers on the use of artificial intelligence to optimize the supply chain. The purpose of this study is to provide one approach for integrating artificial intelligence into supply chain education using the well-known beer game (a gamification and simulation approach to learn supply chain principles). Motivation for integrating artificial intelligence into the industrial engineering classroom is driven by the desire to better prepare students to enter the Industry 4.0 and Big Data workforce. The guiding research question was as follows: How can artificial intelligence be implemented into an elective engineering course to increase student perceptions and student learning outcomes? Participants included 120 sophomore-level students, enrolled in a three-credit course called Supply Chain Management Technology, at a research-intensive university in the midwest United States. Participants completed a 6-week module, including two weeks of the traditional online beer game and four weeks of the artificial intelligence-enhanced beer game using the Jupyter Notebook. In addition, due the split of sections (each was 60 students) a control group and treatment group was used to assess the intervention of adaptive comparative judgment on student learning. Qualitative and quantitative assessments include online discussions, reflections, artifact development, and pre-/post-surveys related to perceived satisfaction and learning outcomes. Findings provide evidence towards the effectiveness of the 6-week module to improve student perceptions and learning outcomes related to supply chain, artificial intelligence, and adaptive comparative judgment. In addition, implications are provided for the opportunity to improve student retention and completion rates using real-world scenarios and practical skill development associated with artificial intelligence.
Dr. Bosman has a Ph.D. in Industrial Engineering. Her research interests include Decision Support Systems (e.g., solar energy performance, valuation, and management) and Engineering Education (entrepreneurial mindset, energy education, interdisciplinary education, and faculty professional development). She spent the first part of her career working as a manufacturing engineer for world-class companies including Harley-Davidson, John Deere, and Oshkosh Defense and continues to provide workforce development consulting within this area.
Dr. Madamanchi is an independent Postdoctoral Researcher in the Future Work and Learning strategic impact area of Purdue University Polytechnic. His research centers around supporting digital transformation through workforce development, with a special focus on data science education and AI literacy.
Scott R. Bartholomew, PhD. is an assistant professor of Engineering/Technology Teacher Education at Purdue University. Previously he taught Technology and Engineering classes at the middle school and university level. Dr. Bartholomew’s current work revolves around Adaptive Comparative Judgment (ACJ) assessment techniques, student design portfolios, and Technology & Engineering teacher preparation.
Dr. Vetria L. Byrd is an assistant professor in the Department of Computer Graphics Technology in the Polytechnic Institute at Purdue University in West Lafayette, Indiana. Dr. Byrd is the founder and organizer of the biennial Broadening Participation in Visualization (BPViz) Workshop. Dr. Byrd has given numerous invited talks on visualization and has been featured in HPC Wire online magazine (2014), and numerous workshops nationally and internationally. Dr. Byrd received her graduate and undergraduate degrees at the University of Alabama at Birmingham, in Birmingham, Alabama which include: Ph.D. in Computer and Information Sciences, Master’s degrees in Computer Science and Biomedical Engineering and a Bachelor’s degree in Computer Science. Dr. Byrd’s research interests include: data visualization, data visualization capacity building and pedagogy, high performance visualization, big data, collaborative visualization, broadening participation and inclusion.
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.
