Artificial Intelligence, Assessment Integrity, and Professionalism in Medical Education: Global Disruption and Lessons from the Gulf Cooperation Council Region

Abstract

Artificial intelligence (AI), particularly generative AI, is rapidly reshaping medical education worldwide. While AI-enabled tools offer significant opportunities for personalized learning, feedback automation, and clinical reasoning support, they simultaneously challenge foundational principles of assessment integrity and professional conduct. Traditional assessment models—largely predicated on individual authorship, knowledge recall, and observable performance—are increasingly strained by AI systems capable of generating sophisticated responses, analyses, and clinical narratives. This disruption has prompted urgent reconsideration of what constitutes academic honesty, valid assessment, and professional identity formation in contemporary medical training. This article critically examines the intersection of AI, assessment integrity, and professionalism in medical education from a global perspective, with particular attention to the experiences and emerging lessons from the Gulf Cooperation Council (GCC). The GCC provides a distinctive context characterized by rapid digital transformation, centralized accreditation and licensing systems, high-stakes assessments, and strong sociocultural norms governing professional behavior. These features make the region an instructive case for understanding how medical education systems respond to AI-driven challenges at scale. The article employs a critical narrative and conceptual framework, positioning generative AI as a normative disruptor that necessitates a reevaluation of assessment validity, ethical accountability, and the construction of professional identity. Utilizing worldwide scholarship, policy frameworks, and regional experiences, the analysis underscores that misalignment between assessment design and professional expectations jeopardizes trust, fairness, and public confidence. The essay advocates for a transition from reactive restriction to the principled integration of AI, highlighting the need for assessment redesign, AI literacy matched with professionalism, teacher development, and cohesive governance. These insights are intended to guide educators, institutions, and regulators in maintaining professional standards inside AI-enhanced medical education systems.

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