2024 ASEE Annual Conference & Exposition

Recently, Generative AI (GenAI) has gained rapid and extensive use. This technology has profound implications for workplace productivity in a way that offers impressive benefits and potential downsides and abuse. Accordingly, both career and ethical challenges loom before us. Engineers will be instrumental in the design and application of this technology; therefore, it is incumbent on universities to prepare them now to wrestle with these challenges. This responsibility is particularly important in the context of engineering leadership development. In this paper, we present work-in-progress of design and effectiveness of delivery of initial interventions on this topic in an undergraduate Engineering Leadership class at Texas A&M University. This technology already shows the potential to dramatically change the trajectory of careers; many fear the elimination of jobs. At the same time, others believe that GenAI will create entire new fields of employment and opportunity. Meanwhile, parallel concerns are detrimental effects on cybersecurity and privacy. A portion of our course content covers the broad topic of data innovations, including GenAI. The lecture that includes this topic provides connection to servant leadership. Our guiding principle is to practice mastery of this technology in ways that enhance humanity and promote transparency. A key assignment includes prompts for associated laboratory teams to grapple with career and ethical dilemmas on GenAI use. In this paper, we provide a literature review, and then describe the course content that includes data innovations including coverage of GenAI and their application to leadership. Next, we relate the prompts and instructions to laboratory teams and the requirements for them to report on their related deliberations. For feedback on the value of these initial attempts, we will perform mixed-methods research. We will survey students on the value of the content and activities in the context of preparing them for leadership roles in workplace decision making on GenAI. A parallel survey of industry representatives provides their perspectives on how they would like university engineering leadership development be done on these topics. We conclude the paper with a discussion and recommendations for future work.