Previously submitted to: JMIR Medical Education (no longer under consideration since Feb 27, 2026)
Date Submitted: Jun 14, 2025
Open Peer Review Period: Jul 4, 2025 - Aug 29, 2025
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Bridging the AI Competency Gap: Readiness and Curricular Strategies Among Medical Students in a Single-Center Turkish Study
ABSTRACT
Background:
The integration of artificial intelligence (AI) into healthcare necessitates a paradigm shift in medical education. Preparing future physicians for AI-enhanced clinical environments requires a nuanced understanding of their readiness and the factors that influence it—particularly within digitally mediated learning contexts.
Objective:
To investigate the level of AI readiness and digital competencies among medical students and to identify key demographic and educational predictors of AI preparedness.
Methods:
A cross-sectional survey was conducted among 256 medical students at a single academic institution. Instruments included the validated Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) and a digital competency scale. Statistical analyses comprised descriptive metrics, correlation analyses, and multivariate linear regression modeling.
Results:
Students reported moderate AI readiness (MAIRS-MS mean = 45.66 ± 18.85). Regression analysis demonstrated that interest in AI was the strongest independent predictor of readiness (β = –17.31, p < 0.001), followed by gender (β = 4.27, p = 0.033) and age (β = 1.57, p = 0.044). Participation in seminars or coursework related to AI was not significantly associated with readiness. The model accounted for 31.4% of the variance (adjusted R² = 0.297).
Conclusions:
AI readiness in medical education emerges as a multidimensional construct shaped more by learner characteristics than by current educational exposures. These findings call into question the sufficiency of short-term curricular interventions and highlight the need for longitudinal, interest-responsive, and equity-focused AI training pathways. Embedding such frameworks early in medical training may be critical for cultivating a workforce capable of navigating an AI-driven future in medicine.
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