Awareness, attitudes, and utilization of large language models among healthcare students in Saudi Arabia: a cross-sectional analysis
Related Experiment Videos
Mohammed A Almeshari1,2, Osama A Asiri1, Ayedh D Alahmari1
1Department of Rehabilitation Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.
Introduction:
Large language models (LLMs) such as ChatGPT are increasingly adopted in health professions education worldwide, yet evidence on their use among healthcare students in the Middle East remains limited.
Methods:
This cross-sectional study assessed awareness, attitudes, and utilization of LLMs among healthcare students across Saudi Arabian universities using a self-administered online questionnaire, adapted from a previously published instrument, distributed to healthcare students at multiple Saudi universities between April and August 2025. Descriptive and inferential statistics were used to compare responses by sex. Of 449 responses collected, 441 provided informed consent; after applying eligibility criteria, 435 were included in the final analysis (70.3% female; median age 21.0 years).
Results:
Most students (78%) reported familiarity with LLMs and 87% agreed they are useful for both students and educators, though 75% acknowledged the risk of inaccurate information and only 18% had attended formal LLM training. Female students reported significantly higher perceived usefulness of LLMs than males (p = 0.011), while males were more likely to report low understanding of LLM functionality (p = 0.009).
Discussion:
These findings reveal a substantial gap between LLM adoption and structured AI-literacy training among healthcare students in Saudi Arabia, suggesting a need for curricula that build critical appraisal and verification skills alongside safe LLM use.
