Abstract Background Precision medicine is often framed in terms of technical innovation and clinical promise, while its broader ethical and societal implications receive less attention in medical training. Addressing these issues requires formats that allow participants to explore uncertainty, competing values, and governance challenges. This study examines SPARK, a scenario-based deliberative activity designed to support collective reflection on the systemic and value-laden dimensions of these technologies in educational settings. Methods We analysed qualitative and descriptive-quantitative data from two university workshops in Norway involving students in medicine and bioethics. The SPARK activity uses structured scenarios, information cards, and issue prompts to guide small-group discussions of policy and implementation dilemmas related to artificial intelligence (AI)-supported precision medicine. The analysis focused on engagement processes, examining how participants articulated trade-offs, uncertainties, and competing priorities during deliberation. Results Participants initially framed precision medicine primarily in technical and individualised terms. During the activity, discussions expanded to include ethical and societal considerations such as fairness, responsibility, and resource allocation. The deliberative structure enabled participants to articulate trade-offs and negotiate competing priorities while moving between individual and group decision stages. Rather than resolving disagreements, the activity made underlying tensions explicit and supported discussion of competing ethical considerations within a time-limited educational setting. Conclusions SPARK demonstrates how lightweight deliberative formats can support structured ethical reflection on emerging medical technologies in educational contexts. By making tensions visible and negotiable within group discussion, such formats may complement existing approaches to ethics education and help create space for engagement with the broader implications of AI-supported precision medicine.
Paiva et al. (2026) studied this question.