Awareness, attitudes, and utilization of large language models among healthcare students in Saudi Arabia: a cross-sectional analysis
Rattachement africain : sa, gb. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Awareness, attitudes, and utilization of large language models among healthcare students in Saudi Arabia: a cross-sectional analysis
- Date Crossref
- 16/09/2026
- Éditeur
- Frontiers Media SA
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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King Saud bin Abdulaziz University for Health Sciences pays non établi dans la noticeUniversité ou école supérieure
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King Saud Medical City pays non établi dans la noticeÉtablissement de santé
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King Saud University pays non établi dans la noticeUniversité ou école supérieure
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University of Birmingham Institute of Inflammation and Ageing pays non établi dans la noticeUniversité ou école supérieure
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Taibah University pays non établi dans la noticeUniversité ou école supérieure
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King Abdullah International Medical Research Center pays non établi dans la noticeStructure de recherche
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National Guard Health Affairs Department of Anesthesia pays non établi dans la noticeÉtablissement de santé
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College of Applied Medical Sciences Department of Rehabilitation Health Sciences pays non établi dans la noticeUniversité ou école supérieure
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College of Medical Rehabilitation Sciences Respiratory Therapy Department pays non établi dans la noticeUniversité ou école supérieure
King Saud bin Abdulaziz University for Health Sciences, King Saud Medical City et King Saud University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.