Exploring the use of AI methods and chatbots in medical education among medical students in Jordan: a cross-sectional study

To determine the attitude of the medical students towards the introduction of artificial intelligence (AI) in undergraduate medical students at Jordanian universities. An online cross-sectional survey was conducted over three months, from October 2023 to December 2023, and 1099 medical students participated from different Jordanian universities. The survey comprised four key sections: demographics, AI Exposure, perceptions and future outlook, and ethical concerns. The survey revealed a notable gender disparity in AI usage among medical students, in which females constituted a larger proportion (74%). Among the types of chatbots used, ChatGPT (GPT-3.5) was the most popular and used by 47.5% of students, and 49.4% indicated 1–5 h saved per week when using chatbots in general. 35.4% agreed that AI would benefit their future careers as doctors, and 30% agreed that medical students should be educated about the limitations and potential risks of AI technologies in patient care. The findings underscore the importance of integrating discussions about AI in medical curricula to ensure students are well-informed and prepared for the evolving landscape of healthcare.

Authors

Institutions

Publication Details

Journal
BMC Medical Education
Published
2026-02-18
DOI
https://doi.org/10.1186/s12909-026-08836-1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the use of AI methods and chatbots in medical education among medical students in Jordan: a cross-sectional study

Hiba Alzoubi, Anwar Rjoop, Khalifa Ahmed Alomari, Tasneem F. Khaleel et al.
BMC Medical Education
Artificial Intelligence in Healthcare and Education
article

Exploring the use of AI methods and chatbots in medical education among medical students in Jordan: a cross-sectional study

Hiba Alzoubi, Anwar Rjoop, Khalifa Ahmed Alomari, Tasneem F. Khaleel, Ahmed Almomani, Saleh A Ba-shammakh, Husam Abu Dawood, Hashem Abu Sarhan, Omar Mahrouq, Sofian Khasawneh, Raghad Yousef, Deema Thalji
article en

Abstract

To determine the attitude of the medical students towards the introduction of artificial intelligence (AI) in undergraduate medical students at Jordanian universities. An online cross-sectional survey was conducted over three months, from October 2023 to December 2023, and 1099 medical students participated from different Jordanian universities. The survey comprised four key sections: demographics, AI Exposure, perceptions and future outlook, and ethical concerns. The survey revealed a notable gender disparity in AI usage among medical students, in which females constituted a larger proportion (74%). Among the types of chatbots used, ChatGPT (GPT-3.5) was the most popular and used by 47.5% of students, and 49.4% indicated 1–5 h saved per week when using chatbots in general. 35.4% agreed that AI would benefit their future careers as doctors, and 30% agreed that medical students should be educated about the limitations and potential risks of AI technologies in patient care. The findings underscore the importance of integrating discussions about AI in medical curricula to ensure students are well-informed and prepared for the evolving landscape of healthcare.

BMC Medical Education
Jordan University of Science and Technology (JO), Palestine Polytechnic University (PS), Ministry of Health (JO), Hamad Medical Corporation (QA), Yarmouk University (JO)
Quality Education
Openalex Percentile: Top 54%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.