2026 ASEE Annual Conference & Exposition

Faculty Experiences with Emerging Technologies: A Case Study of Artificial Intelligence

Presented at Faculty Development Division (FDD) Technical Session 1: Generative AI and Transformations in Faculty Work

Background
This full research paper explores faculty experiences with utilizing emerging technologies for fostering students' competencies in engineering education. Technological advancements continue to shape how teaching and learning occur in engineering education, indicating an increased implementation of technologies across engineering classrooms. Yet, the process through which engineering faculty adopt and integrate emerging technologies remains underexplored. Conceptualizing adoption as a dynamic process rather than a fixed outcome positions faculty as active agents whose attitudes, beliefs, and contextual realities evolve with experience. This process-oriented lens highlights technology adoption as co-constructed within faculty realities and needs rather than a static outcome-based approach. Understanding how engineering faculty navigate this process is critical for designing supports that foster adaptability and purposeful technology use.

Purpose and Methods
This study investigates how engineering faculty experience adopting artificial intelligence (AI) tools to foster student competencies using a qualitative approach. It examines how influencing factors for faculty adoption process align with existing technology adoption frameworks. Using a deductive qualitative approach, three engineering faculty members from R1 institutions in the United States were interviewed. Data were collected, analyzed, and interpreted using a conceptual framework that combined the theoretical lenses of the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and Social Cognitive Career Theory (SCCT).

Results and Significance
Findings illustrate how cognitive (TAM), social (DOI), and career (SCCT) influences manifest in faculty experiences with AI, revealing both alignment and gaps in current frameworks. These insights contribute to extending adoption theory toward emerging technologies and inform strategies to support faculty's effective, competency-driven adoption practices in engineering education.

Authors

Deborah Moyaki http://orcid.org/0009-0005-7441-0306 University of Georgia [biography]

Deborah Moyaki is a doctoral student in the Engineering Education and Transformative Practice program at the University of Georgia. She holds a bachelor’s degree in Educational Technology and is excited about the possibilities technology offers to the learning experience beyond the formal classroom setting. Her research focuses on improving the educational experience of engineering students using virtual reality labs and other emerging technologies.
Dr. Nathaniel Hunsu The University of Georgia [biography]

Nathaniel Hunsu is an assistant professor of Engineering Education. He is affiliated with the Engineering Education Transformational Institute and the school of electrical and computer engineering at the university. His interest is at the nexus of the res
VINCENT OLUWASETO FAKIYESI University of Georgia [biography]

Vincent Oluwaseto Fakiyesi is a doctoral student in the Engineering Education program at the University of Georgia. He holds a bachelor’s degree in Chemical Engineering and is interested in advancing assessment practices to better understand student learning. His research focuses on cognitive diagnostic modeling, student misconceptions, and adaptive assessment systems. He applies diagnostic models to analyze large-scale educational assessment data and generate actionable insights that support teaching, learning, and personalized feedback in engineering education. He also examines assessment practices in the era of generative artificial intelligence, emphasizing adaptive, interpretable, and learning-centered systems.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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