Shuang Geng, Helen Lallos-Harrell, Jiya Ashar, Thomas J. McKenna, Annwesa Dasgupta, Caleb Farny, Emma Lejeune — arXiv preprint (2026). 📄 Full text (arXiv)
Synthesis
This descriptive study documents student LLM use in an undergraduate engineering mechanics course (Spring 2026), responding to the lack of domain-specific empirical evidence for pedagogical policy in engineering education.
The authors contribute a reproducible survey instrument capturing student AI usage patterns, attitudes, and verification practices, linked to academic performance metrics — an open methodological framework for other instructors.
A deployable sequence of nine structured, instructor-led AI demonstrations models strategic LLM delegation and evaluation for students, treating AI use as a taught skill rather than an assumed behavior.
Preliminary data show shifting student behaviors and complex relationships between AI reliance and course outcomes, though the primary contribution is the study design itself; results are framed as preliminary.
Related Pages
Citation
APA: Geng, S., Lallos-Harrell, H., Ashar, J., McKenna, T. J., Dasgupta, A., Farny, C., & Lejeune, E. (2026). Structured AI demonstrations and student LLM use in engineering mechanics: Study design and preliminary results. arXiv:2607.28710.