Exploration of Career and Ethical Challenges of Analytics and 
Generative Artificial Intelligence in an Engineering Leadership Course

B. Michael Aucoin; Zhendi Zhang; Miles O. Dodd

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Engineering, Ethics, and Leadership
Tagged Divisions: Engineering Leadership Development Division (LEAD) and Engineering Ethics Division (ETHICS)
Permanent URL: https://peer.asee.org/47393

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Paper Authors

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B. Michael Aucoin P.E. Texas A&M University orcid.org/0000-0001-6084-1950

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B. Michael Aucoin is a Senior Lecturer in the Department of Engineering Technology & Industrial Distribution at Texas A&M University, an Adjunct Instructor in the School of Leadership Studies at Gonzaga University, and President of Electrical Expert, Inc.

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Zhendi Zhang Texas A&M University

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Miles O. Dodd Texas A&M University

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Miles Dodd is currently pursuing a Master's degree in Engineering Technology at Texas A&M University. His research interests include semiconductor testing and validation, as well as hardware cybersecurity. Miles is a teaching assistant in the Department of Engineering Technology and Industrial Distribution, assisting with engineering leadership, semiconductor testing, and semiconductor validation classes. His experience facilitating lab sections for the engineering leadership class has allowed him to gain insights into students' beliefs about generative artificial intelligence and its future role in engineering leadership and higher education.

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Abstract

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.

Citation
Format

Aucoin, B. M., & Zhang, Z., & Dodd, M. O. (2024, June), Exploration of Career and Ethical Challenges of Analytics and Generative Artificial Intelligence in an Engineering Leadership Course Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47393

TY - CPAPER
AB - 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&amp;amp;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.
AU - B. Michael Aucoin P.E.
AU - Zhendi Zhang
AU - Miles O. Dodd
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Exploration of Career and Ethical Challenges of Analytics and
Generative Artificial Intelligence in an Engineering Leadership Course
UR - https://peer.asee.org/47393
ER -