Title: Medical Students’ AI Literacy and the Human Dimension of Digital Health Education

Name of presenter: Renáta Nagy

Affiliation and academic title: University of Pécs Medical School Department of Languages for Biomedical Purposes and Communication, Hungary

Email address: renata.nagy@aok.pte.hu

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Keywords: AI literacy, Medical education, Human-centered learning, Ethical use of artificial intelligence, Digital health communication

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Abstract:

The rapid integration of generative artificial intelligence into higher education has fundamentally reshaped how students learn, communicate, and construct knowledge. In medical education, these changes raise critical questions about AI literacy. Not only as a technical competence, but as a cognitive, ethical, and communicative skill essential for future physicians. While AI tools offer clear benefits for learning and information access, concerns remain regarding overreliance, diminished critical thinking, and the erosion of human-centered professional values.

This study presents an ongoing mixed-methods PhD research project investigating medical students’ AI literacy in the context of digital health education. The research employs a validated AI literacy questionnaire encompassing cognitive, affective, behavioural, and ethical dimensions, complemented by semi-structured interviews to capture students’ lived experiences, perceptions, and reflective practices related to AI use. Data collection is currently in progress across all four Hungarian medical schools (Pécs, Budapest, Szeged, and Debrecen) and is being extended to multiple European medical universities through established international academic collaborations.

The study aims to explore how medical students understand, use, and critically evaluate AI tools, how confident they feel in navigating ethical and professional challenges, and how AI influences their learning strategies, communication practices, and sense of responsibility as future healthcare professionals. By foregrounding the human factor in AI-supported education, the research seeks to identify gaps in current training and to inform pedagogical approaches that balance technological innovation with ethical awareness, wellbeing, and meaningful human interaction.

Preliminary engagement suggests high student interest and varied patterns of AI use across institutional and cultural contexts. Final results will contribute to ongoing discussions on sustainable, human-centered AI integration in medical and language education.