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Navigating the AI Revolution in Engineering Management Education: Strategies for Detection, Integrity, and Pedagogical Enhancement

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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 Management Division (EMD) Technical Session 2

Tagged Division

Engineering Management Division (EMD)

Permanent URL

https://peer.asee.org/47796

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Paper Authors

biography

Raymond L. Smith III East Carolina University

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Dr. Smith is an assistant professor of engineering in the College of Engineering and Technology at East Carolina University. Dr. Smith’s research focuses on developing and applying operations research and applied statistics methods to provide model-based, implementable solutions for complex systems. His teaching interests include operations research, simulation modeling and analysis, systems optimization, systems engineering and analysis, engineering economy, and health systems engineering. Dr. Smith’s research interests include simulation modeling and optimization methodologies with applications to healthcare, public health, supply chain, information systems, logistics, sustainability, and other industrial and service systems.

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biography

Henry Lester University of Dayton Orcid 16x16 orcid.org/0000-0003-1053-6418

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Dr. Henry D. Lester is an Associate Professor and Director of the Systems Engineering Program in the School of Engineering at the University of Dayton. He holds a Ph.D. & M.S. in Civil Engineering (The University of Alabama), M.S. in Applied Statistics (T

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Abstract

The accessibility of Artificial Intelligence (AI) resources has led to a profound transformation of the educational landscape, redefining how students gather, organize, and absorb information. In engineering management education, students have traditionally engaged in assignments to deepen their knowledge and cultivate their professional expertise. However, the proliferation of advanced AI resources has brought about a significant shift in this paradigm, with students increasingly turning to AI platforms to expedite their assignments, often with minimal personal input and limited learning outcomes. This study investigates the utilization and detection of AI-generated content within the context of engineering management education, emphasizing the critical importance of upholding academic integrity. It explores the far-reaching impact of AI on the education sector, highlighting the emergence of AI detection tools that resemble plagiarism detection tools aimed at evaluating the authenticity of student-submitted work. This study examines the efficacy of several leading AI detection tools, offering insights into their accuracy and dependability. Engineering management, with its diverse subfields encompassing leadership, organizational management, strategic planning, financial resource management, project management, and legal considerations, faces opportunities and challenges in integrating AI-generated material into educational curricula. This study assesses the implications of AI integration within these subfields and its potential impact on students' skill development and comprehension.

Smith, R. L., & Lester, H. (2024, June), Navigating the AI Revolution in Engineering Management Education: Strategies for Detection, Integrity, and Pedagogical Enhancement Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47796

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