BMC medical ethics · 2026
Principles for medical students' responsible use of generative AI: a student-partnered Delphi study.
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Simoni Jennifer, Pereira José Luis, Aschenbrenner-Noriega Ines, Crespo Sánchez Sofía, Egaña-Yin Diego, García-Gargallo Carlota, Hertog Oliver L A, Idoate Andrés, Morales-Gutiérrez Ian, Romero Alexandra, Santiago-Martínez Paula, Sawczyn Gabriela, Vahamaki Adrián, Urtubia-Fernandez Judith, Zurita Rocio, Mengual Elisa
Аннотация
BACKGROUND: Due to the widespread use of generative artificial intelligence (GenAI) in undergraduate medical education (UGME), this study aimed to develop a set of consensus-based principles for the responsible and ethical use of GenAI, established by medical students and faculty and further validated by international medical and artificial intelligence (AI) experts.
METHODS: A four-round modified Delphi process, an in-person idea-generation round, two structured rating rounds, and an external expert validation round were conducted between May 2025 and February 2026. The panel included medical students (n = 13) and medical professors (n = 2) in the first three rounds, and international experts in medical education and AI (n = 9) in the final round. In Round 1, an open-ended question yielded 37 potential principles. In Rounds 2 and 3, these items were rated using a 7-point Likert scale (1 = Strongly Disagree, 7 = Strongly Agree) with space for written comments; median scores and interquartile ranges were used to assess the strength of agreement against predefined decision rules, and the principles were retained, reworded, consolidated, or excluded accordingly. In Round 4, the near-final principles were validated by 9 international experts in AI medical education, AI ethics, AI technology education, or senior medical educators involved with AI curricula.
RESULTS: The process identified 14 consensus-based principles for the use of GenAI in UGME.
CONCLUSION: Given the swift adoption of GenAI in medical education, students need explicit guidance for ethical and responsible use. We established an internationally informed, expert-validated set of principles for medical students' use of GenAI through a student-partner Delphi study with external validation by a multinational expert panel. Institutions developing GenAI guidelines may adapt this framework for implementation in local educational contexts.
METHODS: A four-round modified Delphi process, an in-person idea-generation round, two structured rating rounds, and an external expert validation round were conducted between May 2025 and February 2026. The panel included medical students (n = 13) and medical professors (n = 2) in the first three rounds, and international experts in medical education and AI (n = 9) in the final round. In Round 1, an open-ended question yielded 37 potential principles. In Rounds 2 and 3, these items were rated using a 7-point Likert scale (1 = Strongly Disagree, 7 = Strongly Agree) with space for written comments; median scores and interquartile ranges were used to assess the strength of agreement against predefined decision rules, and the principles were retained, reworded, consolidated, or excluded accordingly. In Round 4, the near-final principles were validated by 9 international experts in AI medical education, AI ethics, AI technology education, or senior medical educators involved with AI curricula.
RESULTS: The process identified 14 consensus-based principles for the use of GenAI in UGME.
CONCLUSION: Given the swift adoption of GenAI in medical education, students need explicit guidance for ethical and responsible use. We established an internationally informed, expert-validated set of principles for medical students' use of GenAI through a student-partner Delphi study with external validation by a multinational expert panel. Institutions developing GenAI guidelines may adapt this framework for implementation in local educational contexts.