Health science reports · 2026
Saudi Medical Students' Perceptions and Attitudes of Integrating Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Study.
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Aljamaan Fadi, Mubarak Muhammad Faisal, Altamimi Ibraheem, Alanteet Alaa A, Alsalman Mohammed A, Dasuqi Shereen A, Alballaa Rashid, Alarifi Mohammed I, Saadon Abdalrhman Al, Alhaqbani Abdulrahman O, Alhadlaq Abdulrahman A, Alokayli Shirin H, Alrasheed Bader N, Alkhalife Sarah I, Sattar Kamran, Jamal Amr, Soliman Mona, Saad Khaled, Temsah Mohamad-Hani
Аннотация
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.
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.