The Delphi Method as a Consensus-Building Tool in Designing an Application Framework for Artificial Intelligence in Medical Education in Spain. - González Soltero, Rocío; García-Pardina, Alejandro; Bellido-Esteban, Alberto; Ryan Murúa, Pablo; de Jesús, María Teresa; Giménez Maroto, Ana; García García, Esther; Ramírez Moreno, Carlos; Biscaia, José Miguel; Mohedano, Rosa B.; Gal Iglesias, Beatriz; Sánchez-Vera, Isabel; et al. (Spanish Journal of Medical Education, 2026)
Introduction. The rapid integration of artificial intelligence (AI) into biomedical education has created an urgent need for a framework that can guide both learners and academic staff. In Spain, where institutions are adopting AI at speed but without homogeneous regulatory or pedagogical guidance, structured approaches are required to prioritize competencies and address ethical, legal and educational challenges. Methods. A Delphi study was coordinated by the Universidad Europea de Madrid, using the REDCap platform to manage iterative rounds. The project received financial support from the Spanish Society for Medical Education (SEDEM). Experts in medical education, including some with specific AI expertise, participated in a sequence of online questionnaires. Through anonymized feedback, participants evaluated, refined and prioritized a core of areas where the impact of AI seems essential. Results. Experts agreed on the need for skills extending beyond technical literacy, emphasizing critical thinking, interpretation of AI‑generated outputs, bias awareness and professional responsibility. Ethical and legal considerations, particularly concerning privacy, transparency and decision‑making, were strongly prioritized. Participants also highlighted the transversal nature of AI, suggesting that competencies should be embedded across curricula rather than treated as isolated content. Discussion. Despite institutional heterogeneity, consensus converged on areas that balance innovation with ethical safeguards. The results support the development of a competency‑based framework capable of guiding curriculum design, informing faculty development and promoting responsible, evidence‑informed use of AI while safeguarding professional autonomy.