Saudi Medical Students' Perceptions and Attitudes of Integrating Generative Artificial Intelligence Integration in Medical Education: A Cross‐Sectional Study
Saudi medical students express strong interest in GenAI—particularly for basic sciences and simulation—but perceive it as complementary rather than superior to human instruction, reflecting learner perceptions.
Abstract
ABSTRACT Background and Aims Generative Artificial Intelligence (GenAI) has catalyzed a transformation in medical education. Understanding learners' perceptions is essential to guide their responsible integration into curricula. Methods A cross‐sectional survey was administered to 1039 undergraduate medical students across Saudi Arabia. A purpose‐developed, pilot‐tested instrument (Cronbach's α = 0.71 for dichotomous items) assessed students' familiarity with computational language models, perceptions of their educational utility, and attitudes toward technology‐enhanced pedagogical approaches. Descriptive statistics, Kolmogorov–Smirnov testing for normality, and multivariable binary logistic regression (two‐sided, α = 0.05) were performed using IBM SPSS Statistics v28.0. Results Among 1039 participants (64.3% male; median age 22 years [IQR 20–24]), 57.2% (595/1039) reported familiarity with computational language models in medical education, and 70.1% (728/1039; 95% CI: 67.2–72.9) supported curricular integration. A strong majority (86.4%; 898/1039; 95% CI: 84.2–88.4) anticipated impact on the future of medical education. While 73.4% (763/1039; 95% CI: 70.6–76.0) perceived benefit for basic science education, only 41.6% (432/1039; 95% CI: 38.6–44.6) recognized utility in clinical skills training. Only 29.8% (310/1039; 95% CI: 27.0–32.7) considered these tools superior to human instruction. Key concerns included distrust in output reliability (52.6%; 547/1039; 95% CI: 49.5–55.7) and awareness of reference fabrication (64.0%; 665/1039; 95% CI: 61.0–66.9). Conclusion Saudi medical students express strong interest in GenAI—particularly for basic sciences and simulation—but perceive it as complementary rather than superior to human instruction. Findings reflect learner perceptions, not measured educational effectiveness. Implementation should prioritize reliability, ethical use, and preservation of humanistic competencies.
BACKGROUND
Artificial intelligence (AI) has emerged as one of the most rapidly evolving technologies in recent years and is increasingly being integrated into healthcare, education, and everyday life. Examining university students' attitudes toward this technology is important for understanding their future professional orientations and adaptation to technological change. This study aimed to identify medical students' attitudes toward artificial intelligence and the determinants of these attitudes, and to develop educational implications for medical training based on the findings.
METHODOLOGY
This descriptive cross-sectional study was conducted between January 22 and May 28, 2025, among 198 final-year medical students at Pamukkale University Faculty of Medicine in Denizli, Türkiye. Data were collected using a Descriptive Information Form, which assessed students' sociodemographic characteristics and their knowledge and experiences regarding AI, and the General Attitudes toward Artificial Intelligence Scale (GAAIS, Turkish version). Since the negative subscale is reverse-coded, higher scores in both indicate more positive attitudes toward AI. Data were analyzed using SPSS v25 with descriptive statistics, Mann-Whitney U, Kruskal-Wallis, and multiple linear regression analyses.
RESULTS
The mean age of participants was 24.47 ± 0.98 years; 56.1% were female. A total of 74.2% of the participants reported general knowledge about AI, and 76.3% reported experience using AI in daily life. A total of 63.1% viewed AI developments positively, 69.7% believed AI changes work and daily life, and 52.0% felt emotionally unaffected by it. The mean positive attitude score was 45.01 ± 9.17, and the negative attitude score was 26.41 ± 6.22. Multiple linear regression analysis showed that positive attitudes toward artificial intelligence were significantly associated with father's education level (university vs. primary school), having an interest in technology, perceptions regarding developments in artificial intelligence, and the belief that AI has an emotional impact (p < 0.05). For the negative attitude subscale, only perceptions regarding developments in artificial intelligence were found to be significantly associated (p < 0.05).
CONCLUSION
Medical students demonstrated generally positive attitudes toward AI. Higher paternal education, technological interest, perceiving AI as emotionally influential, and evaluating AI developments positively were predictors of favorable attitudes. In addition, more positive evaluations of AI-related developments were also associated with higher scores on the reverse-coded negative attitude subscale, indicating lower negative attitudes toward AI. These findings suggest that students' attitudes toward AI are shaped not only by technological interest but also by perceptual factors related to AI. Therefore, integrating clinically oriented AI content and awareness-building activities into medical education may support the development of more balanced and informed attitudes toward AI.
