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New Perspectives in Science Education 15th Edition 2026

Extra Curricula Activities: Safe-by-Design Generative AI for Clinical Leadership Education – A Competency-Based Simulation Framework for Physicians and Dental Clinicians

Dimitrios Rallis; Virna-Maria Tsitou; Maria Dencheva

Abstract

Clinical leadership competencies—communication under stress, conflict management, shared decision-making, and clinician-to-clinician coordination—are increasingly required across medical and dental practice, yet training is often lecture-centred and offers limited opportunities for repeated rehearsal with standardised formative assessment. This paper presents a competency-based framework for integrating generative AI into clinical leadership education for physicians and dental clinicians, spanning clinical-stage students, residents, and continuing professional development participants. The framework is designed to be delivered as an elective extra-curricular module (simulation skills lab) that complements core curricula by developing professional and social skills through structured practice. The approach operationalises AI as bounded simulation rather than open-ended conversation: learners interact with scenario-templated patient/family roles and clinician-to-clinician handover/referral cases that include escalation and de-escalation logic. Performance is assessed via a rubric with domain scores (e.g., rapport, empathy, clarity/teach-back, conflict management, shared decision-making, professionalism, and handover quality), evidence-linked excerpts, and critical-failure rules for unsafe or unethical behaviours. A phased implementation pathway is proposed (prototype, faculty calibration, staged integration), with governance measures appropriate for EU academic settings, including synthetic-only scenarios, role-based access, retention policies, and pseudonymisation-aware analytics.

 

Keywords

generative AI; extra-curricular activities; medical education; dental education; simulation-based learning

 

REFERENCES

[1] Liu, X., Wu, C., Lai, R. et al. ChatGPT: when the artificial intelligence meets standardized patients in clinical training. J Transl Med 21, 447 (2023). https://doi.org/10.1186/s12967-023-04314-0

[2] Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., Gasiea, R. Y., Michie, C., Corral, J., Kwan, B., Dolmans, D., & Thammasitboon, S. (2024). A scoping review of artificial intelligence in medical education: BEME Guide No. 84. Medical teacher46(4), 446–470. https://doi.org/10.1080/0142159X.2024.2314198

[3] Ethics and governance of artificial intelligence for health. Guidance on large multi-modal models. Geneva: World Health Organization; 2024. Licence: CC BY-NC-SA 3.0 IGO

 



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Publication date: 2026/03/20
ISBN: 979-12-80225-97-9
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