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Medical students' knowledge, attitudes, and practices regarding artificial intelligence: A cross-sectional study
Ahmed A Mosa1, Ibrahim A Naqid2, Aram A Younus2
1Department of Medical Education, College of Medicine, University of Zakho, Zakho, Kurdistan Region, Iraq.
Introduction And Objectives:
Artificial intelligence (AI) is a branch of computer science capable of mimicking human cognitive functions. The integration of AI has significantly transformed healthcare and medical education. In medical education, AI has played a crucial role in improving students' educational experiences. However, evidence from low- and middle-income countries (LMICs) such as Iraq remains limited. Therefore, this study aimed to assess medical students' knowledge, attitudes, and practices (KAP) toward AI in the Kurdistan Region of Iraq.
Materials And Methods:
This cross-sectional study was carried out among medical students in the Kurdistan Region of Iraq, between 3rd November 2025 and 20th January 2026. A structured, self-administered questionnaire was delivered to students either by paper and pen or digitally using the Google Form platform. A total of 575 medical students were recruited. The final piloted questionnaire consisted of 39 items and was divided into five distinct sections: basic demographics, knowledge, attitudes, practices, and perceived barriers.
Results:
The mean age of the study participants was 21.31 ± 1.7 SD years, and the representation between both genders was approximately equal. Additionally, only one-fifth (19.7%) of the students had attended a workshop about AI in healthcare and medical education. The internet/social media was the most common information source utilized by the students (77.7%). Moreover, AI was most frequently used for homework/assignments (56.5%), and the most significant barrier limiting AI use was fear of over-reliance (53.2%). Female students had significantly lower odds of having better knowledge (adjusted odds ratio (aOR): 0.34; 95% confidence interval (CI): 0.22-0.51; p<0.001), and positive attitudes (aOR: 0.46; 95% CI: 0.28-0.77; p=0.003) compared to male counterparts. Additionally, students from Duhok Medical College had significantly lower odds of having positive attitudes (aOR: 0.52; 95% CI: 0.30-0.88; p=0.014).
Conclusions:
The present study revealed suboptimal levels of KAP regarding AI among the surveyed medical students. Integrating structured AI education into medical school curricula may strengthen students' understanding and competencies in AI, thereby resulting in better preparedness for AI application in future practice.
