More than marks: The Broader Educational Contribution of Generative AI in Biomedical Science

In higher education, discussion surrounding generative artificial intelligence (GenAI) often centers on academic integrity and potential grade inflation. However, much less attention has been given to how GenAI may be reshaping the learning process itself, including how students reason and construct understanding. This gap limits our ability to evaluate GenAI in educational terms that extend beyond performance outcomes. This study examined the association between declared GenAI use and academic performance in undergraduate biomedical science courses, using physiology and biochemistry as disciplinary contexts. Student grades were analyzed between declared GenAI Users and Non-users within each course and across assignments. Contrary to common assumptions that using GenAI would advantage or disadvantage students academically, no measurable difference in performance was observed between Users and Non-users in any analysis. This consistent absence of a grade effect occurred alongside evidence that students primarily declared using GenAI for tasks such as concept explanation rather than core content generation. As such, the findings support the interpretation that the influence of GenAI use are concentrated at the level of reasoning and self-regulation, rather than having significant effects on grades. These findings suggest that incorporating measures of learning processes alongside summative outcomes may better support and guide the integration of GenAI in biomedical science education.