2026 ASEE Annual Conference & Exposition

Generative artificial intelligence (GenAI) tools are increasingly used by biomedical engineering students, yet formal instruction and guidance on their effective use are often absent from biomedical engineering curricula. In this study, we investigated the impact of integrating structured GenAI instruction into an undergraduate Computational Biomedical Engineering course. We hypothesized that GenAI instruction and optional use would not hinder student learning outcomes. Early in the semester, students received instruction on the fundamentals of GenAI and its application for coding support, including how these tools work, common pitfalls such as inaccurate code generation and hallucinations, and strategies for responsible use. Students were required to use GenAI tools on the first problem set and then given the option to use it on the subsequent ten coding assignments. Learning of non-AI course content, self-reported completion time, and student perceptions were evaluated through pre- and post-course assessments and anonymous surveys. Students who used GenAI (n = 15) and those who did not (n = 8) demonstrated comparable improvements in conceptual understanding, indicating no negative impact on non-AI course content. Students who used GenAI reported shorter average completion times at the aggregate level, though significant time reductions were limited to select assignments and results varied among individual students. Student interest in biomedical engineering and confidence in foundational computational topics were consistently positive across groups. Overall, these findings suggest that intentional GenAI instruction and optional use can be integrated into computational biomedical engineering courses without compromising learning or student interest in biomedical engineering.