2026 ASEE Annual Conference & Exposition

The rapid rise of generative artificial intelligence (AI) tools marks a turning point for undergraduate engineering education, presenting extraordinary opportunities as well as complex pedagogical challenges. Students now have access to powerful technologies capable of performing sophisticated analyses, generating written explanations, solving complicated equations, and visualizing dynamic systems, all of which can potentially deepen conceptual understanding and support independent learning. At the same time, these tools raise important questions about academic integrity, classroom assessment validity, and the design of learning experiences that remain authentic and equitable. Understanding how students are currently using AI tools, and their perceptions about AI use in their coursework is essential for developing informed instructional strategies, clear ethical guidelines, and meaningful course policies.
This exploratory qualitative study presents findings from an open-ended survey conducted with undergraduate students enrolled in an engineering dynamics course at a large research university in the Southeastern United States. By inviting students to reflect on their experiences using (or choosing not to use) AI tools for assignments and problem-solving, as well as the perceived effects of these tools on their learning, confidence, and academic integrity. The study explored three research questions:
(RQ1) What do undergraduate engineering students in a dynamics class at a large research university in the Southern United States perceive as ethical, appropriate, and productive use of AI in their coursework?
(RQ2) How do these students describe their AI practices, skills, and decision-making processes, and how do these relate to their adoption of course AI use polices?
Our findings indicate that students possess sophisticated conceptual frameworks for ethical AI use centered on supporting learning processes rather than bypassing them. Students’ responses also revealed a broad range of AI competencies, from non-users to daily practitioners, with prompting effectiveness identified as a critical skill gap. Across both research questions, a tension was identified between using AI for efficiency (quick answers, completing assignments) and using AI for learning (understanding concepts, developing problem-solving skills). By providing an initial empirically grounded understanding of how students currently engage with these tools, this work contributes to ongoing discussions about responsible AI use, the preservation of academic integrity, and the development of innovative pedagogical policies and models that leverage AI to augment, rather than undermine, student learning.