The attitude, perceived usefulness, perceived ease of use and acceptance of artificial intelligence among medical students in Iran: An application of the technology acceptance model.
Abstract
Introduction: This study evaluated medical students’ attitude, perceived usefulness (PU), perceived ease of use (PEOU), and intention to accept artificial intelligence (AI) technology in Iran in 2024 using the Technology Acceptance Model (TAM). Methodology: In this cross-sectional study, 246 medical students were selected by stratified sampling. Data were collected with a TAM-based questionnaire on AI and analyzed using SPSS 24. Pearson correlation, linear regression, and descriptive statistics were used to assess relationships and predictors. Results: Attitude toward use (β = 0.41, P < 0.001), PEOU (β = 0.50, P < 0.001), PU (β = 0.43, P < 0.001), and intention to use (β = 0.58, P < 0.001) were significantly associated with actual AI use. In a multivariable regression, PU, PEOU, and attitude together explained 78% of the variance in actual AI use (R² = 0.78, Adjusted R² = 0.76, F(4, 241) = 60.75, p < 0.001). Conclusion: PU, PEOU, and positive attitude are strong predictors of AI acceptance and actual use among medical students. Educational institutions should address these factors to facilitate effective integration of AI into medical education.
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References
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