Currently submitted to: JMIR Formative Research
Date Submitted: May 23, 2026
Open Peer Review Period: Jun 3, 2026 - Jul 29, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Bridging the Curricular Lag: Faculty Perspectives on Generative AI in Medical Education Using the MED-AI Survey at a Spanish University
ABSTRACT
Background:
The rapid integration of Generative Artificial Intelligence (GenAI) tools into higher education is transforming teaching practices in medicine. However, empirical data on faculty use, perceptions, and concerns remain limited, particularly regarding how teaching experience and professional profiles influence institutional readiness.
Objective:
This study aimed to explore how medical faculty perceive and use GenAI in teaching, and to identify key barriers, ethical concerns, and future educational needs. To this end, we developed and applied the Medical Education in Artificial Intelligence (MED-AI) Survey, an original instrument designed to assess faculty adoption, perceptions, and attitudes toward GenAI.
Methods:
We conducted an exploratory cross-sectional survey among faculty members at the University of Alcalá (UAH) (n=24). The survey included four domains: (1) demographic and technological profile, (2) frequency and type of AI use, (3) perceived barriers and concerns, and (4) perspectives on future challenges and training needs. Data were analyzed using non-parametric tests (Mann-Whitney U) to compare perceptions based on teaching seniority and professional background (MD vs. Non-MD).
Results:
Faculty reported heterogeneous adoption of GenAI tools, with predominant use in teaching preparation. A statistically significant divergence was identified regarding curricular readiness (p=0.0025); Senior faculty expressed marked skepticism toward the current Medical Degree’s adequacy for an AI-enhanced future compared to Junior faculty. Conversely, a high degree of cross-disciplinary consensus (p>0.05) was found between MDs and Non-MDs regarding ethical risks—specifically the loss of critical thinking—and the potential of AI to provide administrative relief. All groups expressed a near-unanimous demand for formal institutional guidelines and responsible-use training.
Conclusions:
Generative AI is already influencing medical teaching practices, yet its integration is characterized by an "experience-readiness paradox" where senior educators perceive a significant curricular lag. Findings highlight the need for structured faculty development, ethical guidance, and formal curricular reform rather than sporadic experimentation. The MED-AI Survey provides a useful exploratory framework for assessing AI adoption and suggests that institutional strategies must address the unified demand for training across all career stages.
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