A pioneering faculty training program for artificial intelligence in Ukrainian medical education

Abstract

<title>Abstract</title> Background The global higher education landscape is undergoing a profound transition due to the convergence of digital technologies and pedagogical theory. In medical education, large language models now demonstrate clinical reasoning capabilities comparable to those of medical students, yet faculty training remains highly inconsistent globally. In Ukraine, this challenge is compounded by wartime pressures and a high rate of «bottom-up» AI adoption (84%) among students, which is often plagued by ethical uncertainty and a lack of structured guidance. There is an urgent need to shift the educator’s role from a «transmitter of absolute truths» to a «mediator of learning» within the emerging clinician-AI-patient triad. Methods We implemented a multi-component training program at Ivano-Frankivsk National Medical University during the 2024–2025 academic year. The program included practical workshops on AI fundamentals and medical education-specific tools, individual consultations for discipline-specific AI adaptation, the development of methodological materials, and the creation of a community of practice. We tracked quantitative metrics, including the number of trained faculty, program coverage, and the development of resources, alongside qualitative assessments of AI integration into teaching practices. Results Between September 2024 and March 2025, 211 faculty members (29% of the total 732 scientific and pedagogical staff members) completed the foundational training, with participation exceeding 90% in departments such as Anatomy and Physiology. Demographic analysis revealed strong cross-generational engagement, with 34% of participants being over 50 years old. To address the resulting regulatory and ethical gaps, Ivano-Frankivsk National Medical University formally adopted a comprehensive «Policy on the Use of Artificial Intelligence Systems» in May 2025, providing a binding legal and ethical framework for AI integration. Key outputs included Ukraine’s first comprehensive methodological guide for AI in medical education, a library of 142 documented use cases, and the deployment of two custom AI assistants that achieved a user satisfaction rating of 4.3 out of 5 across 3.200 interactions. Faculty reported successful integration of AI into lecture synthesis, case-based learning, and research supervision. Conclusions This study demonstrates the feasibility of large-scale faculty AI literacy initiatives even in resource-constrained and socially disrupted contexts. Systematic training, coupled with a formal institutional policy, facilitates the fundamental transformation of medical education required for the 21st century, ensuring educators can effectively guide students in AI-augmented healthcare practice. Investment in faculty digital competencies is essential for maintaining institutional competitiveness and academic rigor in the face of rapidly advancing machine intelligence.

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