Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Survey of Medical Students’ Perceptions and Attitudes in Saudi Arabia

Abstract

<title>Abstract</title> <bold>Background:</bold> The rapid evolution of Generative Artificial Intelligence (AI), particularly ChatGPT and large language models (LLMs), has introduced transformative potential in medical education. These tools offer innovative approaches to learning, simulation, and assessment. However, their integration into medical education remains underexplored, particularly in developing regions like Saudi Arabia. This study investigates medical students’ perceptions and attitudes toward AI in undergraduate medical education. <bold>Methods:</bold> A cross-sectional survey was conducted among 1,039 undergraduate medical students across Saudi Arabia. The survey, validated through pilot testing, assessed students' familiarity with AI, perceptions of its role in medical education, and acceptance of AI-driven teaching. Statistical analyses, including logistic regression, identified factors influencing students' perceptions. <bold>Results:</bold> Among participants, 57.2% were familiar with AI's role in medical education, and 70.1% supported integrating AI into their curriculum. Additionally, 86.4% believed AI would impact the future of medical education, and 71.1% felt access to AI chatbots would influence their competency. While 73.4% saw AI as beneficial for basic science education, only 41.6% recognized its potential for clinical training. Concerns included trust in AI-generated content (47.4%) and issues like reference fabrication (64%). Only 29.8% viewed AI as superior to traditional methods, yet 60.7% believed it would enhance academic performance. <bold>Conclusion:</bold> Saudi medical students show strong interest in AI integration, especially for basic sciences and simulation-based learning. However, they express skepticism about AI’s reliability and its ability to replace traditional tutor-based education. Concerns about ethical use and quality assurance highlight the need for structured guidelines to ensure AI is effectively incorporated while preserving critical human skills, clinical acumen, and ethical decision-making. Balancing AI with human instruction remains essential for its successful adoption in medical education.

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