ArticlePLOS digital health2026
Barriers, adoption patterns, and determinants of artificial intelligence use among health sciences students in Palestine.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Artificial intelligence (AI) is increasingly integrated into healthcare education worldwide, yet disparities in access, training, and institutional readiness remain evident, particularly in low-resource and conflict-affected settings. Understanding how health sciences students engage with AI technologies and the barriers they encounter is essential for guiding the development of AI-ready curricula in Palestinian universities. This study aimed to examine the adoption patterns, perceived barriers, and determinants of artificial intelligence use among health sciences students at Palestine Polytechnic University in Palestine. A descriptive cross-sectional study was conducted among 666 undergraduate students from the Colleges of Nursing, Medicine and Health Sciences, and Dentistry. Data were collected using a validated self-administered questionnaire assessing demographic characteristics, AI knowledge, attitudes, practice behaviors, and perceived barriers. Descriptive statistics summarized usage patterns. Mann-Whitney U tests, Kruskal-Wallis tests, and chi-square analyses examined group differences. Multivariate logistic regression identified predictors of AI adoption. Statistical significance was set at p ≤ .05. The result of the study. AI use was highly prevalent, with 93.4% of students reporting active engagement. AI was primarily used for study and learning (87.7%), written assignments (57.5%), and personal purposes (54.2%). Significant differences in AI usage were observed across academic disciplines (χ² = 17.292, p = .008), with dentistry students reporting longer daily use. Major barriers included limited curriculum integration (48.2%), ethical and privacy concerns (47.9%), and insufficient training centers (40.4%). Multivariate analysis showed that college affiliation and knowledge score significantly predicted AI adoption, whereas gender, academic year, and previous AI training were not significant predictors. The Conclusion. Despite widespread exposure to AI technologies, students' engagement remains largely informal and constrained by curricular, infrastructural, and ethical barriers. Institutional strategies including curriculum reform, faculty development, and improved digital infrastructure are necessary to support responsible AI integration in health sciences education in Palestine.
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Registered trials
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