Batuhan Horasan, A. Ergin, Eda Şenarabacı· BMC Medical Education· 0 citations
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare and health-professions education. While it may improve efficiency, decision support, and learning, it also raises concerns about workforce impacts, safety, privacy, and accountability. Gulf-region evidence on how students and faculty perceive AI across disciplines within a single health science center remains limited.ObjectiveTo assess attitudes toward AI among health-sciences students and faculty and examine demographic factors associated with these attitudes.MethodsA descriptive cross-sectional online survey using convenience sampling was distributed to Health Science Centre (HSC) students and faculty (605 students, 43 faculty). It included demographic variables and two standardized measures, the General Attitudes towards Artificial Intelligence Scale (GAAIS) and the Artificial Intelligence Attitude Scale-4 (AIAS-4). Internal consistency was assessed using Cronbach's alpha; groups were compared using Mann-Whitney U and Kruskal-Wallis tests.ResultsAttitudes were broadly positive in both groups. Faculty scored higher than students on GAAIS (62.0 vs 55.5; p < 0.001) but not AIAS-4 (68.7 vs 63.1; p = 0.094). Among students, males scored higher on GAAIS (61.2 vs 54.9; p < 0.001) and AIAS-4 (71.4 vs 62.2; p = 0.004). AIAS-4 also differed by nationality (Kuwaiti 64.3 vs non-Kuwaiti 56.6; p = 0.005) and major (p < 0.001), with lower-GPA students less positive. In multivariable regression, male gender, Kuwaiti nationality, higher GPA, and discipline were independently associated with AIAS-4; physical-therapy students scored significantly lower than several disciplines.ConclusionsStudents and faculty demonstrate broadly positive attitudes toward AI alongside concerns about safety, surveillance, and workforce displacement. Structured education, practical exposure, and ethics- and governance-oriented teaching may support responsible AI integration in health-professions education and practice.
Hamad Alhamad, Ahmad Alenezi, Musaed Z Alnaser et al.· Work· 0 citations
Nursing students had a wavier level of artificial intelligence literacy of knowledge, attitude and practice and its application in nursing learning, which foster effective integration of artificial intelligence in nursing education.
Haider Mohammed Majeed, Ali Hussein Alek Al-Ganmi, Ali Dhahir Abdulyemmah et al.· F1000Research· 0 citations
BACKGROUND
Artificial intelligence is increasingly used in health care and medical education. This study aimed to identify factors associated with medical students' attitudes towards artificial intelligence, with particular attention to digital literacy, emotional intelligence and artificial intelligence-related perceptions.
METHODS
This cross-sectional study was conducted between November 2025 and January 2026 among 358 medical students. Data were collected using an online questionnaire including sociodemographic items, the Trait Emotional Intelligence Scale-Short Form, the Digital Literacy Scale and the General Attitude Towards Artificial Intelligence Scale. Group comparisons, correlation analyses, hierarchical linear regression and exploratory indirect-effect analysis were performed.
RESULTS
Most students had previously used artificial intelligence (88.5%), while 59.5% reported ethical or legal concerns and 41.1% believed that artificial intelligence could reduce clinical reasoning. Digital literacy was positively correlated with attitudes towards artificial intelligence (r = 0.319, p < 0.001). In the final hierarchical regression model, digital literacy was the strongest independent factor associated with attitudes towards artificial intelligence (B = 0.242, β = 0.320, p< 0.001). Clinical educational stage, ethical or legal concerns, and perceptions regarding the impact of artificial intelligence on clinical reasoning were also independently associated with attitude scores. Previous artificial intelligence use and emotional intelligence were not independently associated with attitudes after adjustment.
CONCLUSIONS
Medical students' attitudes towards artificial intelligence were associated more strongly with digital literacy and artificial intelligence-related perceptions than with previous use alone. Undergraduate medical education should integrate digital literacy, ethical awareness and reflective discussion on clinical reasoning into artificial intelligence-related teaching.
Nur Demirbaş, Hatice Kucukceran, O. Arık· Postgraduate medical journal· 0 citations
Undergraduate medical students show moderate awareness of AI in healthcare but lack formal training and in-depth understanding, highlighting the need for structured AI education within medical curricula and further research on its long-term impact.
Sonali Sharma, Smriti Kayat, N. Saboo et al.· Nigerian Medical Journal· 0 citations
Introduction: Rapid advancements in artificial intelligence (AI) have brought significant transformations to healthcare systems and medical education. Medical sciences students' awareness and attitudes toward AI play a crucial role in its acceptance and effective use. This study aimed to assess the level of AI awareness and attitudes among students at Shahid Sadoughi University of Medical Sciences, Yazd, Iran, and examine related demographic factors.
Method: This descriptive cross-sectional study was conducted in 2024 with 500 undergraduate and general medicine (MD) students from multiple faculties. Data were collected using a researcher-developed and standardized questionnaire covering demographics, AI-related awareness, attitudes, and concerns. Content validity was confirmed by 10 experts (CVI = 0.89), and reliability was verified (Cronbach's alpha = 0.83). Data analysis was performed with chi-square test, independent samples t-test, and ANOVA.
Results: Among participants, 62% were female and 90% were under 25 years old. The highest awareness and most positive attitudes were found among medical and midwifery students, while emergency medical services and operating room students showed the lowest levels (P < 0.05). Mean awareness and attitude scores were significantly higher in females than males (P < 0.05), with no significant differences across age groups. Concerns about job displacement due to AI were also prominent.
Conclusion: Although students showed positive attitudes toward AI, their conceptual and practical awareness was moderate. Integrating structured educational programs focusing on practical and ethical aspects of AI into the medical curriculum is essential.
Rastegar Ghavi Z, Najaryan N, Cheraghi Nik A et al.· Journal of Medical Education...· 0 citations