Frontiers in medicine · 2026
Awareness, attitudes, and utilization of large language models among healthcare students in Saudi Arabia: a cross-sectional analysis.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Almeshari Mohammed A, Asiri Osama A, Alahmari Ayedh D, Alghamdi Abdulrhman S, Alsaif Sulaiman S, Alhammad Saad A, Bamogaddam Yasser K, Alowaisi Malak, Alasmari Ali M, Alobaidi Nowaf Y, Alenezi Faraj K, Alwadeai Khalid S
Аннотация
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.
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.