Generative AI in Engineering Education: A Survey of Student and Instructor Usage and Attitudes

Abstract

Generative AI tools offer benefits to engineering education while introducing challenges, as perspectives on their use vary significantly. To understand perceptions of Generative AI tools among students and instructors, this study explores usage patterns of these tools and investigates perspectives and attitudes toward these tools among the two groups, with a specific focus on academic integrity. Data was collected through targeted questionnaires hosted by Qualtrics. Separate instruments were developed for students and instructors to capture their experiences and viewpoints on Generative AI tools. Descriptive statistics, including frequencies and percentages, were used to summarize perceptions and usage patterns. Findings of this study reveal a gap in the adoption and integration of Generative AI tools between students and instructors in engineering education. Students reported higher levels of familiarity and engagement with these tools, while instructors showed reluctance to incorporate them into teaching practices. This reflects a divide between students and instructors, both technologically and philosophically: students prioritize function and outcome from Generative AI tools, while instructors focus on process and oversight. Addressing this gap requires institutionally supported dialogue and co-developed guidelines that respect both student and instructor perspectives.

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